The PolicyViz Podcast

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On this week’s episode, I talk with Jessica Calarco about her book “Holding It Together: How Women Became America’s Safety Net” and the role of qualitative data in research and data visualization. Calarco, a sociologist from UW Madison, discusses her research on family life inequalities and the shift to a “DIY society,” where individuals, particularly women, manage risks without government support, leading to reliance on low-wage caregiving. She critiques the wealthy elite for discouraging collective social support and emphasizes the need for policy changes to ensure basic needs, caregiving opportunities, and work-life balance through measures like paid family leave. We also talk about Jessica’s data collection efforts, which involved more than 400 hours of interviews, surveys, and national studies, to understand human experiences deeply. She addresses critiques of qualitative research’s representativeness, arguing its strength lies in capturing life’s complexities.

Keywords: data, data visualization, flourish, jessica calarco, holding it together, safety net, DIY, data analysis, qualitative data, qualitative data analysis, qualitative data analysis - coding and developing themes, machine learning, nvivo, dedoose, ai, data scientist, qualitative data analysis thematic analysis, qualitative data collection, university of wisconsin, data analyst, data science, machine learning for beginners

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Welcome to Season 11 of the PolicyViz Podcast! After a relaxing summer full of travel and reading and fun, I’m ready to kick off a whole new season of the show. To kick off this season, I’m excited to be joined by Federica Fragapane, an independent designer known for her intricate and beautiful data visualizations. Our conversation delves into her creative process, the tools she uses, and where she finds inspiration. Fragapane, with a Master’s degree in visualization design and experience at Accurat Studio, integrates data into her bespoke visualizations that convey deeper narratives, particularly human experiences and environmental issues. She prefers organic shapes to reflect the living presence behind data, emphasizing beauty, context, audience, and accessibility.

Keywords: data, data visualization, flourish, graphic design, how to, information design, graphic design tutorials, graphic design portfolio, graphic design course, online learning, graphic design photoshop, graphic design trends 2024, how to draw, data scientist, Federica fragapane, accurat, AccessibilityInDesign, EngagingVisuals, Inspiration, DataNarratives, VisualizationDesign, InstagramPortfolio, BehancePortfolio, mathematics, Al, machine learning

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Welcome to the Season 10 finale of the PolicyViz Podcast. I can’t believe I’ve been doing this podcast for 10 years! I’m truly grateful to all my guests and all my listeners who have been tuning in and, hopefully, have learned a lot about data, data visualization, presentation skills, and more. As I sign off for the summer, I hope you are able to take a break and get a bit of rest.

In this final episode of the season, I welcome Nancy Organ to the show to discuss her new book Data Visualization for People of All Ages. Nancy’s book aims to make dataviz accessible to everyday readers. Our conversation highlights the importance of not altering data simply for aesthetics but to facilitate understanding. We also explore balancing creativity with informed design choices, and suggest alternatives to traditional graphs, such as infographics, timelines, flowcharts, and diagrams.

Our discussion extends to the challenges of creating visually appealing infographics and the significance of design in effective communication. Because Nancy’s book could work well to help kids better understand data and data visualization, we talk about how she might better integrate data visualization into educational curriculums. We also talk about non-traditional learning environments like science camps and homeschooling, and we also discuss inclusivity and diverse thinking approaches.

➡️ Check out more links, notes, transcript, and more at the PolicyViz website.

Topics Discussed Importance of Data Visualization. Nancy underscores the necessity of data visualization skills in the modern world and how these skills can be nurtured from a young age. Nancy’s book aims to demystify complex data concepts, starting with basic data units and building up to more sophisticated visual representations. * Educational Approach. Her book includes self-assessment tools and classroom exercises to facilitate learning with an emphasis on making the content relatable and straightforward for a broad audience. * Ethics in Data Visualization. There is a strong focus in Nancy’s book and in our conversation on maintaining data integrity and making ethical choices in visual storytelling. * Techniques and Tools. We also discuss various data visualization formats such as timelines and flowcharts and how to understand different data encodings to enhance perceptibility and engagement. * Incorporating Visualization into Education. Finally, Nancy and I talk about* integrating data visualization and data science into the K-12 curriculum and how to promote visual thinking across multiple disciplines and learning environments.

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Georgia Lupi joins the show to discuss her work in data visualization, her journey from Accurat to Pentagram, and how she takes a human-centric perspective to working with and communicating data. Our conversation also focuses on her new book, “This is Me and Only Me.” The book encourages kids to observe and collect data to understand emotions and human questions, using symbols and colors to express emotions. Giorgia hopes the book will inspire kids and adults to be imaginative, observant, and mindful. We also have some breaking news on this episode because Giorgia is working on another big project, a new book called “Speak Data” that explores data as a language intersecting various fields.

➡️ Check out more links, notes, transcript, and more at the PolicyViz website.

Topics Discussed

Human-Centric Data Visualization. Georgia emphasizes the importance of incorporating human elements and context into data visualization to make data more relatable and engaging. She discusses how Pentagram utilizes these principles in various branding projects to create compelling stories with data.
Impact of Chronic Illness through Data. Georgia shares her personal experience with long COVID, illustrating how data can be used to convey the profound impact of chronic illness on individuals’ lives.
Children’s Book Project - “This is Me and Only Me”. Inspired by her Dear Dataexperiment with Stefanie Posavec, Georgia’s new book encourages children to observe, collect, and use data to understand their emotions and human questions. Through the use of symbols and colors, the book aims to make data visualization accessible and fun for kids and adults.
Upcoming Book - “Speak Data”. And yes, Giorgia is working on a new book project: Speak Data will delve into the concept of data as a language that intersects with various fields, featuring interviews and insights from diverse disciplines.

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Nicole Lachenmeier and Darjan Hil’s new book,Visualizing Complexity: Modular Information Design Handbook, focuses on deconstructing data encodings into fundamental elements to create effective visualizations. They take an exciting and hands-on approach to data visualization design for their own work and how they teach design to others. In this week’s episode of the podcast, our conversation highlights Nicole and Darjan’s journey in data design and how they stress the importance of deliberate thinking, manual effort, and critical analysis in their design process.

While our conversation often focuses on the details of the book, we also take a larger perspective to data visualization and discuss the need to simplify data for better comprehension through hands-on visualization workshops and the significance of selecting appropriate charts. We explore the complexity of chart selection and focus on breaking down elements of charts and graphs for better design. Nicole and Darjan talk about their collaborative process of writing a book that effectively integrates visuals and text along with the challenges they encountered and the positive feedback they’ve received.

➡️ Check out more links, notes, transcript, and more at the PolicyViz website.Topics Discussed Authors’ Journey in Data Design. Nicole and Darjan share their extensive experiences in the field and their emphasis on deliberate thinking, manual effort, and critical analysis as pillars of the design process. * Understanding Basic Visualizations. We discuss the importance of mastering fundamental visualization techniques and using manual sketching as a vital tool for improving design skills and fostering creativity. * Selecting Appropriate Charts. We talk about the complexities inherent in choosing the right chart for your data and how their approach can help you break down chart elements into elementary pieces * Book Creation and Integration. The authors share their collaborative* process of merging visuals and text in their new book

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Nathan Yau’s Flowing Data website was one of the first data visualization websites I discovered in my own data journey. With his new book, Visualize This, now out, I thought it would be a great opportunity to talk with Nathan about his work, his book, and how his own approach to data has evolved over the last several years.

The new edition of Visualize This enriches readers with modern techniques and examples, focusing on effectively learning data visualization by exploring different data types and designing for clear communication, even for those without a formal design background. Nathan emphasizes the necessity of audience-appropriate visualizations and the selection of suitable tools, all of which have changed and evolved since the first edition of the book was published in 2011.

We obviously talk about the latest book in this episode of the podcast, including Nathan’s process for creating graphics (a lot of R and Adobe Illustrator), his professional growth from a statistics PhD program to embracing full-time visualization work. We discuss the nuances of handling feedback, the differentiation between misinformation and subjective interpretation, and the significance of constructive criticism. We also touch on challenges for newcomers in the field, the need for clearer communication of uncertainty, and the potential of virtual and augmented reality.

➡️ Check out more links, notes, transcript, and more at the PolicyViz website.

Topics Discussed

Updated Techniques and Modern Examples: Nathan’s new edition of “Visualize This” brings to the forefront the latest in data visualization, incorporating modern techniques and examples that cater to both beginners and seasoned practitioners.
Learning by Exploring: The book emphasizes a hands-on approach to understanding data visualization. It guides readers through exploring different data types and designing visualizations that communicate clearly, irrespective of the reader’s design background.
Personal Data Collection and Analysis: Nathan shares his insights into the importance of personal data collection for self-analysis, a practice influenced by his time at the New York Times. This self-exploratory journey into data helps individuals understand the nuances of their own information.
The Growth of Flowing Data: Nathan reflects on the evolution of his platform, Flowing Data, highlighting its expansion to include daily emails, tutorials, and personal projects.

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Welcome to a solo episode! In this special episode of the podcast, I talk about the recen Tableau Customer conference in San Diego, which drew over 9,000 participants. I reflect on various aspects of the conference, including its diverse sessions that ranged from showcasing new Tableau features and case studies to hands-on workshops and discussions on data visualization beyond Tableau. I was a presenter at TC and presented my work on the Urban Institute’s Do No Harm Project.

My review of the conference focuses just on the positives of the conference—the upcoming features in Tableau, such as allowing Tableau Public users to save work their locally, as well as some thoughts on why I—who is not a huge Tableau user—actually attends the conference. I also discuss a few of the sessions I attended, including hands-on sessions and games in Tableau.

Topics Discussed Conference Overview: A recap of the Tableau Conference, highlighting its scale with over 9,000 participants and the variety of sessions that catered to both seasoned data analysts and newcomers to the field. * Tableau’s New Features. Significant updates to Tableau, including the ability for Tableau Public users to save work locally – a feature that potentially redefines the need for Tableau Desktop licenses. I also talk about the introduction of customizable themes, Google Fonts integration, VIS Extensions, and the leaps in AI and real-time data integration within Tableau. * Community and Networking:* I reflect on the value of the community that Tableau fosters, especially through networking opportunities that are more crucial than ever in the post-pandemic era.

➡️ Check out more links, notes, transcript, and more at the PolicyViz website.

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You know Enrico Bertini, right? Writer, teacher, co-host of the Data Stories podcast, Enrico does it all. Now at Northeastern University, I invited Enrico to the show to talk about his research, great Substack newsletter, and for views on the evolving landscape of data visualization on social media. In our discussion, Enrico emphasized the significance of interdisciplinary collaboration at Northeastern University. He has some concerns about the current state of visualization theory and tools and talks about his ideas around “critical data thinking” as a crucial way of thinking about data visualization, highlighting the challenges of data accuracy and interpretation. We also talk about Enrico’s teaching methods to help students improve their data interpretation and data visualization skills. Enrico and I share some of the same feelings about the shifts in social media use in the dataviz community, and how it has led to a loss in diverse intellectual exchanges, underscoring the importance of finding new ways to foster community engagement and creativity, including through writing platforms like Substack and LinkedIn.

Topics Discussed The Current State of Visualization Theories and Tools: Enrico critiques the prevalent theories and tools in data visualization, calling for a more systematic and thoughtful approach to both creating and interpreting visual data. * Challenges of Presenting Accurate Data: Our conversation delves into the difficulties faced in presenting precise and accurate data, especially highlighted during the COVID-19 pandemic, and how these challenges have impacted the field. * Impact of Social Media Platform Shifts: A significant focus of our conversation is on the changing landscape of social media platforms, particularly the decline of Twitter as a crucial space for professional exchanges within the data visualization community. * Reflections Prompted by the Pandemic: Enrico reflects on the pandemic’s role in helping him reevaluate his work and teaching practices, which is a helpful insight into how his creativity has changed and adapted over the last few years. * The Role of Newsletters in Idea Refinement and Audience Connection: Enrico shares insights into how newsletters have become a pivotal tool for refining ideas and connecting with a diverse audience, including students from various disciplines, fostering a richer, more engaged community. * Interdisciplinary Collaboration for Innovation:* Highlighting the value of interdisciplinary collaboration, especially at Northeastern, this week’s episode showcases how interactions between computer science engineering students and design peers, as well as varied problem-solving approaches from faculty members, can lead to fresh insights and propel the field forward.

➡️ Check out more links, notes, transcript, and more at the PolicyViz website.

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On this week’s episode of the podcast, I speak to author and teacher Nick Desbarats about his new book, Practical Charts: The Essential Guide to Creating Clear, Compelling Charts for Reports and Presentations. Our conversation covers choosing appropriate chart types, emphasizing simplicity and clarity, and understanding when to use different formats. Nick aims to challenge dogmatic views on charts, such as the use of pie charts, and stresses the importance of catering to the audience’s familiarity with graph types. Our chat also includes insights on transitioning to online teaching during the pandemic, the distinction between clear graphs and dashboards, and the significance of mastering fundamentals in data visualization for beginners and intermediates. If you’re familiar with Stephen Few’s work, you’re also bound to find some behind-the-scenes gems in this week’s episode.

Topics Discussed

Choosing the Right Chart. Nick kicks off our conversation with an essential primer on selecting the appropriate chart types for different datasets. His focus is on simplicity and clarity, ensuring that the chosen chart communicates the intended message as effectively as possible.
Challenging Chart Dogmas. Prepare to have your preconceptions challenged as Nick takes on the controversial stance on pie charts and other commonly debated graph types. It’s all about breaking the mold and understanding why certain charts work better for specific audiences.
Catering to Audience Familiarity. A significant portion of our chat is dedicated to the importance of tailoring chart choices to the audience’s level of comfort and familiarity with different types of graphs. This segment is crucial for anyone looking to maximize the impact of their data presentations.

➡️ Check out more links, notes, transcript, and more at the PolicyViz website.

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In this week’s episode of the PolicyViz Podcast, I interview Rahul Bhargava from Northeastern University on the topic of data physicalization. We discuss the role of community engagement and societal impact in communicating data and including different people and communities. Our conversation touches upon teaching combined majors at Northeastern and expanding data engagement through Rahul’s participatory art methods. We explore the limitations of visual learning and advocate for including diverse voices via data sculptures and embodied experiences.

Topics Discussed Inclusivity in Data-Driven Society. The episode opens with a discussion on the necessity of inclusivity in our increasingly data-centric world. Rahul shares his insights into how data physicalization can bridge the gap between complex data and diverse community members. * Teaching Combined Majors at Northeastern. Rahul gives us a glimpse into Northeastern’s approach to education, emphasizing the value of combined majors that integrate data science with other disciplines. * Participatory Art Methods in Data Engagement. Rahul describes his use of art tomake data more accessible and engaging. We talk about the potential of data sculptures and embodied experiences to include those who may not be reached through traditional visual data presentations. * Culturally Sensitive Approaches to Data. We discuss the importance ofunderstanding and respecting cultural differences, particularly when working with youth from lower socioeconomic backgrounds. * Community Empowerment through Data. Rahul shares his strategies for adapting data collection and dissemination to empower communities, and his use of everyday materials like craft items to make data physicalization more inclusive. * Data Literacy and Design Principles. Finally, we discuss* on how to build data literacy by employing engaging and thoughtful design principles.

➡️ Check out more links, notes, transcript, and more at the PolicyViz website.

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Amanda Makulec is the current Executive Director of the Data Visualization Society (DVS), and in this week’s episode of the PolicyViz Podcast, we discuss her journey and the DVS’s evolution as it approaches its fifth anniversary. Amanda shares her experience starting as a volunteer all the way to leading the entire organization. With her second term coming to an end, she emphasizes the importance of term limits and her commitment to ensuring the organization’s sustainability by focusing on operational systems, finances, compliance, and community responsiveness.Topics Discussed Leadership and Evolution of DVS. Amanda discusses her path from volunteering to leading DVS and reflects on the importance of term limits and her dedication to the sustainability of the society. * Community Building and Knowledge Sharing. Amanda discusses DVS’s role as a hub for individuals from different tech backgrounds to share insights and best practices and how DVS seeks to create more meaningful community spaces. * Data Literacy and Supportive Initiatives. We discuss DVS’s commitment to data literacy and providing a nurturing environment through initiatives like the Outlier conference and the Nightingale magazine. * Navigating Social Media and Communication Platforms. As I’ve talked about with other recent guests, we talk about decreased engagement on Twitter/X and limited real-time interaction on LinkedIn. We discuss the balance between online space fragmentation, privacy, and psychological safety, as well as DVS’s use of Slack and potential migration to other platforms such as Discord. * Financial Challenges and Operational Sustainability*. We talk about the financial constraints of DVS, including the high costs of Slack, and the importance of allocating the budget wisely to support key community and operations management roles.

➡️ Check out more links, notes, transcript, and more at the PolicyViz website.

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In this week’s episode, I chat with Dietmar Offenhuber about his new book, Autographic Design and the concept of autographic data analysis. Dietmar stresses the significance of recognizing the material origins of data and the influence of extraneous variables. He advocates for a qualitative approach that pays attention to data traces, which can uncover deeper narratives. In our conversation, we explore what is meant by autographic design and urge a wider lens on data to grasp multifaceted problems thoroughly. Additionally, Dietmar’s work underscores the interplay between qualitative and quantitative methods, emphasizing the role of subtlety and conjecture in data interpretation to bring a more nuanced understanding of the stories behind the numbers.

Topics Discussed The Material Context of Data Collection. We dive into why understanding where and how data is collected is paramount for accurate analysis. We talk through a number of examples in Dietmar’s work and book. * The Impact of the Third Variable. We explore how the introduction of a third variable can dramatically shift the interpretation of data and data visualizations. We discuss the importance of being vigilant for these variables to avoid erroneous assumptions. * Unintentional Digital Traces. Our conversation uncovers the value of unintentional digital traces that we leave behind and how they can be a gold mine for analysts. * Qualitative Meets Quantitative. We discuss the need for blending qualitative insights with quantitative research and how they can complement each other to provide a fuller picture of analysis. * The Speculative Nature of Data Analysis. We address the inherently speculative aspect of data analysis, highlighting the fact that, despite the numbers, much of what analysts do involves informed guesswork. * A Call for Collaboration*. The discussion opens the floor for collaborative efforts, emphasizing that the best results often come from pooling knowledge and expertise across different fields.

➡️ Check out more links, notes, transcript, and more at the PolicyViz website.

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In this week’s episode of the podcast, I welcome author, speaker, and professor Alberto Cairo to the show. We discuss his most recent book, The Art of Insight, and our conversation extends to acquiring reliable data and challenges people across the world face in creating useful and accessible data visualizations. We also discuss the current state of social media as it relates to the data visualization community and Alberto contemplates the future landscape for both the community and data-related conferences in a post-pandemic world.

Topics Discussed The Quest for Reliable Data: Alberto and I discuss the critical importance of acquiring accurate and reliable data. We talk about the the complexities involved when dealing with cross-country datasets and how cultural and systemic differences can impact data collection and representation. * The Shifting Platforms of DataViz Communities: Once a thriving hub for sharing insights and innovations, Twitter’s popularity in the dataviz community is on the decline. We explore the implications of this shift and what platforms are emerging as new gathering spots for professionals and enthusiasts alike. * The Post-Pandemic Outlook: With the world slowly adjusting to the new normal, we consider the future of data-related conferences and community gatherings. How will these events evolve, and what can we expect from virtual and in-person interactions in the coming years? * Spotlight on Alberto: No conversation about data visualization would be complete without mentioning Alberto’s influential work in the field. We talk about his contributions, including his most recent book, The Art of Insight*, and how his teachings have shaped the way we approach data storytelling.

➡️ Check out more links, notes, transcript, and more at the PolicyViz website.

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In this week’s episode, we delve into the pivotal role of visual clarity in scientific research. Join me and Professor of Clinical Epidemiology Maarten Boers as we discuss his new book, Data Visualization for Biomedical Scientists. If you are at all interested in being a better science communicator—and especially if you are interested in publishing your work in academic journals—this episode is for you! We talk about how Maarten’s book extends beyond the world of biomedical science into good table design, small multiples, and how academic publishing needs to get its act in order.

Topics Discussed The Necessity of Clear Experimental Procedures: We highlight the significance of understanding every step within an experiment. Our discussion unpacks the ways in which clear, precise procedures facilitate reproducibility and validation of scientific work. * Deciphering Scientific Terminology: Maarten’s book emphasizes the importance of demystifying complex scientific jargon. We examine strategies for breaking down terminology barriers for both specialist and general audiences. * Graphical Excellence in Research Communication: We focus on the power of well-titled, labeled, and annotated graphs in conveying research and analysis. * Impactful Captions and Visual Storytelling: Captions are more than mere descriptions—they’re a gateway to engaging the reader. We explore how to craft active captions that not only inform but also captivate and retain the reader’s attention. * Challenges in Academic Publishing: We confront the practical challenges researchers often encounter with journals, their design (or lack thereof), and other publishing pitfalls. We talk about how to effectively intervene when production staff mishandle figures and how to work within the constraints of journal page limits. * Ensuring Accuracy in the Publication Process:* Our conversation also touches on the responsibilities of researchers to ensure their findings are presented accurately and effectively, even in the final stages of publication.

➡️ Check out more links, notes, transcript, and more at the PolicyViz website.

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On this week’s episode of the podcast, we dive into the transformative work of Neighborhood Nexus, led by Tommy Pierce. This civic data intermediary is making waves in Georgia by harnessing the power of data to create a lasting social impact. Here are the highlights of our discussion on how Neighborhood Nexus is revolutionizing the nonprofit sector through data-driven strategies:

Topics Discussed The Mission of Neighborhood Nexus: We explore the core objectives of the organization, which include addressing inquiries, informing programs, and fostering crucial relationships between data providers and users. The organization’s goal is to enhance the nonprofit sector’s ability to utilize data for effective problem-solving and informed decision-making. * Data Utilization and Technical Assistance: Discover how Neighborhood Nexus offers support to ensure that data is not just collected but used effectively. This includes advocating for data-informed leadership through comprehensive training and building a community around data expertise. * Quantitative vs. Qualitative Data: We highlight the organization’s focus on not just numerical data but also qualitative insights. This includes gathering real-time information and community input, recognizing the need to capture the full spectrum of lived experiences. * Bridging the Gap: Learn about the pivotal role Neighborhood Nexus plays in connecting local and regional governments with nonprofits. The organization helps with strategic planning and addresses the resource challenges exacerbated by the pandemic, emphasizing data and equity. * Understanding Local Nonprofit Sectors: The episode delves into the challenges of missing data and the importance of marketing insights to serve communities more effectively. We discuss the organization’s collaborative efforts with local journalism for deeper data collection and understanding of community needs. * Navigating the Political Landscape: The discussion touches upon the political context in Georgia, particularly the urban-rural divide that transcends traditional partisan lines, and how this affects data-driven approaches. * Capacity Building and Community Engagement:* We emphasize the key themes of capacity and community building and the ambition to make data more actionable. This includes website improvements and the development of new tools to engage stakeholders.

➡️ Check out more links, notes, transcript, and more at the PolicyViz website.

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On this week’s episode of the show, I talk with Nate Braun, author of several Python books, all having to do with sports. Nate shares his journey from having a background in economics to writing books on sports data analysis and visualization using Python. Despite not initially being skilled in coding, Braun was inspired by his work in environmental issues and modeling, leading him to develop fantasy football models and later educational books on coding and data analysis with a focus on various sports. We cover Nate’s data scraping and writing process, as well as the ins and outs of why he likes to work with Python and the various libraries he uses in his work.

Topics Discussed Background and Transition: Nate shares his unconventional journey from working on environmental issues to developing a niche in sports data analytics. His inspiration took root during his work on modeling the impact of the BP oil spill. * Fantasy Football and Education: The pivot to sports began with fantasy football models. The success of these models led Nate to author books designed to educate enthusiasts on coding and data analysis, specifically tailored for those outside the computer science field. * Challenges and Opportunities: Nate talks about the difficulties he faced entering the competitive fantasy football advice market. With the rise in betting and fantasy sports advertising, he recognizes the potential for educating people on data analysis. * Sport-by-Sport Learning Curve: Despite not being an expert in all sports, Braun has written instructional books on a range of sports by dedicating time to write and develop new models, leveraging the success and experience gained from his initial football book. * Data Gathering and Visualization: Our conversation delves into the varying difficulty levels of acquiring and visualizing data across sports and we highlight Nate’s use of the Python Seaborn library. * Python Over R: Nate expresses his preference for Python due to its versatility in machine learning, data visualization, web scraping, and content creation, favoring it over R. * Technical Deep-Dive into Web Scraping: We talk about using Python for web scraping, including dealing with JavaScript-heavy websites, and the other tools Nate uses like Beautiful Soup and Selenium. * Future Plans:* A teaser for a potential Python book on Formula One as Braun’s love for sports continues to drive his writing endeavors.

➡️ Check out more links, notes, transcript, and more at the PolicyViz website.

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Happy New Year and welcome back to the PolicyViz Podcast! In this first episode of 2024, I welcome welcome Sheila Pontis and Michael Babwahsingh, authors of the new book Information Design Unbound. They delve into the heart of information design, sharing their experiences in creating a pivotal resource for both students and professionals. This episode is a must-listen for anyone interested in the ever-evolving world of design thinking and information conveyance.

Topics Discussed

Origins of “Information Design Unbound”: Sheila and Michael discuss their drive to pen a comprehensive guide in the field of information design, recognizing the gap in educational resources for this burgeoning discipline.
Collaborative Challenges: The duo sheds light on the difficulties they faced while integrating varied viewpoints and adhering to publishing constraints and deadlines.
Educational Resource: With over 400 images and contributions from more than 65 experts, the book is a treasure trove of knowledge, featuring exercises and activities aimed at both new learners and seasoned practitioners.
Design Education for Non-Designers: The authors emphasize the importance of teaching design and information design to students without a formal background in design, tailoring approaches based on the students’ areas of study.
Professional Team Dynamics: An exploration into the various team structures within the field of information design and how they collaborate to address complex issues.
Evolution of Design Thinking: A shift from creating traditional design artifacts to solving complex systems and wicked problems is discussed, marking the advancement in design methodologies.
Cultural Sensitivity in Design: The conversation highlights the crucial role of context, audience, and cultural differences when employing icons and other design elements, acknowledging that design solutions are not universally applicable.

➡️ Check out more links, notes, transcript, and more at the PolicyViz website.

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It's the final episode of 2023! I hope you have a great holiday season and a happy new year!

Gulrez is a father of three beautiful kids and works as a Data Science Leader in his day job. He has almost two decades of experience under his belt and has a knack for turning boring numbers into captivating stories. When he's not busy working, you can find him passing on his skills to the next generation in the hopes of creating a world of data literate children. A strong believer in the power of data literacy, Gulrez is on a mission to improve the way people make sense of data. He's known for delivering corporate workshops that are equal parts informative and entertaining.

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William Gray is the guy behind Floor Charts, the website and Twitter feed that documents all things graphic in the US Congress. During the day, Bill oversees the strategic communications efforts at R Street and manages its growing Communications team, including overseeing the public relations, digital and events units. He joined the organization in 2020. Previously, William was communications director at Issue One, the leading cross partisan political reform group in Washington, where he helped launch and executive produce the first conservative political reform podcast, Swamp Stories. Prior to Issue One, he managed press and negotiated news partnerships as the media relations specialist for the Center for Public Integrity, one of the oldest nonprofit investigative newsrooms in the country; and was a producer at C-SPAN, delivering daily public affairs programming and coverage of Congress and the White House to viewers around the world.

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Andrea Pacini is the author of the best-selling book Confident Presenter, a presentation coach and Head of Ideas on Stage UK. He specializes in working with business owners, leaders and their teams who want to become more confident presenters. Since 2010 Ideas on Stage has worked with thousands of clients around the world, including organizations like Microsoft, Spotify, eBay, The World Bank and over 500 TEDx speakers. Andrea is on a mission to stop great ideas from failing just because of the way they are presented. His vision is to help hundreds of thousands of business leaders inspire their audiences, increase their influence, and make a positive impact in the world.

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Tomas Pueyo is the author of Uncharted Territories, a newsletter where he tries to deeply understand how the world works to understand where it's going and nudge it in the right direction. He became world viral with his COVID articles, notably The Hammer and the Dance. He has 75,000 readers, and 300,000 on Twitter. Before Uncharted Territories, he has worked in tech companies in Silicon Valley for 15 years.

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Running your own data literacy and data consulting shop is no easy task. And helping customers not only build better visualizations and dashboards, but helping them create a better (or new) data culture is even harder. But Serena Roberts and her team at Moxy Analytics has been fighting that good fight for a few years now. Serena and I talk about what Moxy is up to, how to build better data teams, getting over imposter syndrome, and much, much more.

Sponsor: Maryland Institute College of ArtMICA’s Master of Professional Studies degrees offer intensive, online education designed to develop both creative and professional skills. Now accepting applications for the spring, summer, and fall semesters.

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Creating data visualizations in the physical world is not a new phenomenon. Humans have been drawing on walls, tallying money and crops, and carving on stone tablets for thousands of years. Today, though the practice of data visualization is largely done in the digital world, there is an exciting area of working in the physical space--the real world, as it were--to create, share, and communicate data and information. That brings us to the exciting new book, Making with Data, that provides a snapshot of the diverse practices contemporary creators are using to produce objects, spaces, and experiences imbued with data. In this week's episode of the podcast, I chat with the editors of the book to get their take on this exciting field.

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Welcome back to a whole new season of the PolicyViz Podcast! I'm excited to bring you a whole new exciting slate of guests this year covering a huge array of data visualization and data communication strategies, technologies, and techniques.

Maureen Stone (Tableau Research) has been involved with Tableau since 2004, when she was asked to design the initial data colors for Tableau 1.5. She joined the company in late 2011 and became a founding member of the Research Team in 2012. As a member of Tableau Research, she continued her work on optimizing the use of color in visualization. She served as research director (2017-2021), and has recently retired (June, 2022). While best known for her expertise in digital color, she has a broad experience in information visualization, interactive graphics and user interface design. She is a member of the IEEE VGTC Visualization Academy and the author of A Field Guide to Digital Color.

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Nigel Holmes is a British/American graphic designer, author, and theorist, who focuses on information graphics and information design. Graduating from Royal College of Art in London in 1966, Holmes ran his own successful graphic design practice in England. From 1966 to 1977, he worked as a freelance illustrator and graphic designer for clients such as British Broadcasting Corporation, Ford Motor Company, and Island Records. His work appeared in New Scientist, Radio Times, The Observer, Daily Telegraph, and The Times. In 1977, art director Walter Bernard hired him to work in the map and chart department of Time magazine, where Holmes later became graphics director. After a sabbatical he started his own company, which has explained things to and for a wide variety of clients, including Apple, Fortune, Nike, The Smithsonian Institution, Sony, United Healthcare, US Airways, and Visa.

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Joe Sharpe has been founder and creative director at Applied Works since 2005, a design studio using data visualisation, user-centred design and storytelling to create digital tools and products that drive positive change. Joe also teaches on the BA Graphic Design degree at Kingston School of Art, running an elective pathway for second and third year students that explores how emerging technology is transforming the way we communicate, work, play and consume.

Mike Orwell is a digital executive producer, filmmaker and consultant. Between 2009 & 2018, he was a producer and commissioning editor at the BBC and since then has worked with award-winning digital production studios like Unit9, Marshmallow Laser Feast and Applied Works to explore new storytelling methods. At the BBC, he pioneered various mass-audience, data-driven storytelling & branching narrative projects , including the Great British Class Calculator and the BBC Lab UK platform. His boutique film-making collective Elastic Semantic specialises in telling research-driven science & engineering stories for clients such as Arup.

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Jonathon Reilly is an innovative and results-driven executive with over 20 years of experience in product management, business development, and operations. As the Co-Founder and COO of Akkio, he has helped create an easy-to-use AI platform that empowers users to build and deploy AI solutions to data problems in minutes.

Prior to founding Akkio, Jonathon served as the VP of Product & Marketing at Markforged, where he played a critical role in the company's growth and success. With a strong background in the tech industry, Jonathon held various leadership positions at Sonos, Inc., including Leader of the Music Player Product Management Team, Global Channel Development, and Senior Product Manager. He began his career at Sony Electronics, where he contributed significantly to the development of a wide range of consumer products as a product manager and electrical engineer.

Jonathon holds an MBA in Entrepreneurship/Entrepreneurial Studies from Babson College - Franklin W. Olin Graduate School of Business and a BSEE in Electrical Engineering from Gonzaga University.

See links, notes, transcript more at the PolicyViz website.Episode NotesJonathon | Medium | TwitterAkkio

How to Lie with Statistics by Darrell Huff and Irving Geis

Data at Urban: How We Used Machine Learning to Predict Neighborhood Change

autoML

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On this week's episode of the PolicyViz podcast, I chat with Susan Schulten and Georges Hattab, authors of the new books on dataviz luminaries Emma Willard and Etienne-Jules Marey. We talk about these two creators and their impacts on the data visualization field today.

Susan Schulten is Distinguished University Professor of History at the University of Denver, where she has taught since 1996. Georges Hattab is the Visualization Group Leader at the Center for Artificial Intelligence in Public Health Research at the Robert Koch Institute since 2022.

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Micah is a mathematician who likes to use pictures to understand things. He runs a website, hockeyviz.com, where he stores pictures about hockey. He lives in Halifax, Nova Scotia with his wife and his two children.

Episode NotesMicah | Twitter | Site

Bubble physics
Python
Beautiful Soup
svgwrite
Matplotlib
Line-width illusion

Related EpisodesEpisode #238: Jeremy Ney
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Jeremy is the author of American Inequality, a biweekly newsletter that uses data visualization to highlight U.S. inequality topics and to drive change in communities. His work has been published in TIME, Bloomberg, and the LA Times. He was a dual-degree masters student at MIT Sloan and the Harvard Kennedy School and was formerly a macro policy strategist at the Federal Reserve. He now works at Google and lives in Brooklyn.

Episode NotesJeremy on Twitter | Op-ed in Time

American Inequality newsletter: americaninequality.substack.com

Federal Reserve Bank of New York

Food Deserts and Inequality

Technology and Disability: The Relationship Between Broadband Access and Disability Insurance Awards

Some coverage of the map:

American Inequality
Paul Krugman
David Wallace-Wells
LA Times

Related EpisodesEpisode #228: Ethan Mollick
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Tristan is a Data Visualization Freelancer who likes to combine different techniques to find the best way to represent data. He regularly creates tools and videos to help people build their next projects or level up their skills. Tristan is the 2017 Iron Viz Champion, and current Tableau Visionary.

Episode NotesTristan | Web | Twitter | YouTube

Figma

Observable

PowerBI

Svelte

Tableau

Tableau Public

Related EpisodesEpisode #234: Kirk Munroe
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New Ways to Support the Show!With more than 200 guests and eight seasons of episodes, the PolicyViz Podcast is one of the longest-running data visualization podcasts around. You can support the show by downloading and listening, following the work of my guests, and sharing the show with your networks. I’m grateful to everyone who listens and supports the show, and now I’m offering new exciting ways for you to support the show financially. You can check out the special paid version of my newsletter, receive text messages with special data visualization tips, or go to the simplified Patreon platform. Whichever you choose, you’ll be sure to get great content to your inbox or phone every week!

PatreonNewsletterOne-Time with PayPalTranscriptThis episode of the PolicyViz podcast is brought to you by BlendJet. I’ve had my BlendJet blender for about a month now, and I love it. Instead of grabbing a bag of chips or a cookie or a brownie or whatever’s left over on the counter, I really quickly, really easily blend up a little smoothie in the afternoon, gets me that little afternoon perk that I need, filling me up to get me through the end of the day. The BlendJet 2 is a portable blender, so you can blend up a smoothie at work, a protein shake at the gym, or even a margarita on the beach. It is small enough to fit in a couple there, but it’s powerful enough to blast through tough ingredients like ice and frozen fruit with ease, and it is whisper quiet, so you can make your morning smoothie without waking up the whole house. The BlendJet 2 lasts for more than 15 blends and recharges quickly via USB-C, and best of all, BlendJet 2 cleans itself. You just put in some water, a drop of soap, and you’re good to go. So what are you waiting for? Head over to blendjet.com and grab yours today, and be sure to use my promo code, that’s policyviz12, policyviz, one, two, to get 12% off your order and free two-day shipping. No other portable blender on the market comes close to the quality, power, and innovation of the BlendJet 2. They guarantee you’ll love it or your money back. So blend anytime, anywhere with the BlendJet 2 portable blender, go to blendjet.com, and use the code PolicyViz 12 to get 12% off your order and free two-day shipping. Shop today and get the best deal ever.

PolicyViz Podcast Episode #237: Tristan Guillevin

Welcome back to the PolicyViz podcast. I am your host, Jon Schwabish. On this week’s episode of the show, I am excited to have Tristan Guillevin – that’s my worst French accent, by the way – join me on this episode of the show. Tristan is now a freelance data visualization designer consultant, and he does a lot of his work in Tableau, and he’s created some amazing Tableau add-ons, you might call them. He has a free open source site where you can go drop your data in and it will create Tableau templates for some more Bespoke data visualizations like [inaudible 00:02:20] charts, core diagrams, Sankey diagrams, those sorts of charts that can be more difficult for us to create on our own, and he’s created this great tool where you can just drop in your data, download a Tableau file, open it up, and you’re on your way. So I wanted to talk to Tristan about his work, about his journey to becoming a freelancer, and what he has planned in store for this tool that he’s created, and open it up to the community. So we’re going to get right to it. Here’s this week’s episode of the podcast with Tristan Guillevin.

Jon Schwabish: Hi Tristan, welcome to the show. Good to see you.

Tristan Guillevin: Hello. Good to see you too.

JS: I’m really glad we got to finally do this. I’ve been rescheduling on you, like, a million times. But I finally got you.

TG: I know scheduling can be difficult, no worries.

JS: Yeah, we are, but we got it, and I caught you now. So you’re in Mexico for a little bit?

TG: I am in Mexico for a while, yes.

JS: So for folks who don’t know, Tristan has this new fantastic, well, does lots of great work in general, but the reason that I want to chat specifically because you have these new, I would say, several tools, they’re all within the same sort of ecosystem, but several tools that help people create different, more advanced, more Bespoke visualizations in Tableau with just a couple of clicks, which is pretty amazing. I want to get there in a little bit, but I want to start by talking about your personal journey over the last few years because right towards the beginning of the pandemic, you started your own company, became a freelancer, moved countries, like, didn’t just move down the block, so you started this whole thing. So I’m just curious, what were you doing, what are you doing now, and what was that transition and change like for you?

TG: Yes, okay, so I think the best to answer that is to really start, let’s say, from the beginning, when I finished school, I always wanted to be kind of independent in some way. It’s something no one in my family has done, my family are like pure worker in the factory, and they don’t know anything about starting a business or something, and me neither. But I just like the fact of being independent, to not necessarily have a boss, and to not to have – to have freedom of working with who you want, where you want. So if, right now, I can be in Mexico, it’s exactly that, the freedom to choose. But yeah, I had no idea how to start that, and I also had no experience, so I started working in a consulting company for two and a half years just working with different clients, learning Tableau, getting better data visualization with Tableau.

Then for two and a half years I worked with a startup in France, so smaller team, but this time not as a consultant really, part of the team, working on a product. That’s where I learned mostly the coding aspect, GitHub, working in a project with multiple people with just like a consulting that goes from client to client. And then COVID happened, so that was five years after I started, COVID happened. And it was a bit of that weird timing, because I was also kind of finishing my project with that startup. What we planned to do, to build what we wanted to build was done, was almost finished, and we were wondering, okay, what to do next. And since there was also COVID, no one knew really what to do, how it was going to change and the impact.

So I asked my company, and I have to thank them to allow me to do that is I say, well, I would like to start being kind of independent freelancer, would you allow me to start working part time, so part time freelance, and they said, okay, for us it’s even good, because we actually don’t really know what to do right now, so you will work three days with us, and two days as a freelancer, and I started to do that for seven months. And after seven months, I chose definitely that path, and yeah that was not necessarily something really interesting for me to do, and actually the entire team that I was working with in the startup, they also all left. So it was kind at the end, and it was a good transition. I have to say it was also a time where companies started to accept fully remote working. So the place that I was living was not necessarily being that important compared to one or two years before. So I could work with clients in different parts of France, or in different parts of the world, while being in France, and that’s how it started. So it was not like, yeah, I quit everything from one day to the other to go that way. It was like a bit of, lucky to be at the right – it was aligning, my work let me do it, and it was a good moment to start.

JS: Yeah, it’s great that they let you sort of downshift a little bit for them, and everybody, and it all seemed to work out. So now, we’re sort of where, like, the end of 2020 or so, and you’re full time freelancing, but you’re living in France. So then you said, I’m going to try a different country.

TG: And it’s always something I wanted. I moved to the Netherlands mostly for personal reason, because I really wanted, but it was a first step into a new country. But yeah, right now, we are also, yeah, figure out which country will be next, because we are not necessarily – I will not say not attached, because we like the Netherlands, but it’s like, also wanted, like, having that will to see something else, and not necessarily as digital nomad that don’t live in a place but just go from, I don’t necessarily like that aspect, but more like, let’s see, in which place we really feel like living. And those things can change through your life, right? Maybe at some point, you like something, and 10 years later, you like something else.

JS: 100%.

TG: And I like the freedom to be able to do this, more or less.

JS: So tell us as much as you can about the freelancing work that you’re doing, like who, I mean, you don’t have to obviously reveal names or specifics, but like…

TG: Yeah, no problem.

JS: Yeah, what is the kind of work that you’re doing, and maybe even more to the point, like, what is the work that really gets you excited – I’m sure just like working in an office or some of the freelance work that you’re kind of like, okay, and some of the stuff really gets you excited. Now that you’re sort of in it, you’ve been doing it now for what, about two years?

TG: Yeah.

JS: Two and a half years, yeah. So what are the different types of projects that you’re working on?

TG: So first is more like what I don’t want to – I think that is easy, and it’s [inaudible 00:09:32] I don’t have a full time client. I don’t want to spend five days a week for client, because I think that is just going kind of back as being an employee in a place. So I have currently two main clients, and I’m just helping them with Tableau stuff. So it’s like they want new dashboards, they have problematics of performance, they don’t know how to do certain things, so I’m helping those two clients to be better at the Tableau, develop dashboard on them. And that is kind of like a, I don’t know if you have that saying in English, that [inaudible 00:10:15] so it’s like those two clients that I have been working for the past two years, and we have this relation of we trust each other. And I don’t have a specific amount of day, and neither are specific days in the week, they just ask me to do things, I do them, and then we have this ongoing…

JS: This ongoing relationship, yeah.

TG: So these are my stable income, if you want to say. And then, apart from that, from time to time, I have like new people reaching out and be like, oh, I’d like you to help redesign dashboards, because we have some things, and we don’t necessarily like how it looks, and we have seen what you have been doing on Tableau Public, we would like you to help us design. And that could be like really short project of two-three days, just pure, pure design, and then, after that, maybe I don’t hear from them for six months. And after six months, they will be like, hey, we have a new one that we would like your input. So it’s really free, I really try my best currently to not work full time for a client, to have the time to build the things I’m building. Because I think this has always been my kind of plan when I started, is like, I have a three-year plan. So the first year when I started, it was really about working as much as possible to have kind of emergency fund within my company, to be able to then do something. So the first year was really working a lot.

The second year was more about learning new things, because I like to learn new things, and if I am able to create the tool that I’m doing, if I want to stay relevant, I want to learn. So I spent a good amount of time learning Webflow, to create website, Figma for more the design aspect, and Svelte for the coding aspect. So before I was just the Tableau guy, and now I can really be like, okay, I can have this kind of complete vision of like Tableau is for BI, let’s say, for company; Webflow is to make my website; Figma is to do my design; and Svelte is – and D3, Svelte and all that, the web thing is because I also create things on the web. So that was my second year spending a lot of time learning those things and not necessarily doing anything with that, just learning. And now, that third year, I’m really focused on sharing, so sharing what I’ve learned, and sharing trading tools and really use what I learned to create things. So that’s why I created those tools, and I have other things I want to create, also make probably online courses by the end of the year to also teach, give back.

JS: Right, that’s great. So that’s a great segue to the next part, which is this, your site generally, and particularly this tool that you’ve been building out, which I believe at the moment has six different, I guess, templates that you call them, templates tools to build different types of visualizations. So network diagrams, Sankey diagrams, bump charts, beeswarm charts, Voronoi treemaps, which I should say, if I recall correctly, the Verona treemap one that you have is like multiple different shapes, it’s not just like in a square or in a circle, it’s like you can sort of…

TG: It can choose, yeah.

JS: Yeah. And then a chord diagram. So these are amazing, and I know that there’s lots of ways that people try to build these in Tableau natively. But I’ll just say so, well, actually, I’ll just say how I use it, but why don’t I just let you describe how people can use them, and then, I want to learn more about, like, what’s going on under the hood a little bit.

TG: So they started to exist, because I was mostly, it’s a mixed feeling of being lazy, and I noticed the way it was, it had to be done before. So before I was like, you had to take your own data and make some joints and scrape a lot of calculation, create table calculation, and basically the old tutorials were really long. Okay, now you need to create these calculated fields, write this, and it was just like a really long process. And before doing that tool, I never did a Sankey, I never did anything in Tableau because I didn’t want to go through all of that.

JS: All the steps, right, exactly.

TG: And like I said, last year, I spent a good amount of time learning new things, and I was like, well, it’s really so easy, or much easier to do a Sankey in D3 with Observable or anything than doing it in Tableau. Right?

JS: Yeah.

TG: And then, I think that’s what I like when you learn different things, and I made a talk a year ago at Elevate, it’s on my YouTube channel about how being constantly a beginner and having that beginner mindset opens connection or create connection in your brain, I would say to be like, okay, I know how to do this, and I know how to do that that is completely different. But maybe there is a path that could connect those two things to create something new, and that’s exactly what happens. So because I was able to create like a [inaudible 00:15:50] or a Sankey on network diagram with D3 as well, I was also like, well, maybe there is a way to convert the SVG, like an SVG into a polygon, and there are libraries that does just that. It’s like you gave them, you give the library a SVG path, and it converted into a polygon. So a set of dots that are connected, right, a set of coordinates that you can connect. Tableau cannot read SVG, Tableau doesn’t know what SVG is. But Tableau is really good at making polygons. So as long as you have like a X and a Y, and a plus, you can write pretty good. So I was like, what if I use – so when you use my tool, what you see is pure SVG generated with [inaudible 00:16:40]. And when you click on I want that visualization in Tableau, the tool will convert the SVG into polygons put that in a data source, and package it with a Tableau workbook. And then, when you open the Tableau workbook, you have that template that you never really see, it’s just like a template connected to the data source that you just generated visually. So I think I started by explaining how it works before saying how to use it. So to use it…

JS: Yeah, but that’s really interesting, I do want to get to how people can use it, but before that, so I feed my data, and we’ll get to that in a second, it generates the polygons, and then, how does it get to that part to Tableau so that when I – because when I download from your site, I download a Tableau workbook. So have you built sort of like a Tableau template that it feeds in automatically, because the data is always going to be in that same structure?

TG: Exactly.

JS: Yeah.

TG: So for each of the visualization, there is a Tableau template, a file that is connected to some dummy data, but that dummy data has the exact same structure, because I am in control of the structure, the data source. And then, when you click on download, it does a few things, because a Tableau file is just an XML file. So you can pass the XML, and change a few things. So, for example, my Tableau template have a fixed size of 1400 by 1000. But when you use the tool, you can configure the width and the height of the [inaudible 00:18:32] and then, I’m just going through the XML, and replacing that 1400 by your [inaudible 00:18:38]. So it just changed a little bit of things in the XML file, and then, the template is always connected to data source that is called network.csv, or cod.csv or sankey.csv, and I’m just replacing that dummy file by the one you just – by the one generated by the tool, and I packaged it, and I put it in your download folder.

JS: So I upload a CSV, essentially, it converts it to an SVG, then to XML, into Tableau. And at the same time, that XML and the CSV is what I end up pulling down as the user.

TG: The only difference is kind of the, yes, main thing is your initial CSV that you input will not…

JS: Right, that doesn’t come back, right.

TG: Yeah, exactly. I’m just reading that the tool is, and actually, that’s I think something really important to say is, like, the tool is not keeping any of the file, it just read it and used, so if you refresh the page, it slows, right, the file is not stored anywhere. And so, it’s just reading through the file, generating some coordinates or some polygons, and then, when you download, you actually download those polygons’ information that you can then use to make your visualization.

JS: Right.

TG: And so, the main difference between what was before, and that’s why I felt the need of creating this is it’s not a template in the sense of you are not following a tutorial, it’s really you input your file, you drag and drop a few things, and you click and you have it. So in that sense, it’s easier. It’s also, I know there is some extension in Tableau that you can download that allow you to create those things, a diagram network, Sankey. But the result will be D3 visualization, so SVGs, it will not be native Tableau. And what I create is pure native Tableau using maps layers. But the biggest disadvantage of my tool as it is right now and it’s something I know and I’m aware, and I need to find a solution for that is, it’s no longer connected to your data. So if you create like a network, and you have suddenly new nodes appearing, or if you make a Sankey, and your values change, you need to regenerate the new coordinate, that goes through the tool again. So I would say currently is really good for people who want to do Tableau Public visualization, because it really, it’s like a one-shot, you have your data, you publish it, and it’s done. So there is no refresh. I also know some journalists have been using it to create some network of some piece, because also once it’s printed, or when it’s done, it’s done, right?

JS: It’s done.

TG: Currently, it’s not usable in a business scenario where your data will change, because you will have to go through the tool every day to regenerate [inaudible 00:21:48]. So that is not currently, but, I guess, eventually, I will find a way. Also, I think it’s one point that I won’t mind working with someone who has an idea how to do that, to be like, hey, I would like to help you on – I’m not looking for any employee, I think I’m not at that stage, anywhere that stage right now, but to work with another freelance or someone who has an ID, I’m completely okay to take that part, to tackle that part. Yeah, because it really…

JS: Yeah, no, absolutely. But to this point, because we were talking about this before we started chatting for the show, it is worth noting that if I wanted to create several beeswarm charts, and I wanted to have them linked in terms of the action, you know, I have two beeswarms, and I click on a dot, and the dot highlights on the other beeswarm chart, that I could do using your tool.

TG: Yes. So you can do this, for example, also, on my Tableau Public, I have examples, I have actually one that I think does exactly something like that about Latin music artists. When you click on an artist, it will highlight in all parts of the dashboard. So not only on the network, but also on the line chart, on the bar chart and everywhere. So you can link multiple highlights action, even if the data are on different data source, and using parameter action. So when you click on anything, you will put the value of what you click. So in my case, the name of the artist, I put that in a parameter, and in all the other worksheets, I have a small calculation, say, if the name of the artist is equal to that parameter value, well, then it’s true, right? And then, you can follow when it’s true, and then, follow when it’s false, and then, the user is really always is when you click something, it highlights everywhere. So if you’re this one, you will have three different data sources, one for each beeswarm that you generate, but then when you click on one, you can use the same thing that you put the value in a parameter and then you are have it everywhere.

JS: So it’s not connected to the raw data, but it is connected kind of enough that you can build a, I mean, I don’t even know what the right framing in here, but you could build a comprehensive dashboard, even though you couldn’t use real time data or updating data. But I think it’s important for people to know that you can still use the tool and link it to other visualizations in your dashboard simply by kind of importing one workbook into another workbook, and then, using parameters to sort of link that together. So I think that’s important to know. But I want to get back because we sped ahead, but I want to get back to asking you to describe how people use it, because there’s a lot of options that you can play around with, like, on your site in the browser itself before you render the Tableau workbook.

TG: Yeah, so I think I’m at the stage that building the tool is really, I don’t know what to say, fun, in the sense that also Svelte is really fun to use, like, I really rediscovered that I like to code with Svelte. And so, if someone has a bug, or if someone has a request, someone would like to add something new, it’s really easy to add the thread, and I remember for this one, before you could only have like an X value, and now you can have X and Y, like, those kinds of requests. I can easily change and break things, and also come with solution. So it’s all the tools are bit like that. So you upload your file, it can be CSV, it can be Excel file, sometimes it needs to be a JSON in the case of the network. And then, you have a certain amount of, depending on the chart, you have a certain amount of parameter that you can change, so, for example, in a network, you can change the different forks that you will apply. In [inaudible 00:26:18] you can change the size of the bubble. In the Sankey, you can change the padding, and just released the core diagram, and you can also change the spacing, the size of the arcs. You can change a good amount of things. And then, when you expand the result in Tableau, and I think that’s what makes it different, and that’s what people – I see a lot of people using it, and creating things on Tableau Public is because I started because I wanted to create those things, right, I started because I wanted to make me being able to do network of Sankey more easily. So I also noticed that if I just gave the coordinates of the polygons, I could have the visual, but it was kind of ending there, right? I was, okay, how do I customize, how do I personalize it. So I was really trying to put as much information in the export for you to be able to go beyond that. So, for example, if you create a network diagram, the export could be just the position of the circles and the lines, and maybe the size of your circle. But if in your JSON, you have other attributes, like, for example, I made one, like, the first one about Marvel movies, then in my JSON file, I also added the phase. So Marvel movies are released in phases, so phase one, phase two, phase three, phase four. And if the information of the phase is in the data, then when you export the result in my tool, you will also find back that phase information for you to use it in colors. So if you create a beeswarm, for example, yes, I’m going to give you the coordinates of the points. But I’m also going to give you as many data that I can to help you maybe relinked that to another CSV where you have more information. So you can then make a join between the file that I’m giving you, and your own data to add more information, because maybe you need much more information. So I’m not just exporting the position, I’m exporting whatever I can…

JS: As much as you can, right.

TG: Whatever the tool can have in the extraction, and I’m giving you that.

JS: Yeah.

TG: In the core diagram, for example, I think it’s always difficult to put colors in a core diagram, I never really know if it’s like how do you – there is so many ways to make a core diagram. So I’m giving you what is the name of the source, what is the name of the target, and the value of the source, the value of the target, so you can also then make your own calculation saying, well, if the value of source is higher than the value of the target, then I will use the color of source to see each core will have the color of the one that is superior to the other. Or you can choose to do the opposite. So actually I was giving the flexibility to the users to create those things and…

JS: Right. And then, like you said, you could try to join your original data or another dataset back onto that.

TG: Exactly.

JS: Yeah, I also want to know, like, for listeners, because I was using it to make a beeswarm chart, and I wanted to do something kind of specific, and then, I just like DMed you, and you’re like, oh yeah, hold on. And like, two minutes later, you’re like, okay, that’s fixed. And like, there was this new capability, so like, it is a really interesting project that I think is giving a lot to the community, I was going to say Tableau community, but I think it’s beyond that, because you don’t really have to be a sophisticated Tableau user, because you can just download and double click. But really, you can do a lot in the tool itself, like, in the browser.

TG: Yeah, that’s why someone asked me to add SVG export. So now I’m not, like on the Flourish, or I’m not – they allow you to, like, I’m just by myself. But if you want, you can create core diagram, and export the results in SVG [inaudible 00:30:50]. And then use Illustrator to finish your work or anything. You can also just export the CSV, just the data generated, and then, probably I am not a Power BI expert, but you could also use that CSV as a source in Power BI too. And I don’t know if Power BI allows you to create polygons, but we’re not like – and like, in the end, you will just have like coordinates. Right? You will have coordinates of polygon. So if you want to input that in any other tool that you want, please do.

JS: Yeah, it should work.

TG: My expertise was always Tableau, and I have always – I started with Tableau. So for me it was – it started as how can I make those visually more easy to build in Tableau. But then, yeah, I was like, well, if I can generate coordinates, then probably you can just put them anywhere you want.

JS: Right, should work anywhere. Do you have plans for more?

TG: Yes. I think I try to release two type of charts every month. So now we are beginning of March, I’ve just released today the core diagram. And I think the next one will be stream graph. I’ve already started, and I think also it’s like a nice visualization that is not by default, it’s not in Tableau, so yeah, why not. And sometimes, people reach out to me and be like, hey, I would like this and that, and I evaluate. So, for example, one was about gauge chart.

JS: Yeah, gauge chart, yeah.

TG: Yes. And I see why, because it’s difficult to make them in Tableau. There are a lot of tutorials about how to make them in Tableau. But also I feel that this is something that needs to be connected to your data, right? It is really, you want your gouge to update, and show you. And if you have to go through a tool to generate it every time, it’s like maybe one day if I manage to do the refresh…

JS: Yeah, that last shot, right.

TG: So yeah, I’m just – and so, currently I’m focusing on making visual tools for Tableau. But I also have a big idea that I would like to experiment this month or next month. That has nothing to do with visual, but also, since I already have something that can manipulate the XML of a Tableau file, and then, giving you back, I’m trying to think about new ways to automate things that are really annoying to do in Tableau, through a tool that will make it easier for you. So basically, what I’m doing, I’m building those tools because I want them for myself…

JS: For yourself, right, yeah.

TG: When I work with clients, I am always – I want to save time in, like, that’s the most important thing is like I want to be able to create things faster. So everything that is really fastidious, long, and that doesn’t bring anything is just a lot of clicks. I am trying to think of ways that I can make that a no-impact and faster.

JS: Yeah, absolutely. So you’ve got a lot of great stuff building it out, so before we wrap up, how can people find you, and how can they keep up to date on what you are releasing, so that they know like when that stream graph comes out that they’re itching to be – I am sure there’s someone listening to this is like, I want to make that stream graph, so how can they make sure that they find out about that?

TG: So it’s quite easy. I have spent a good amount of time making it [inaudible 00:34:51] LaDataViz. So LaDataViz, I am LaDataViz anywhere, right, on YouTube, on Twitter, on LinkedIn, everywhere you can find me as LaDataViz. So depending on your favorite social media or whatever, it’s LaDataViz. If you want to keep up to date on a daily basis, it’s mostly Twitter. So I’m just sharing as often as I can on Twitter the new things. And if that’s not your thing, and I completely understand, I have a newsletter that I send at the end of each month that just is a big recap of all the things that I’ve done during the months. So I’m not selling anything in those newsletters or anything is basically, like, okay, all the new features in the tools of March, and I just kind of list them. So those are the two things. And the third pillar of my communication is YouTube. We will be more and more on YouTube, so I started currently to do just the tutorials of how to use my tool in YouTube, but I also plan to do much more than just tutorials, like, really discussing about data visualization, good practices, maybe also, like, some introduction to Svelte and D3. Because my community is mostly, the people who are following me are mostly Tableau people, and I know a lot of them are afraid of coding or having this image of like, no, D3 is like that super complicated thing and on the web. But I really think that the new approach with Svelte and D3 simplify a lot. You don’t have the chain D3, you know, enter up and join. Those things you can – I think the experience of making visuals on the web in 2023 right now is much better than it was five years ago, easier, more enjoyable. So I also want to share those kinds of things to show people that it’s not that.

JS: Yeah, that’s terrific. Yeah, providing those skills, and the learning the tools for folks is fantastic. So I’ll be the first to say thank you if no one else has, but I’m sure many have already reached out, so thanks for making this. Tristan, thanks so much for coming on the show. Thanks for making these open tools, and I’ll look forward to the next set. So thanks a lot for coming on.

TG: Yes. Thank you. Thank you for inviting me. I know you have been using Tableau more and more, so maybe you [inaudible 00:37:34] Tableau visualization using my tool.

JS: Yeah, for sure.

TG: The community your listeners are who are not necessarily Tableau users can now see and try those tools and maybe discover that actually you can do for free also, because you have Tableau Public, so also you can create some key [inaudible 00:37:53].

JS: Yeah. Terrific. All right. Thanks so much, appreciate it.

TG: Thank you.

Thanks everyone for tuning into this week’s episode of the show. I hope you enjoyed that. I hope you will check out Tristan’s tool. I do have a big favor to ask. If you are listening to the show on iTunes or on Spotify, please consider leaving a review and a rating of the show on the podcast provider. The more people that get to know about the show, the more likely it is I can get bigger and better guests to join me on the show, so that you can learn more about data visualization, about presentation skills, and all those other things that you know that you can learn about here on the PolicyViz podcast. If you’re on YouTube and watching this episode, please give it a thumbs up, subscribe to the channel, or just leave a comment. I’d love to hear what you think about the show. So thanks again for listening to the episode of the show. Until next time, this has been the PolicyViz podcast. Thanks so much for listening.

A number of people help bring you the PolicyViz podcast. Music is provided by the NRIs. Audio editing is provided by Ken Skaggs. Design and promotion is created with assistance from Sharon Stotsky Ramirez. And each episode is transcribed by Jenny Transcription Services. If you’d like to help support the podcast, please share it and review it on iTunes, Stitcher, Spotify, YouTube, or wherever you get your podcasts. The PolicyViz podcast is ad free and supported by listeners. If you’d like to help support the show financially, please visit our PayPal page or our Patreon page at patreon.com/policyviz.

The post Episode #237: Tristan Guillevin appeared first on PolicyViz.

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Gabrielle Ione Hickmon (b. 1994) is a Black woman from a middle place—Ypsilanti, MI. Her lab is a place where clay and words meet. She is interested in body memory, waiting rooms, layovers, circles, Black imaginaries, and ocular proof.

Her work includes essays, ethnographic research, and coil-built ceramics. She won Bronze in the Leisure, Games, & Sport category of the 2022 Information is Beautiful Awards and First Honorable Mention in the 2022 NYU American Journalism Online Awards for her ethnographic research project, How You Play Spades is How You Play Life: Spades in the African American Community. Her writing has appeared in Condé Nast Traveler, The Baffler, The Pudding, Literary Hub, and elsewhere. She attended Cornell University and the University of Pennsylvania. She has been in residence at Pocoapoco, Mas Palou, and will soon be in residence at Dairy Hollow, Mudhouse, and Haystack.

Gabrielle is currently at work on The Boyne City Project, a series of vessels chronicling her family history in Michigan which dates back to before the Great Migration, an essay collection, and a memoir. She works out of a studio in Ann Arbor, MI.

Episode NotesGabrielle | Web | Instagram | Twitter

How You Play Spades Is How You Live Life at The Pudding

Information is Beautiful Awards

Mixed-ish from Kenya Barris

Do No Harm Project from the Urban Institute

Nvivo

Matt Daniels at the Pudding

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PolicyViz Podcast Episode #236: Gabrielle Ione Hickmon

Welcome back to the PolicyViz podcast. I am your host, Jon Schwabish. On this week’s episode of the show, we continue a little bit of our journey talking about data and art, and I’m very fortunate to be joined by Gabrielle Ione Hickmon – Gabrielle won one of the Information is Beautiful Awards back in December of 2022 for her project on Spades that was published at the Pudding. And Gabrielle combines a very interesting background of research and writing and art, and she created this really fantastic piece with the folks over at the Pudding on Spades, and you can’t see this if you are listening to this podcast, but right now I’m holding up a copy of the card deck that was published, printed along with the digital piece at the Pudding about the history of this game, Spades. And it really reaches deep into the Black and African American culture and history, as you’ll hear in our conversation, and it’s also just an interesting story about thinking about how we can combine qualitative research, historical documentation, and our own experience into telling immersive stories. So I’m going to let Gabrielle tell you all about it in this week’s episode of the podcast. So here is my discussion with Gabrielle Ione Hickmon.

Jon Schwabish: Hi Gabrielle. Good morning. Good morning, my time, it’s not Barcelona.

Gabrielle Ione Hickmon: Yeah, good evening, my time.

JS: Yeah, your time. Okay. So before we get into talking about your work, so tell me a little bit about what you’re doing in Spain.

GH: Yeah, so I’m in Barcelona right now, I just got here today. I’ll be here for a few days. And then, I’m actually heading off to a residency at Moscalu for a week [inaudible 00:03:33] working on a book project while I’m there.

JS: Oh that’s cool. So this is just a week for you to just, like, sit in the countryside of Spain and just work – economists, we don’t get residencies, so, like, what does an artist’s residency look like, what does it feel like?

GH: Each is different, some of them have like a lot of activities built into them to try to stimulate your craft or your practice and some of them are more like crude form for you to fill in the space. This one seems to be kind of a good mix of the two, there are some opportunities, and I’ll be there with a group of people, so there’s obviously time and space for connection, but I’m really looking forward to having time without kind of having to balance, like, all of the other things in life, like, to read with my project, and start drafting for my project. So yeah, it’s just kind of a structure of uninterrupted time for me to hopefully only do my trade practice.

JS: Yeah, that’s terrific, that’s exciting. Yeah, like I said, not a lot of residency programs for us economists. So I reached out to you – actually, we met at the Information is Beautiful Awards a few months ago here in DC, when you won for your Spades project. I’m going to hold up the cards but people on listening can’t really see this, but I have the physical card set. So I want to talk to you about this, and talk about some of your other work. But maybe we could just start with you telling folks about the project and what inspired you to do it, and then, we can talk a little bit about the nuts and bolts of it.

GH: Yeah, so, I mean, I grew up watching people in my life, like, my aunt, uncle, my mom, my dad, my older cousins. I wasn’t allowed to play then, because I was sitting at the kids table. But I saw, and I saw how, like, at every Christmas, or every barbecue, someone was playing spades or dominoes or some type of card game. And then, when I turned 18, and I was off to college, my parents taught me how to play, and I played all through college, and now when I get together with my friends, we’re often playing spades, and I have the same spades partner in my friend group, we kind of keep a running mental tally of you’ve won how many games, it’s really actually very serious. And during the pandemic, kind of, at the height of it, because, of course, it’s not over, my mom and I were playing cards a lot in the house together, we weren’t playing spades because we are only two people in our household, and we kind of need four people to play, but we were playing Phase 10, we were playing Five Crowns, and I kept also like seeing spades pop up in popular culture.

So there’s a mixed-ish episode that deals with spades or spades is like played in the Batman movie and TV series, and in securities a way is we are having a beach party and spades comes up. Right? And so, I just kept seeing it, and I kept thinking, like, oh – and also, spades is always being debated and discussed on Black Twitter at any given point in time. So I just kept seeing it around, I was playing cards, and I kept kind of – just a question popped into my head of like, why is this so important to us, where did this come from, why [inaudible 00:06:44] and where did this come from. And I couldn’t find any answers that were satisfactory to me online. And so, I just said, okay, well, I will figure it out, or I’ll do it myself, if that record, that data isn’t there, then I’ll be the one to create it.

JS: Right. But what’s interesting about what you did was you combined the historical record or experience with your own survey, your own data collection.

GH: Yes.

JS: So can you talk a little bit about – I guess, the question is, I do want to ask, like, how you did the survey, but what was your thinking there, did you want to get sort of the present and the past, like, how were you thinking about sort of doing both of those pieces?

GH: Yeah, I mean, I think, for me, a lot of the times when I’m doing my work, in general, but especially this piece, it’s always important for me to have records for black folks, and just, in general, society to come back to, because there’s so much of human history that hasn’t been written by people who [inaudible 00:07:48] or my history has been discarded or damaged or just not given the attention that it deserves. And so, part of it was, okay, like, understanding, yeah, past, present, and maybe, future, like, where did this come from, how has it endured for so long, what does it mean in the present moment, and how might it like hold space or make room for black futures beyond kind of what we’re living through or experiencing right now. So it was both about, like, establishing a record, and then, also, I just really wanted to work with the Pudding. I had one that I had been reading their newsletter and seen work for years, and I was just like, kept telling myself, one day, you’re going to have an idea you’re going to work with them. And this just ended up being that, because it just felt, even from the very beginning when I was pitching and thinking like conceiving of the idea, it just felt like it couldn’t be your kind of stereotypical article with text and a couple of pictures, and then, like, it couldn’t, let’s say, [inaudible 00:08:55] a vibrant thing, and it just felt like that kind of more standard, but internet format wouldn’t do the game itself justice, in the telling of the story.

JS: Yeah, so tell me a little bit about that process, working with the putting folks, I’ve had some of them on the show in the past, but what was it like, what was your – I mean, you are artist by background, so I don’t know, like, if you have a ton of HTML coding background, I certainly don’t, but what was the process of working with them and building out the story on the site itself?

GH: Yeah, so from pitch to story on the site was eight or nine months, and from pitch to selling the first project was a full year. So we worked on the story all the way through the process for a year, which is kind of crazy, and it started out, and I just kind of pitched them, hey, I want to write something about black people in cards, and I was thinking about multiple card games like this Phase 10, Uno, Five Crown, Spades, lots of different card games. But I think there was a specific line in my pitch that kind of spoke maybe specifically to Spades, and so Matt Daniels, who was the editor that I probably worked the closest with at the Pudding responded and was like, this is really interesting, I think we should just do the stage piece of it. And so, shout out to him for kind of being able to see through what all was in my pitch to like [inaudible 00:10:27] that real nugget. And then, we had, like, I had a call with I think Matt and Jane and myself. Jane did the designs for the piece, and I know the other thing for the Pudding, which is one of their main designers. And so, we had a call, just like talk through it more, and then from there, they were like, yes, we’re interested, you do all the contract typeset. And then, it was literally just me and Matt meeting every week, every two weeks, and that first stretch of like, all right, how do we actually do this, because there isn’t any data on it that we can [inaudible 00:11:03]. Or, like, there are books about [inaudible 00:11:05] which is the parent game of Spades, but there wasn’t really anything that was directly, here’s the history of Spades, just like, some of it was a lot of like, all right, this doesn’t exist, so we have to create this data. And then also we have to find different sources and places where we can pull from to kind of fill in the gaps or speak to speculate of what kind of this history might have been based upon the records that we do have. And so, we met every week, every two weeks for that year long time period. And just kind of took the project in phases. So at first it was like, all right, we know we need to do a survey, so let’s figure out a survey question. So I drafted them, Matt would respond, and we’d meet and talk about them. And then, it’s like, okay, we need graphics to go with Pudding [inaudible 00:11:59] put the survey into type form, we need to then send it out on Twitter, and then, it’s looking at that and saying, okay, we’ve got, maybe it was like [inaudible 00:12:10] the responses, what are the demographics of that response. So from age, from gender, from regional location, things like that, to try to tell as complete a picture of Spades, and the African American community in the US. And so, it turned out that we didn’t have enough people in older demographics, we didn’t have enough [inaudible 00:12:33] representation. So then we had to go and use like Pollfish, I believe it was, and do a much more targeted kind of push to get people to fill in the data that we didn’t get just from kind of organically sharing it on Twitter and Instagram. So we got all that back, and Matt coded the data, quantitatively, and I coded it qualitatively, because I’m trying to do qualitative methods. And so, then we kind of sat with what came from both of those analyses, yes…

JS: Yeah, those pieces, yeah, right.

GH: Yeah, we sat with what came from those, and then, I kind of went off and did some reading and some research into, okay, well, what is our here about space that I can try to glean from. And then I had to write it. But it was – I think I maybe started drafting it in May or June. It was five or six months…

JS: Pretty far into it.

GH: Yeah, it was five or six months of work before I even put any words onto the page. And so, then extracting it, and then trying to figure out, okay, how do we pull in the research that we did into it, the data that we have, is this section interesting, or how do we make this engaging, just in the actual kind of narration on the page, and then, we had to go, okay, how are we visually presenting this, and what does that look like, and then, you go through the design, and then, I didn’t do any of that, the coding or the design. I like, I’d say that I created directly the process, but I don’t know how to do GitHub or – I want to learn, but I don’t have the exposure.

JS: I hear you.

GH: So they did that, and we would review it. And then, I also pulled in friends and other sources to look at it as I was going, like, am I on track here based upon your experience with Spades and what you know about it, just to make sure that it wasn’t necessarily true just to my experience or my interpretation of the game. And then, once we kind of had the narrative and we had edits, and we had the designs, and we had the code, it’s time to publish. And at some point within that time period, we’re kind of getting burned out on it, it was a long time to work on one thing. And so, it was close to when we were going to publish and we were like, we should print this on actual cards. I think that got us really excited about the piece, because it was something that neither of us [inaudible 00:15:02] had ever done before. So that was kind of like that real push to, like, let’s get this out, because we want to then do this, like, next cool thing.

JS: Right. So I want to ask about the card piece of it, so it sounds like you were writing – when you were in that phase of writing the text, were you thinking we’re going to lay this out on cards, like, was that like, were you going into it like that, because I would imagine that that would affect how you would write.

GH: Yeah, so when I pitched it, what I kind of – I drew something that essentially looked like a Spades table. So the screen would have had four hands kind of at North, South, East and West, and my idea was that you would click into one of the hands, and then, kind of, read that story section, that section of the piece.

JS: Right.

GH: So even from the pitch I was kind of thinking about this is the Pudding, like, they do this… You know what I mean?

JS: Yeah.

GH: So I knew I had to have some idea of how I wanted this visually to come together. That ended up being too complicated to code, and also just kind of didn’t – I think we ended up having more than four sections. So we have three sections, those just didn’t work. And so, Matt, Jane, we were all kind of brainstorming and came up with the idea to do it. It’s best on a mobile device, because that’s inherently vertical, but to do it on cards, because obviously [inaudible 00:16:30] so it just kind of made sense.

JS: Yeah, it’s interesting, because the desktop version, cause I’ve tested all three of them, right? So I’ve got the physical card set, which my daughter and I were reading this weekend. I’ve played with it on the mobile phone, and I’ve played on the desktop. And the desktop, it goes horizontally, and that’s sort of interesting. I haven’t tried them on tablet, which maybe should be like, I mean, that’s just…

GH: Tablet is vertical.

JS: Is vertical. So for the desktop, what was the thinking about keeping – this is just kind of an aside, but I’m just kind of curious – what was the thinking of having it horizontal on the desktop, but vertical on mobile?

GH: I honestly would say you probably have to ask him that, what that answer is, and I’m not sure that I remember. It might have been just like a limitation in terms of the coding, and the way that you had to have to code it to…

JS: Had to code it, yeah.

GH: To work on that for certain.

JS: Right. The technical piece is always like…

GH: Right. And then also, we read from left to right, and so, it’s also kind of a bit disorienting for me to be reading something that scrolls vertically on a desktop. But on your phone, that made sense. And so, I think we also kind of talked about it from that perspective as well.

JS: Yeah, that is really interesting, right, because we read left to right, but the motion on the phone is natural to scroll up and down.

GH: Right.

JS: Yeah, that’s really interesting. You also mentioned that in this whole process you asked friends and presumably family that just read through it, and I get the sense from you both of what I’ve read and what we’ve been talking about, this is a personal – this project has personal meaning to you clearly, like, rooted all the way back in your family not letting you sit at the adult table, which I enjoy. But like, what were the conversations like with your friends and family when you were showing them this piece, especially the early drafts?

GH: Yeah, I mean, a lot of it was like, this is really great, when is it going to be out. And that was even just because I was kind of giving people updates on Twitter and stuff, people knew that I was working on this for that year. But then, I mean, one thing that came up, I have a friend Phil Lewis, who’s an editor at HuffPost, he’s really great. I had him read a piece and give me some editing feedback. And in an initial draft, I had just written black, and he was like, well, I think you really should hone in on Africa, we need to pick one, it’s either black or African American. Well, African Americans are black, not all black people are African American, so like, true to the specific ethnicity, given that Spades is a game that originates from folks who were enslaved in this country, and so, that’s a different black experience than someone whose ancestors were not enslaved in America, specifically, of course, now there was [inaudible 00:19:26]. But that was even a function of, okay, we need to be specific to the actual story that it is that you’re trying to tell. I had even gone and interviewed some Caribbean and African black folks, because all black people play Spades. But those interviews were coming out was like, oh I learned this from my African American friends. And so, they went with Phil’s feedback, and then Rob, who was an undergrad [inaudible 00:19:56] he was like, yeah, we can actually leave that out and just like, it’s okay for this to just be about the African American experience instead of trying to like encompass…

JS: Right, the broader.

GH: Yeah. And then, also the history section, I’m a history nerd, I’m trained in social sciences, so I’ve always loved history was longer, and it wasn’t interesting people. So we cut a lot of that out to make it kind of flow better, more interesting. I also at one point wanted to kind of include something about my personal history with Spades. I didn’t end up making the cut either, because it just, as the piece like, took shape, it just didn’t make sense to go from this survey to history to random personal diatribe which didn’t fit the narrative, some of the things that I kind of wanted to do or we were thinking about doing that didn’t end up kind of…

JS: Didn’t work, yeah.

GH: Yeah.

JS: But now we’ve got it on the podcast, so now folks know a little bit about your personal Spades’ history.

GH: Yeah.

JS: You had mentioned also earlier that out of the survey that you all ran, Matt was doing the quantitative side, and you were doing the qualitative side, and I’m curious if you could talk a little bit about your analysis of the qualitative data. I mean, I guess, were there specific methods you used, did you use tools like InVivo or something else, and then, what was the richness that you found from the qualitative piece?

GH: Yeah, I coded it by hand. I’m a pen and paper, or paper and crayons girl. And so, I remember just kind of using different colors to represent different codes, so particularly, like, one of the things that comes to mind is that question about how to speak and to feel, thinking and seeing, okay, how often are certain responses coming up, whether it’s excited, or blank, or love, or family, or whatever else, like, coloring each of those every time that came through in the data, and then, kind of doing that same process to all of the different questions to kind of see what is the narrative story that’s emerging from the data that people are giving, which I think is really helpful to get in. And then, I also did interviews, so I’m looking at this, I’m coding it, and then, I’m saying, oh, I really liked this person’s answer to this, or this perspective is really interesting, let me reach out to XYZ to then have an interview to talk even further about their survey responses, and that’s where you get Terence’s quote and the piece or Robin’s perspective, like, that’s where that comes in. And even within that, we were trying to think about gender, age, where in the country do you live, all of that, so that even in the people who we’re quoting in the story, there was representation and diversity.

JS: Right, that’s really interesting. The whole thing is pretty interesting, because, in some ways, even though it’s obviously on the Pudding and celebrated and won an Information is Beautiful Awards, in some ways, it’s not really kind of like data visualization project. I mean, it has data, but it’s a storytelling project. So when you pitched it, were you thinking, oh this is a data project, or, were you thinking, this is a storytelling project?

GH: I think I was thinking this is something that doesn’t exist that needs to exist. I have the ability to create it. I was really excited about using my training in the social sciences to do [inaudible 00:23:37]. I was really excited about being able to say, I’m going to create a survey, and then, I’m going to code the survey, and then, I’m going to use that to, like, it’s almost like – and I have a lot of friends and people in my network who are in academia, a lot of them are like this could have been like a dissertation project.

JS: Yeah, I mean, absolutely.

GH: Under the kind of way that I both went about it, and then, also kind of in how it ended up, and people have said that it’s changed the way that they think about who’s our friends or like professors in the academy, and it’s changed the way they think about sharing that information from their research. Right? Because I could have done all this and put it in a journal, and then, no one [inaudible 00:24:19]. But that’s not the point. And so, I think I was excited about being able to merge my passion and interest for writing and telling black stories with my academic training. I have a master’s in social sciences, I’ve taken all these different methods courses, and so, it’s really exciting to get to – and I bring my research, it goes into everything that I do, right, which is conservatively to get to kind of merge the two on a project. Like I said before, I knew that it needed to be visual, and I just had loved the Pudding for forever, and so, I knew I wanted to work with them, and so it was kind of all [inaudible 00:24:59]. I mean, we do have visualizations in there. We have graphs, there is that, but yeah, I do agree that in some ways it does feel, it’d be like a narrative history project, that then has…

JS: Has data with it.

GH: Yeah.

JS: But it’s not like a dashboard kind of like, a lot of the things that were at the Information Awards, it’s a dashboard kind of thing. But yeah, this is more of those immersive stories. But what’s also, like, the physical cards, what’s great about the physical cards is we all know the internet just kind of like things are going to disappear from the internet, but the cards will always exist. Right? So the physical – have you played Spades with the cards?

GH: I have not, but my family members have, so we [inaudible 00:25:43] my mom, two of my uncles and a family friends, they played [inaudible 00:25:47] with my card deck, I think [inaudible 00:25:49] Spades, and that was like a really, really cool moment for me.

JS: Yeah, that’s pretty cool.

GH: [inaudible 00:25:52] playing with the cards. And then, we just did like another print run that sold out, so it’s exciting for me, like, the card deck is really like an everyday accessible archive, because you can sit there and read the story, but you can also play with it. And so, there’s something interesting to me in that, like, being able to kind of be so hands-on and tactile, like, engaged with this archive, with this story, with this archival object, with this art object that also has a real world purpose. And I’m also, when we did the print, when I got some decks just for myself, one for my personal archive, but I’m also endeavoring to get them into black and other cultural institutions [inaudible 00:26:36] in the States, but elsewhere around the world, so that they can also be properly preserved beyond people having them in their homes as well.

JS: Yeah, and it’s so interesting to think about a cultural story, and then, being used in actual gameplay, and how they kind of would wear over time, and how those various things just kind of interact in the non, you know, in the analog world, in people’s actual lives, it’s just kind of interesting to think about how they’ll bend and they’ll tear and they’ll, when you, you know…

GH: Yeah.

JS: And they won’t be as slippery and glossy after a couple plays, right, like [inaudible 00:27:14]

GH: Yeah, I haven’t even thought about that. I mean, I know some people who have bought multiple decks so that they have one that they can play with, and then one that is preserved. I even have some friends who, when we did the – I have friends who bought decks the first time, and then, we did the reprint, they were [inaudible 00:27:32]. And I was like, yeah, well, one, you should get one preserved, they’re going to sell out, and then, two, then you have one [inaudible 00:27:37] and you have one that you can just keep. And so, I think a lot of people have thought about it that way as well, recognizing, like, oh, this is an archival art object, let me have one that I take care of, and let me have one that I touch and interact with and play with.

JS: Right. So now looking forward, do you see yourself doing other projects – I don’t want to say similar vein, but, I guess, I mean, a similar vein insofar as doing another survey and doing that sort of social science research type thing and immersive, sort of, blending it with another story?

GH: Yeah, absolutely, I mean, I have every intention of getting a PhD. So there’s obviously a lot of research efforts that will go in that, and also I was in the words of [inaudible 00:28:28] interested in everybody black. So a lot of my work always is centering around black culture, so there definitely are some other projects that I’m thinking about, whether I’d do them in school, or I would just try to, like, the next thing that I do, one, I think, will take longer time just because of how I’m conceiving of it in my head. And two, I want to get like a big grant to do it, kind of, upfront or have it be like what I’m doing for school, which, I think is doable, we didn’t have this ones before.

JS: Absolutely.

GH: Etc., etc. So I definitely do have some other projects in mind. They’re kind of still rooted in my personal black experience or things that I’m kind of noticing in my life and in my worldview. But yeah, I definitely do have some other ones in mind that I’m hoping to kind of get going on in the next year or so.

JS: I kind of already feel bad for your PhD classmates who are going to write a dissertation, and it’ll be just like 40-page essay, and you’re going to come up with some storytelling thing that’s going to blow people’s socks off, so.

GH: Hopefully, I mean, but also, hopefully, the academy doesn’t try to leave that out of me.

JS: So in ECON, at least, like, one of the guiding principles or lights is like you do your dissertation and then you try to get the chapters published as [inaudible 00:29:50]. So, like, maybe you don’t do that in grad school, but you turn that thing into something else. I know lots of history folks, they end up publishing their dissertation as a book later once they learn how to write for the non-academics.

GH: I’ve been writing for non-academics, so I’ve done both, so it’d be interesting to see kind of how that goes. I’m also hopeful that having this part on my CV will be interesting [inaudible 00:30:18] just like, oh yeah, we should accept her, so fingers crossed, we’ll see what happens.

JS: I’m rooting for you. That’s awesome. Do you want to talk for a moment about the project you’re working on during your residency?

GH: It’s not the project that [inaudible 00:30:34]

JS: Okay.

GH: I talked about that one probably, I’m [inaudible 00:30:37] on my website though.

JS: Yeah, let’s talk about that one. So that also seems like a pretty personal project to you. So maybe you just talk about that. Let folks know.

GH: Yeah, I mean, a lot of – yeah, like I said, a lot of my work is personal and rooted in my experience, or what I’m seeing and kind of parallels in my experience, a lot of black experience. But the project that I’m starting on now is mostly rooted in ceramics, so if you didn’t know, I’m a writer, a journalist, but I’m also a researcher, and I also work with clay and ceramic artists. And so, it’s called the Boyne City project. The kind of short and sweet version of it or gist of it is that my great, great, great grandparents helped found the city in Northern Michigan called Boyne City in the 1800s; a lot of kind of their history and mark on the town is still there; there’s a church that my great, great, great grandmother, like, when you go into it, there’s a photo of her in there because she’s a family member; there’s a historical marker outside of the church, there’s few things after them, there’s all kinds of stuff that so exists. And so, this project is very much still taking shape. But what’s come to me so far is specifically through clay, it’s about kind of reaching back and trying to understand and I always kind of group my practice at African tribal traditions, but also to think about I’ve forgotten a crucial piece of this, I hope this make sense. They own the brickyard and bricks are made of clay, and so, for a long time, I’ve been interested in ceramics, and felt called to ceramics, and in the last few years, I’ve stepped into that kind of calling. And so, I now work with clay and see it as a continuation of the work that my ancestors did in their brickyard and with clay. And so, it’s both about, like, the fact that I can’t trace my – I can only trace my history so far back, I can’t tell you where in Africa I’m descended from, so part of it is like looking towards those methods that I don’t – that I can, like, intuitively and bodily understand and work with and act on, but can’t necessarily directly speak to. And then also, thinking through, like, the history itself in Michigan, documenting that, and just like bringing it into a larger consciousness, right? It’s not a narrative, I feel like you hear about, like, people in the 1800s said often. And so that, for me, means that it deserves to be told. And then, thinking about the present of like, well, what does this mean not only for my family, but how does this change our understandings of the black experience, like, blackness in that time period, what does it mean that, like, this bit – and not that I aspire to being a billionaire or anything but you see people like the Rockefellers or Vanderbilt, like, who were operating in similar time periods and kind of parallel industries may have all this sustained wealth, why isn’t that really the case for my family. I’m also kind of interested in questions like what does it really mean to be from a place, because I would if anybody’s like from Michigan, I think I’m from Michigan, I can trace that back, I guess. And so, it’s just,, there’s a lot of different questions around it. Right now, it’s mostly kind of taking shape through my ceramic work, but I do think that eventually there will be some sort of written piece to it that this hasn’t kind of come to me as clearly yet. Yeah, it’s a family history project, and I’m hoping that, as I continue to do it, it will also inspire people to, again, reflect on what it means for black people in American history and black Midwestern history, specifically outside of Chicago and Detroit [inaudible 00:34:28] think of black people as being, what does it mean for them, I’ve been there before the Great Migration, which is typically when folks moved up from south to north. But also just to inspire people to look into their own family histories themselves, because I think you can’t know where you’re going if you don’t know where you’ve been and where you’ve come from.

JS: Well, that’s a great note to end on, Gabrielle, thanks so much for coming on the show. Good luck on the residency. I’m rooting for you on the PhD program, although, get ready, because it’s, yeah.

GH: Yes.

JS: But the piece is fantastic, and I’ll share the links on the episode page so folks can check out both the Pudding story, and your work on your website, and I’m looking forward to the next thing that you come up with. So thanks so much for coming on the show, I appreciate it.

GH: Thank you so much for having me. This was really fun.

And thanks for tuning in to this week’s episode of the show. I hope you enjoyed that. I really do hope you will check out the story on the Pudding website about Spades. You should also consider just buying yourself a card deck, it’s great, like, to actually hold the project in your hands is really just fantastic. So I hope you enjoyed this week’s episode of the show. Thanks for tuning in each and every other week, and I hope you will stay tuned for more great podcasts, great episodes, and other great content on the PolicyViz website over the next few months. So until next time, this has been the PolicyViz podcast. Thanks so much for listening.

A number of people help bring you the PolicyViz podcast. Music is provided by the NRIs. Audio editing is provided by Ken Skaggs. Design and promotion is created with assistance from Sharon Sotsky Remirez. And each episode is transcribed by Jenny Transcription Services. If you’d like to help support the podcast, please share it and review it on iTunes, Stitcher, Spotify, YouTube, or wherever you get your podcasts. The PolicyViz podcast is ad free and supported by listeners. If you’d like to help support the show financially, please visit our PayPal page or our Patreon page at patreon.com/policyviz.

The post Episode #236: Gabrielle Ione Hickmon appeared first on PolicyViz.

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Jen Christiansen is the author of Building Science Graphics: An Illustrated Guide to Communicating Science through Diagrams and Visualizations (CRC Press) and senior graphics editor at Scientific American, where she art directs and produces illustrated explanatory diagrams and data visualizations.

Episode NotesJen | Web | Book | Book site

Scientific American

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Welcome back to the PolicyViz podcast. I am your host, Jon Schwabish. On this week’s episode of the show, I am so excited to be joined by Jen Christiansen from Scientific American. Jen has a fantastic new book Building Science Graphics that will help anyone who’s working with data, and particularly scientists, do a better job, presenting their information, creating graphics, telling stories with their data and with their science. And what’s really, I think, fabulous about this book is she kind of walks you through the process of creating better visualizations. So it’s not sort of your basic DataViz 101 type book, but gets you into the process of creating more in-depth and better graphics; I mean, really, at the end of the day, that’s really what this book is about. So it’s a really interesting book, Jen and I have a great conversation, and so, here’s this week’s episode of the PolicyViz podcast with Jen Christiansen, author of the new book, Building Science Graphics.

Jon Schwabish: Hi, Jen, so good to see you.

Jen Christiansen: Hello. Thanks for having me back.

JS: It’s been a long time since we’ve seen each other, right, since the pandemic?

JC: Yes.

JS: Well, it’s great to see you. Congrats on this new book, Building Science Graphics, very exciting. How long did this take you to pull together?

JC: Oh gosh, let’s see, the first email from Alberto Cairo, my editor, one of my coeditors on the piece was February two years ago. So that’s when he reached out to see if I’d ever thought about writing a book, and so, that kind of kicked off the proposal process. Well, a lot of the content had been pulled together from various talks I’ve given over time, and blog posts and things, so I wasn’t completely starting from scratch. I kind of had a general idea of a few major themes I wanted to discuss.

JS: Right. But as I’m sure, like, I’ve had that too where I’m like, oh, I’ve got this all written down, it’s all in my head, I just got to get it into book form, like, yeah, no problem. But that’s where the rubber hits the road, and I want to talk to you about this a little bit in a bit, but, like, the design of the book is quite unique as well; and I want to talk about the layout, how you actually went through the whole process of building it. But before we get there, I want to just ask like a general, this is like a meta-Jen Christiansen question, which is like, what do you find special about science graphics.

JC: Well, first of all, I think science graphics – I think this is important to state upfront – science graphics are beholden to the same best practices and design principles as graphics that communicate nonscientific information. So I don’t think they’re exempt from a lot of, you know, the design best practices, but I think it’s fair to say that they may deal with some challenges more routinely than graphics about other subject matter, things like communicating complexity, visualizing uncertainty, and combating misinformation. I mean, like, those themes occur across all different kinds of graphics, but I think it happens a lot in the science realm. But I think they’re most unique in that the content they convey is rooted in a process that a lot of people aren’t familiar with. So this idea of bodies of, kind of, scientific knowledge is built like one step at a time, and it’s the self-correcting enterprise that kind of happen slowly. So conclusions that are rooted in evidence that are shown in a graphic might be true at one moment in time, but the interpretations might shift a little bit as additional evidence is collected. So you kind of need to figure out how to provide the context that your audience needs, and help them understand where this kind of fits in the larger research arc.

JS: Right. I mean, I also would suspect, and I do want to talk about this as well, for the scientist, I mean, there’s the reader side of understanding the sort of the content, but it’s also on the scientist side, right, of thinking about how to communicate to a broader audience. When they’re working with you, and maybe we should talk about this, but when they’re working with you at Scientific American, it’s not like they’re using all the jargon that they would use in the academic version.

JC: Well, I think that’s one of the reasons I wanted to write this book is because scientists are charged with developing graphics a lot, journal articles, poster presentations, like, imagery is a really common language in the form of communication that’s used in the science world for scientists to communicate with each other, and, eventually, to broader audiences, whether it’s for the scientists directly or through other designers, yet, there’s not a lot of training for scientists to learn how to do that. And so, I think that it was just kind of important to get a lot of just baseline information out there for folks who might be a little bit new to the topic.

JS: Was that your main motivation, and I want to dive into some of the more specific pieces of the book, but there’s like whole chunks of, like, the whole third part of the book, for example, is kind of like a roadmap on how to build scientific graphics for someone who does not have like a degree in design, like, was that your motivation was to help scientists just do a better job?

JC: That was one of the motivations and probably the primary motivation, because I feel like designers who are already involved in developing graphics might find certain chapters of this book a little bit familiar, but I think there’s whole sections within it that would help different kind of target audiences. But I think the folks who are going to get something out of it from page one to 300 and something, how many pages are in here, are scientists.

JS: Right. Yeah. Maybe it could help folks if you could talk a little bit about your process of working with scientists at Scientific American, like, I feel like that process maybe folks don’t realize, because you do a lot of stuff in-house and also work with a lot of freelancers in helping scientists build different types of things, so maybe you could just talk a little bit about that process, just sort of, I’m just thinking about like a scientist who’s listening this, like, oh, I would love to have my new paper on blah, blah, blah in Scientific American, like, how do I get to the point where I could work with Jen and get something great in the magazine?

JC: Well, that’s kind of a split question, because part of that is based on how we get manuscripts into the magazine to kind of determine whether or not it’s a story that we want to print. And so, that goes through, honestly, a text first kind of approach, and it gets, you know, so but once we accept manuscripts, then it comes to the point of reading them and seeing what graphics might have been included, and what kinds of visuals or ideas the author might have, but that’s also where the internal team, kind of, you know, me and my colleague, Amanda Montañez who works on news graphics, we kind of read through these manuscripts and try to figure out when a graphic would be useful to convey some of the information in the story.

So we’re kind of going through this process, and that’s what I tried to outline in the book, at a few different levels is like to get across this idea that there are several steps that you take; and just because you’re a professional designer, doesn’t mean you just like jump in and create this amazing visual, or a visual that looks good. It’s this process of determining whether or not a graphic could be useful, because they take a lot of resources, both time and money sometimes – what’s the goal of that graphic, and what are the benchmarks that you need to have in order for the whole team to come together and kind of evaluate if that graphic is successful at different stages along the way.

So in the book, I describe how I do that in a magazine setting with three primary steps where everybody’s providing feedback, that’s like a concept sketch, tight sketch final art. But there’s a flowchart and decision tree, a couple of them in this book that actually just sort of walk folks through one step at a time, just this idea of what question needs to be answered right now in order for you to take the next step towards then building a graphic. And I find things like that useful for myself as well, because sometimes, even if you’re doing these all of the time, you just sit down and you can’t get past that first step. And you to procrastinate, and you just need to get started on the project, and so, something like a decision tree, it’s like, okay, I just need to answer this question. And then, suddenly, you’re answering the next question. And then, suddenly, you’re problem-solving, and you’re getting it done. So I think it’s just kind of a useful tool for novices and professionals alike.

JS: I’m curious, when you make that, you know, get through that whole process, the manuscript is accepted, and there’s going to be a graphic, do you find that most of your work is creating a new graphic, or is it mostly taking what’s already in the academic paper and modifying it to make it more suitable for this broader audience?

JC: I think it’s about half and half. I love doing makeover graphics, because when you see that, like, a scientist that’s problem solved it, and they figured out an interesting way to convey the information in visual form, whether it’s a data visualization or explanatory diagram, that’s really exciting because you’re like, okay, they have the metaphor, or, they have this really unique way of looking at the information and they know the information better than I do. So, like, let’s roll with that, and now, let’s start to unpack it and make it more accessible to more people. Sometimes that means like, you know, making it aesthetically pleasing, but also just doing things like getting rid of jargon and in both, like, labels and in image form, like, and just start to make it more accessible.

So I love doing those, but other times, it’s like this opportunity to create something new, it’s like, okay, I can’t find a way that somebody else has successfully, like, created a graphic about this topic that feels like it would be useful in this context. So then, it’s just like really kind of walking through those steps and trying to figure out what is the goal of this graphic, and, as a metaphor useful to help explain this, what kind of graphic is it going to be, is it something that’s comparing and contrasting, is it showing change over time. So just kind of walking through and trying to figure out a good solution for that. And one of my favorite parts about that is working with other professional designers, because I’m kind of a project manager of sorts, so I’m working as a liaison between freelancers and scientists, and my text editing colleagues, so that becomes a pretty collaborative experience.

JS: Yeah. So on this process, and then, we’ll get into some specific parts, so on this process, what is it like working with a scientist creating an entirely new graphic, maybe it’s on the process of the experiment, it’s on the process of the science itself, what does that process look like, because, I’m guessing that there’s – I’m guessing here – but I’m guessing there’s a pretty large amount of education that you were doing with the scientists to say, this is why we want to have this totally new diagram about your work. And I’m curious about how they respond and how they work in that process with you.

JC: Well, it’s interesting, because although scientists are very often the author of the text itself, and so, they’re featured prominently as this is their work, the magazine does have full control over the imagery that goes with it, because we’re the ones that are then investing in the money and time to make those elements happen, and it needs to happen quickly. So there’s a lot of efficient, just sort of here’s what we think the graphics plan should be, and so, that’s being communicated either through me or through my text editing colleague. And so, it’s sort of here’s what we think would be a good idea for our audience. We’re constantly saying, we understand that these ways of showing it might be great for your peers, we’re dealing with another audience that we know pretty well, or we know better than perhaps you do; and so, this is what we think will be useful for our audience on our formats. And then, the conversation shifts a little bit towards, is this accurate, are we describing things correctly, if we’re not, can you help us understand why. So it kind of shifts the conversation from what do they think is the thing that we should be illustrating to this is where we’re thinking we should go, can you help us do that to the best of our ability.

JS: Right, I got you. Okay, so let’s shift gears a little bit, and talk about some specific parts of the book because I, for those who are listening to the show, what I – the way these work is I usually send the guests a list of questions. And what I had to do Jen for the list I send you is like, I really had to shorten it, because I had about 13 bullet points, and I had to just like – because there’s just so much great stuff in here. So I kind of picked out the things that I think may be the most helpful to people who are working in data, may not be designers, and are sort of scientists that I think for me at least were kind of the most useful part to think about design. So the first thing I want to talk about was grids, because you have an entire chapter devoted to grids, and this would sort of overlap with some work I’ve been doing in dashboard design, where I see so many people just like focusing on grids, and I wanted to ask you, what is so special about grids, what should people think about working in grids. And I have a kind of a follow-up question to all of that, which is, when I look at a lot of the graphics in the book, and in Scientific American, it kind of feels like it doesn’t work in a grid, it feels more like organic and flowing, but then, when I look at how you draw off the grid, I’m like, oh yeah, it does fit into this really nice grid structure. And so, I’m curious how you play with grids a little bit to kind of not feel kind of so rectangular and make it feel more integrated in kind of way if that makes sense. So that’s a big question to say tell us everything we need to know about designing in grids.

JC: I don’t know if I can say everything you need to know, there are entire books written on this topic.

JS: Right.

JC: I would like to start by saying that, so for folks who are unfamiliar with grids, they’re literally the same thing as those ruled loose leaf paper pages that you learned to write on as a kid. So they’re a guiding system of vertical and horizontal lines. A graph paper is another kind of design grid. More useful ones for design purposes, I think, are generally larger, like, if you think in terms of the guides that shape newspaper columns or modular website designs. So I think they’re a really useful starting point because they force you to immediately start making conscious decisions. So immediately you’re thinking, oh, should the titling caption of my graphics span one column or two, does the imagery fit into kind of modules that are side by side or on top of each other. And so, they kind of – they’re an efficient way to impose order and kind of this conscious level of visual hierarchy, you’re suddenly forced to kind of move objects around within this kind of set space. Some designers do issue them altogether, and I think you kind of picked up on this, I do love breaking the grid as much as I love starting with one.

So breaking the grid just means that you’re including elements that don’t align perfectly with those guidelines, but you’re making a conscious choice to do so for a particular reason. So, for me, I like to sometimes highlight a key annotation by putting it in a circle that kind of pops out of the edge of that column a little bit or something, and that draws attention. It sort of implies that this information is pretty critical. In general, I think just starting with a grid takes the edge off of being faced with a blank page, and they force you to think about things logically, and that helps give readers a sense of the different subsections within your graphic or page.

Now, I think they’re most important when it comes to things like aligning text and labels. I think imagery can start to kind of pop out of that, and that might have been what you were picking up with, with that kind of more organic feel of some pages – when you look at them, you don’t think, well, this just looks like a grid. So some elements can expand beyond that. But if you have your captions and your labels and your annotations aligned and very orderly, it kind of helps the reader see that hierarchy a little bit more clearly, I think.

JS: Right. So it’s really interesting the way you say, you know, you’ve got this bubble or circle as a good simple example, and it just kind of pop – breaks, like, the edge of that grid just a little bit. I mean, you see it, but I kind of feel it, when I look at some of the graphics in your book, I can really feel that that’s the thing that’s kind of different from everything else on the page, because, you know, the text is left aligned, and the title is right here. But there’s this thing in the middle, just feels a little bit different for some reason, and that’s where my eye just goes naturally.

JC: Yeah, I think it’s really useful for annotations that really call out, this is the important part, or, to help people follow, like, okay, there’s three bits that are popping out of this grid, those three things are probably related somehow.

JS: Yeah. So the other part of the book that I found really interesting was on posters, academic posters, and I’ve had a few people on the show, Mike Morrison, and Zen Faulkes, who spend a lot of time thinking about academic posters, but it was really interesting to see it in here, because it’s such a kind of niche area for scientists, and they have this, like, full reference book in front of them that they can use, and they can use it in their own work, even without, like, having something published in Scientific American, which I just love – I can just imagine scientists just picking this up, reading that, and just having that section, like, you’re marked and ready to roll. I guess, my question is pretty broad, but I just wonder about how you think about academic posters, whether you talk to scientists that you work with about their work and posters, and what is your kind of main view of making better posters for an academic conference?

JC: Sure. So for your listeners that aren’t scientists, and you kind of helped explain this a bit, but it’s really common for science conferences to have poster sessions. So it’s like basically just this hotel ballroom filled with room dividers that have large paper posters affixed to them with pins. They’re like four to five foot printouts often, and during a session, a scientist generally stands by their poster and kind of talks through people through it about their research, and they use their poster as a visual aid. So it’s very science fairesque. Attendees are milling through the space.

JS: Yeah.

JC: So it fascinates me that there’s such a common occurrence at science conferences, but I think it’s safe to say that scientists aren’t being trained to thoughtfully design them. I think they’re often given templates, but not necessarily any training or discussion of how to really make the best use of them. They often hold way too much text, the font size isn’t legible, and they’re totally lacking in breathing space, or a clear indication of kind of the flow of information. So in my book, I encourage scientists to approach a full poster as if they’re designing one huge graphic because that’s pretty much what it is. I mean, you might have a little more text than a graphic technically would, but maybe it shouldn’t. And I know there’s a lot of folks who are exploring different ways of doing posters, and some say you shouldn’t have any text at all, you should just have a QR code or others are these interactive screens that are amazing. But I’m thinking, like, what are people actually doing right now. I mean, I feel like a lot of like with your work on Excel and PowerPoint, it’s like, these are the tools, or this is the product that people are using right now. We can be aspirational about really rethinking the how that happens at all. But in this moment in time, these printed posters are what most people are using.

JS: Still what we are doing, yeah.

JC: Yeah. So I think just people need to, like, start with the grid, use negative space instead of box frames, think about what your main takeaways are, how you can use color and scale and position to bring attention to those takeaways. But most importantly, I think, just remember that all of that needs to be done at a scale that can be read and skimmed from, like, several feet away in a room with, like, dubious lighting, and there’s lots of chatter and activity going on. So like there’s a lot going on, it shouldn’t be your science paper just kind of reformatted for a wall.

JS: Yeah, dubious lighting is definitely, I’m going to hold on to that for sure. So I want to talk about process again, because, like I said, that’s like a good chunk of the book. Okay, so when it comes to posters, so here’s my question – so a scientist comes to this section on the book, they’re looking through some of the amazing graphics in here – and I’m going to show one here for the video folks; for those who are listening, it’s a graphic here, the title is a Churning Burning Star, and it’s got all these illustrations of the inside of a planet or a star with these illustrations on the side, and its motion and this and that. And so, Jen, my question to you is, a scientist goes through this book, they are excited about what they can do, but they come and they see something like this, and they say, but I don’t know how to draw this illustration or find this photograph, like, so what is your recommendation to those folks who have the science, they are excited about the process, but they don’t actually have those particular design skills? Maybe they buy into the whole grid and everything you’ve already mentioned, but they don’t know how to get to this stage of it.

JC: Right. So that illustration that you described is from science, I believe it was drawn by Chris Bickel, who is a professional illustrator, and it’s for, I think, the front section of science, so it’s kind of in their journalistic, you know, for a broad audience part of the magazine. So it’s gorgeous, but not everything needs to be rendered to that [inaudible 00:22:55]. So I just really think that folks who maybe don’t have those rendering skills right now, it’s just you really think about objects and how they’re placed on a page and how somebody moves through a space. One of the reasons graphics like that are so gorgeous is because you need to entice a reader to join in, and to investigate it. A lot of scientists [inaudible 00:23:18] done for other scientists who already want to read that paper, so you already have your audience. So now your goal is to help them understand things in a better way. So some of the goals are slightly different, depending on your audience.

That said, there are tools that help – that can be used to help things like PhyloPic, it’s an online resource of thousands of animal drawings that are for very specific species, they’re little silhouettes; and they’re, I believe, under a Creative Commons license. So there’s things that you can use, things like clipart that’s done by other people who know that it needs to be accurate. And then, there’s also just, well, you can learn to draw if you’d like, you know, there’s different books and resources that can help you along that path. Or you can hire collaborators, and, like, you might think I don’t have the budget to hire somebody to do this, but if you problem-solve out what you think how you think the information might best be presented, and then, involve a collaborator doing different parts of that whole, that might bring your prices down a little bit.

So I think folks, when they see these gorgeous three dimensional drawings or things that are award winning pieces, it’s like, just remember, well, that’s for one purpose, but you don’t need to have those skills to kind of have successful graphics for other purposes.

JS: Yeah, I think that’s such a great point that, yeah, the goal is different when you’re doing an academic poster, and the audience is different. And yeah, I think that’s just like a great point that people shouldn’t feel down, that they’re not an award winning designer and really get information out there. Okay, so I’ve mentioned this a few times already, but the last part of the book is on the creative process, and I wonder if you could just talk a little bit about, you’ve already talked a little bit about it, but maybe talked a little bit more about how you see scientists building out their own process to create more effective visuals, be it for a poster or their papers or a slide presentation, or whatever it might be, what does that process look like for them?

JC: Well, I think it starts by determining whether a graphic would be useful, figuring out what the goal of the graphic should be, where is it going to live, who is its audience, kind of, walking through those questions does help kind of define your edges and kind of help focus your thought, like, you might be envisioning this grand piece, but if it needs to be visible on a mobile phone, that grand vision isn’t going to work. So it’s just kind of creating your edges by answering a series of questions about where is it going to be, what does it need to show, and who is the audience for it. So kind of walking through those things, and then, starting to do concept sketches like doodles, just really rough exploratory doodles as you’re doing the research to kind of shift your mind from thinking in terms of words into images, and how can image retell that story in a way that might be more efficient than words. And then, as you start to kind of hone in on something that feels like it’s starting to work, then develop a more kind of fully realized concept sketch and get feedback from others, and then, just kind of narrow things down and get them more and more close to final through that process.

JS: I think the message that I’m picking up here from you is to not see something that’s in Scientific American or Science or National Geographic, as a scientist, and feel like that’s your goal. And instead, to think about what you have in front of you, your skill set, and go a little bit more slowly, just concentrate on your goals for your particular audience. And you don’t, you know, it’s almost like perfection is the enemy of the good in this case.

JC: Yeah, and I also think that when we’re developing graphics for places like Scientific American and National Geographic, it’s also the thought process that we’re putting into the first stages are very similar to what I think anybody should be doing. And I like to tell people that you need to think about getting the bones organized properly, before flushing it out, and so, if you just stop at that bones’ stage, that’s still really informative. Flushing it out might be the, like, taking it over the top in terms of aesthetics and beauty and really kind of a professional veneer. But if you can get the information organized in a useful and kind of clear way, that’s like 90% of the job in many ways. If you don’t get that right, then no matter how beautifully rendered it is, it’s not going to be useful to people.

JS: Yeah, this is what I love about the book is it does have a little bit of sort of like your best practices, there’s a section – a really nice section on color, and there’s a section on grids. But the focus of the work is not sort of the standard, like, a 101 kind of book, it’s taking us to the next level, which I think is kind of this new evolution or a next phase of DataViz, data communication books on this design, on these layouts, on the structure, on this process, which I think just opens doors for people, especially to think more creatively that you don’t have to be skilled in everything to be able to make beautiful looking projects and products.

JC: Great. That’s great to hear, because I did want it to be very inviting and useful to a wide range of people. And so, yeah, I love your takeaway on that, thanks.

JS: The last part I want to talk about was the actual production of the book, because there is, among all the other amazing things in this book, the thing that caught my eye when I just, like, first opened it is you don’t have figure numbers, it’s not like see image 2.1, it doesn’t even say see the figure below or see the figure on the next page, you have these little pointers directly from the text to the image or to the graphic or to the caption. And so I want to ask you, what was your thought process behind approaching it that way, and then, what was your production process like, because I think for anyone who’s tried to publish a book, or just a journal article for that matter, like, working with a lot of production companies is not a very easy process, so I’m curious about how you conceptualized it, and then, actually put it into practice.

JC: Yeah, so I knew if I was 1.2 ever going to write a book, I wanted to design it too, and that didn’t feel like too big of a leap because I’ve been working in magazine and textbook design for a long time, so I knew a lot of – I knew I had the InDesign skills to make that happen. And I was also really inspired by Ellen Lupton, who’s an author and designer who designs her own books and her book collaborations; and has talked about it, if you look up, she has some great YouTube videos, lectures that were really inspiring. But I wanted to design it not because I thought I’d be creating this award winning book design, because I’m not a professional book designer, there are people like Stefanie Posavec who are, who take it to the next level.

JS: Yeah, right.

JC: But I wanted to approach the book like a large graphic, and so, for that to happen, I knew that the text and the visual elements needed to evolve together. And part of that is like I didn’t want the reader to have to pause and search for things. So when I wasn’t able to kind of put a graphic right in the text where I talk about it, I simply, as you described, used the line to connect the period of the sentence that related to that image to the image itself. And so, then that line is like a bookmark, so when the reader is done looking at the image, they just follow it right back to where they left off.

So there’s not this disconnect and kind of popping around, and I’m just helping their eye go where I intended it to go next, much in the same way I would do within just a graphic itself. So in order to make that work, I wrote rough drafts in Google documents, but then I moved things over really early on into InDesign page layouts. So I actually created the design and figured out the typography of the book and everything before I had written much more than the introduction. And so, then I was writing and editing to fit, so that the images would fall on the same spread as the text that referenced them. So in that way, it kind of became like, I have this page and this page has a graphic, and there’s this much text, and what do I need to cut, there’s a lot of back and forth. And one of the reasons I wanted to prioritize that is because I got really weary of reading about design principles and perception science research results in documents that didn’t walk the talk, like, so many books, yeah, they include discussion of things like Gestalt principles of proximity, like, really kind of these foundational ideas, and then, they don’t enact those ideas in the design of the book. And I thought why are we telling people this is important, and then, we’re not showing them that it’s important.

I should say this kind of goes back to challenges in working with larger production groups. It’s amazing working with a publisher and with the great team of editors, but there are some things you can’t control then. And so, I feel like I’m being a little bit of a hypocrite in one piece about this, so I addressed the topic of accessibility in the book. But my decision to focus on creating a print design that reflected the design principles that I write about meant that I removed some flexibility in how that content appears in eBook form. And that’s because I was going from like a Bespoke print design into an automated eBook workflow. And, ideally, I could have designed the eBook separately myself as well and included responsive versions of graphics when possible. But that wasn’t an option. And so, I’m sad that I wasn’t able to walk the talk as much in the electronic version for accessibility reasons. So that was kind of the part that I was most sad about, yeah.

JS: Well, like you said, it’s an evolution, so maybe this book sets the stage to say, how do we get from here to an eBook technology that actually works in that way, because I think the book itself takes us another step forward, right, because it does integrate everything together – how can we build that in the digital technology to make that work for people who have vision or physical impairments to work in the digital world? So I can imagine why you said you’re not going to create the eBook on your own. That sounds like a pretty amazing, incredible amount of work, but I just love the overall design and how you’ve, like you said, everything is just kind of integrated together. And the cover was designed by Alli Torban.

JC: Yes, she designed the illustration on the cover, which I was very excited about, it was so great working with her. I love working with other designers to see what their process is like, and hers is really like thoughtful, a series of questions about who the audience was and the tone and everything. So it was a real treat, it was kind of a treat to myself to go ahead and commission some cover art for that yeah.

JS: Yeah, that’s great. Well, the book is great. Thank you so much for coming on the show. Good luck with shopping this around in all this time. I’m sure you’re going to have to take boxes of them to every science conference now around the country. So good luck, but congrats again. It’s great. And thanks for coming on the show.

JC: Thanks for chatting. It was a lot of fun.

Thanks for tuning in to this week’s episode of the show. I hope you enjoyed that conversation. I hope you will check out Jen’s book and I hope you will learn a lot about communicating science information. If you would like to support the show, please rate and review it on your favorite podcast provider. You can find me on YouTube at policyviz.com, and, of course, on Twitter. So until next time, this has been the PolicyViz podcast. Thanks so much for listening.

A number of people help bring you the PolicyViz podcast. Music is provided by the NRIs. Audio editing is provided by Ken Skaggs. Design and promotion is created with assistance from Sharon Sotsky Remirez. And each episode is transcribed by Jenny Transcription Services. If you’d like to help support the podcast, please share it, and review it on iTunes, Stitcher, Spotify, YouTube, or wherever you get your podcast. The PolicyViz podcast is ad free and supported by listeners. If you would like to help support the show financially, please visit our PayPal page or our Patreon page at patreon.com/policyviz.

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Kirk Munroe is a business analytics and performance management expert. He has held leadership roles in product management, marketing, sales enablement, and customer success in analytics software companies including, Cognos, IBM, Kinaxis, Tableau, and Salesforce. Kirk has a passion for coaching and mentoring people to make better decisions through storytelling with data. He is currently one of the two owners and principal consultants at Paint with Data, a visual analytics consulting firm. Kirk lives in Halifax, Nova Scotia, Canada.

Episode NotesKirk | Web | Twitter

Book: Data Modeling with Tableau: A practical guide to building data models using Tableau Prep and Tableau Desktop

Kirk Munroe: 4 Common Tableau Data Model Problems…and How to Fix Them

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Welcome back to the PolicyViz podcast. I am your host, Jon Schwabish. I hope you are well, I hope the weather is turning nice into spring. But, you are listening to a podcast, you’re probably out walking in the sun, walking your dog, your run, taking your jog, I don’t know, but I’m glad you’re here, I’m glad you have tuned in to this week’s episode of the show where we’re going to learn more about data in Tableau. I’m really excited to have on the show Kirk Munroe join me for the conversation. Kirk is the author of the new book Data Modeling with Tableau. Kirk is also one of the chiefs at Paint with Data. And here’s the thing, I was on Twitter, sort of, complaining about – I’m going to admit, I was complaining – complaining about how a lot of Tableau blog posts ignore the part about the format of your data, the structure of your data, and a lot of that is because most of those tutorials use the basic built-in datasets within Tableau. Fine, that’s great. But in many cases, my data aren’t as clean, or they’re not in the same format, the same structure. So Kirk reached out, he’s got this great blog post on the Kevin & Ken Flerlage twins blog on their website, which I will also share in the show notes. But he’s also got this new great book Data Modeling with Tableau, where I am almost certainly going to offer the first two chapters, at least, if not the full book, but, at least, the first two chapters to my students, ways to think about how to use data in Tableau. I just think this is super important, you can’t get to the visualization part without knowing anything about the data part, and particularly about the data structure part. So I hope you’ll enjoy this week’s episode of the podcast. Here’s my conversation with Kirk.

Jon Schwabish: Hey, Kirk, good morning for both of us, even though we’re like an hour apart, right?

Kirk Munroe: Yeah, good morning, Jon. Thanks for having me on, flattered to be here.

JS: I was saying earlier, now that I understand that Halifax time is an hour ahead, when it’s four o’clock Eastern, I can say, well, it’s cocktail hour in Halifax.

KM: Right, five o’clock, somewhere four.

JS: Right, now I know exactly where it’s five o’clock and four, so I feel better, yeah. Well, thanks for coming on the show. I’m excited to chat with you, because let me give listeners just a little quick background. So I was having not a struggle, I was wrestling a little bit with data formats in Tableau, and the one thing I noticed in a lot of blog posts of Tableau tutorials was that people didn’t talk about the structure of the data, they’re always using the superstore data, and it’s in this particular format, and you responded with this fantastic blog post that you had written for Kevin & Ken Flerlage, and that was really great. And then, on top of that, you have this great new book, Data Modeling with Tableau, which the first two or three chapters go into even more detail on that. So I reached out, super happy to have you on the show, but I want to start with a little bit of background. You have a firm Paint with Data, but you also have an impressive background before that, so I was hoping you could just talk a little bit about that, and then, we can dive into some Tableau stuff.

KM: Yeah, sure. And yeah, thanks, happy to be here. So yeah, my background’s in BI, at least, with, you know, going through whole history started in 2001. I went to Cognos as a product manager, and I kind of went up the product management ranks there for about five years. The reason I like being a product manager at that time was BI analytics was still a little bit nascent. So I felt like being on the product side, I have a bit of a technical background, was the place to be. Then once we got bought by IBM, I went into sales enablement, because I thought the natural next step of that was to help sellers, customer facing people actually understand what analytics was, like, it’s not just reports that you can present it to people that have static information, like, what actual analytics meant and answering business questions.

And then, I went to a supply chain analytics company called Connexus, and ran product marketing there with really super deep analytics, like, but very niche. So we could do cool things, like, if you had a bill of materials for say, like, I don’t know, two different laptop models, and you could ask a question of it and say, well, what if we took this part from this one and gave it to this one, and you can see how many customer orders would be laid within like seconds, which would normally take overnight to run in an MRP system or something. And then, anyway, I did a start-up again, then I went to Tableau actually for four years to do customer success, because I was drawn to that, because I thought, you know, this stage we’ve gotten to is that people didn’t understand –they understood the analytics at a high level, but they didn’t know how to make it happen. I did that for four years, and then, on the payment data thing, my wife actually who’s a Tableau ambassador, a User Group ambassador, started a company called Paint with Data to be a consultancy. And, of course, we used to take data and actually paint with it. So we were a consultancy that worked with companies of all kinds of different sizes, I joined 18 months ago. Was it intersection of two things really, one was we’d always said we wanted to work together, and then, we kind of went, well, just run them [inaudible 00:06:23].

JS: Right, yeah.

KM: And part of it was just the customer success role was good at Tableau, it certainly got a little bit frustrated that I couldn’t get hands on, like, the things that really make customers successful, like, the job became a little bit too much Relationship, as opposed to the things that actually make people like as their data source as an example, right? And the role got really far removed from that kind of stuff, like, if they didn’t care that new hires are trained in Tableau really are certainly at that kind of level, right? Like, I would go get every Tableau certification I could get, I mean, I thought it was really important to – if you’re going to be a trusted advisor, you got to know more than people your advice and conceptual.

JS: Right, yeah. Okay, so you joined 18 months ago. So were you working on the book.

KM: No, so the book, it was kind of a funny thing, because I joined, and then, I was in for maybe six months, and then, the publisher called me and went, we want someone to write a book on data modelling, will you write this book – in Tableau. And the first thing I thought was, well, who am I to write a book, and, of course, you get into, there’s so many smarter people than me that could do this, which is just a natural thing to do, I think. And I thought, and I think in my head, I went, there’s so many Viz books being written all the time, there must be a lot of data modeling books. So I asked for 24 hours, and I did a search, and there are none. So, to be fair, Carl Allchin is awesome, like, Carl’s got a book on Tableau Prep, but not on data modeling, kind of, the stack for Tableau.

So I went, well, then you quickly – this has happened to me a lot in my life – I go from why me to why not me. And I started writing the book in about April, I guess, so it took about six months from April to October to write the book. And then, I thought it would be a 200-page book, and trying to be as concise as I can possibly be. I got it down to like, it turned out to be 325 pages in the end, like, there are probably more there than I talk about.

JS: Right. Yeah, I’ve been there. Okay, so let’s talk about data. I preface this whole conversation with this difference kind of between wide data and tall data, but maybe I’ll ask the question in sort of a more general way, like, from your perspective, what is the biggest challenge, in particular, I guess, new Tableau users face when it comes to the data modeling and the data structure in Tableau?

KM: Yeah, to do one step back from that, which I’ll get to it a little bit in the book, and I think we’ll probably have more blog posts on this is what makes Tableau so special that no one sees actually is SQL, which Tableau used to talk about. So until Tableau came along you would have to query your data, and then, you have to format your data a little bit, and then, you would visualize your data. So, in most other BI tools, I think ThoughtSpot have done a pretty good job, the same kind of approach as Tableau now, but traditionally, what it is, is you open up a product, and the first thing it will ask you is, how do you want to chart this data.

And my frustration till I saw Tableau, you know, I was working with Cognos so we are one of the frustrations at the time, was like, I don’t know how I want to visualize it, yeah, I’m just trying to interact with my data. So Tableau solved that problem, and what it is, is, so basically, when they write their SQL underneath the scenes, they have this clause appended on the end that basically goes display as, which is why people fight Tableau sometimes and find it unintuitive at the very start. But if you get in, Tableau talks about – used to talk about, at least, the analytics flow, you just start clicking around and ask and answer questions and dragging things to [inaudible 00:10:02] and the Viz keeps changing without you explicitly going drop this on columns, right, one color on, it just – it’s smart enough to mostly know. Like, I never drag anything to – I rarely drag anything to a column or row shelf as an example, I’m a big double clicker.

JS: Okay.

KM: And I let Tableau to figure that out, like, I’ve done in my top rows and columns, but I’m rarely dragging.

JS: You’re rarely dragging, okay.

KM: That background is important, I think, because Tableau always assumes, and this is where the data modeling comes in, is that your data structure underneath has a series of columns that it’s going to convert to feel, and every one of those columns is going to be distinct. So not discrete which we get, but distinct in that if it says customer, the only thing in that field is going to be customer. And if it says revenue, the only thing that’s going to be in there is revenue, sales, etc., and that’s why a lot of people pick up superstore that’s already formatted that way, and they have a lot of fun, and they bring their own data, they token it off what’s going on. And the reason Tableau does that is it makes this SQL thing easy to do, it makes it easy to know how to visualize it, because they know how the data is structured. I guess, they assume how the data is structured.

JS: They assume, right.

KM: Right. So that’s why you can run into problems. And we don’t need to be super technical. The way CPUs work, like, have always worked is they work well when you pass them an array of data. And then, you can filter, slice, whatever that – and they’re really good at aggregating it. So analysis at the end of the day is about some level of aggregation of data, visual analytics which Tableau does is that visually. So basically, it’s really easy if you have a row called revenue, and then, you pull on region, it’s easy for Tableau to go, okay, some, and then, break it down by that region, right? And then, colored by subcategory, it’d be ugly, but it would know by subcategory. It does this stuff very fast if the data is structured that way.

If, for instance, your field was conditional, and it was called name, and the next column was vendor or customer, and then, there was a name, and the next column had vendor customer, and you were to try to dynamically write a calculation in Tableau to go, if this column equals vendor, then this one, Tableau is going to be terrible. Like, it’s just awful, right? Because it assumes that struck, which I knew, as soon as I went to Tableau I – we had to do a demo at a time to get hired. I refused to use superstore because no data looks like this. I’ve been around long enough that I knew that, so I brought in a bunch of Airbnb data, or inside Airbnb data, you know, just great data, but to show. And then, I made a whole scenario about if you’re working for the city, how happy would you be with this, or your potential host, and I did a whole demo around that. And then, I realized, oh, this, like, formatting data thing is tricky, like, you have to get this right.

JS: Yeah.

KM: So yeah, that’s it – so that’s fundamentally it’s 325 pages of how do you get your data like that, and because there’s a lot of nuance, they need that.

JS: So I know you’re not at Tableau anymore, so this is just dreaming, but, like, if you had your druthers, would you have Tableau focus there, presumably, they have an AI and a ML team working on a variety of things, would you have them do something similar to the show me tab, but not for graphs, but for data where it says it looks like your data in this structure, would you want in this structure, and you click a couple of buttons and you’re good to go.

KM: Yeah, for sure. And maybe, on the – what’s the new feature called – the workbook optimizer, it could be more than just – it should be able to go the – it’s getting there a little bit, but it definitely has to, I think, get better at going. The reason that you have all these calculations and weird parameters and whatever is because your data is not shaped, right?

JS: Right.

KM: I think it would be not terribly hard for them to pick it up, I know, like, Ken Flerlage has a great line, which I love, which is, if you’re doing something in Tableau, and it seems like it’s more difficult than it should be, it’s probably because you’re data [inaudible 00:14:11] shaped right. You can just, like, this is more complicated than it should be, like, nine times out of 10, that’s because your data is not shaped.

JS: Your data is not shaped right. So for those who are, let’s say, like me, sort of, relatively Tableau newbies, what do you recommend for folks to do when they’re in that position where they’re struggling, and maybe they even realize, oh, my data’s wide, and it needs to be long or tall, like, what tools do you use to do that reshaping when you’re working there.

KM: Yeah, well, I mean, first, they should buy the book.

JS: Obviously, yeah.

KM: I think it’s just that the first exercise is whether you use a piece of paper or you mentally do it or whatever, just like if you were going to, I know, Chantilly talks about this a lot is not the only one who talks about if you’re going to create a visualization, you should kind of map it on a piece of paper somewhere first to see. You should think about what would it take before I even get to a tool, what would it take to get these fields into these very distinct colors.

JS: Right.

KM: And then, the talk would be sometime, and most of the time, what it’s all it’s going to take is, if it’s not, it’s pivoting rows to columns, or pivoting columns to rows. And it’s that simple, usually, and then, you can pivot columns to rows in either Desktop or Prep, and you can pivot rows to columns in Prep. I feel like that’s a feature that’s been in there now for, at least, a couple of years that almost no one sees…

JS: No one knows about.

KM: Even the Prep community knows it’s there, it’s a slickest little feature, and it just makes the world of difference, because when you open up a workbook, and you see if people have those type of calculations, I was talking about, like, if this field equals profit, then profit, and then, they’ve got a string field to put dollar signs in front of it. I’m like, just reshape that feeling.

JS: Yeah.

KM: Just reshape it…

JS: [inaudible 00:16:10] and add a dollar sign, yeah, I’ve done all that too, yeah.

KM: Yeah, and, in Prep, it’s literally pivot rows to columns, AND it’s like, which one do you want to – and then, you drag two things over, and it’s done. I mean, like Prep, like, Prep is terribly underrated tool.

JS: So let’s talk about Prep, because you have, I think it’s like part two of the book, I think there’s four or five chapters that are dedicated to using Prep. So is that section in there, because you feel it is the right tool for Tableau users, or, because there really isn’t, like, in my reading, there’s just not enough, I would say, enough materials out there to really help you dive into it.

KM: Yeah, sometimes I wonder a lot if Prep, like a lot of features come out. So it’s probably a few things like Alteryx had this great partnership with Tableau, right? So a lot of people probably knew that, right? And if you’re already licensed for that, then you could substitute everything in Prep. That’s fine. But I think for people that don’t have it, especially, I think a couple of things happen. First off, when Prep came out, it didn’t do a lot, it was okay, but it didn’t do a lot. It was still kind of a cool product back in 2018 now. And I think a lot of people might have evaluated it then and written it off a little bit, and not kept up with all the innovation that’s in it. But certainly, it’s a really valuable tool for people who already know Tableau well, because why? First off, it comes with the creator license. I mean, there’s the annoying thing to schedule, you need data management, but it comes with it. It’s the same calculation language. It’s the same UI in, as far as you could have the same UI and UX, like, they’re different UX because they’re different processes. So, I mean, from a cost of ownership, it’s just so long to use Prep, like, I wish more people use…

I’ll tell you, we talked about this before we started a little bit, like, so I’ve been in data for 22 years, and I probably haven’t, other than a very simple line of SQL, probably haven’t written a line in 18 or something. Even though people insist on asking for the SQL skills, I’ll give you an idea of how much I avoid it. Lately, I’ve been using Snowflake. All right, so technically, I’m writing a SQL statement to create my Snowflake table, but it’s not really a SQL statement. So I create my table, and then, what I do is, Relationships are also a very powerful thing in Tableau, but they only work against live connections. So you can’t use Prep per se. But the Prep I know so well now, and it’s so familiar that I usually create my Snowflake tables, and I Prep the data and load it into Snowflake with Prep. Like I don’t even – I always use a published data source. Like, I’ll go into Prep, and then, do what I need to do to it, and I’ll move it to a Snowflake table, because, let’s say, I’ve got two tables at different levels of aggregation, and I don’t want to explode those, I’ll put them into two Snowflake tables, and then, I’ll get Tableau to create a Relationship.

JS: Oh wow.

KM: So I don’t even always use it, a published data source. So sometimes I’m using something Snowflake to actually as the output, so you don’t even have to think about it’s always been the last thing on is I don’t know why Tableau has been so hesitant to call it an ETL tool, because it’s what it’s always been, like, you extract data, you transform it, and you load it somewhere else. I think they were afraid to do it, because they’re like, well, we don’t want to compete against all these, like, ETL specific…

JS: Yeah.

KM: You don’t have to say that’s what you. You only mean, like, I would – if I was going to some data engineering team that never did visual analytics, I would not recommend Tableau Prep, just because there’s probably more powerful tools. And for someone who’s used to Tableau anyway, like, why not, like, the cost of ownership…

JS: It is an interesting thought about who they target, like, what is their avatar of their core customer base, and I always find that interesting, because the folks that I work with tend to be, you know, it’s a nice nonprofit of six, eight, 12 people, and there’s like one person or two people who have demonstrated this interest in creating visualizations, but they’re not necessarily maybe data people, they haven’t – they don’t have coding experience, maybe they’ve never used a tool like this before. And I think they often get, as you’ve mentioned, they get frustrated by these little but crucial things, and maybe being able to help those folks would unleash it, I don’t really know, but…

KM: Yeah, you know what, that’s a great point, for consulting with small companies, I would say, it’s worth learning data at the level of Tableau Prep first, especially for the nontraditional technical people who don’t come from a programming background, because not only is your Viz going to be easier and faster, they’re actually not going to have to write nearly as many calculations and struggle with that kind of stuff in Tableau. Because otherwise, they’re going to get frustrated, because, they’re like, I’m not a coder, and I have to write all this, and, like, you would and if the data were structured.

JS: Right.

KM: And this idea of, like, every column being distinct is not that hard of a concept again, I don’t know why people don’t go back to make it that way. I just neatly need it in a column. It’s just, you need pretty good SQL skills before this pivot rows to columns. I think that’s the secret feature Tableau has, because it’s so fast, and it’s a little bit hard to do in SQL, like, it would be daunting to try to split that up. And it’s counterintuitive, because it makes your data longer, and people think, long data is slow. I’m like, long data is not slow.

JS: No, right. Yeah, it is interesting, I mean, I grew up in the SaaS Data World, and there is, like, a burn into my brain, there’s a little image in the state of helpfile about the reshape command, which goes from, they use long and wide is what the language they use, but there’s this, like, there’s this image, it’s like, here’s a wide, and then, it’s like a little arrow, and then, here’s the long and, you know, if you want to go left to right, this is the syntax here, right, the left, this is the syntax. And I agree, it’s not a complicated concept to get, but it’s so crucial to everything that you’re going to do down the road.

KM: Right.

JS: Okay, so almost without intentionally doing it, we’ve talked about the first two parts of the book, so I think it makes sense, we should get to it, so let me get to the last part. So the first part is really about the types of data model setting up your data. The second part is about Prep, and then, the third part is about connecting and building Relationships, which I have found also to be a frustrating, especially, the Relationship part, it’s a frustrating piece. So I’ll just make it a super general question, which is, as we walk through now, through the book, and sort of the process by which someone would work in Tableau, what is this third part about when it comes to connecting and building Relationships in the data?

KM: Yeah, so the next thing becomes almost, I wish there’s a term for this, like, it’s treating tables instead of tables as distinct analytic units that sometimes need to be combined to perform a different level of analysis. So I also, like, you referenced the blog post that I had on the Ken and Kevin site, on the flerlagetwins.com. We have two more coming, one on when to use Prep and when to use Desktop, and then, another one on when to use Relationships versus Joins, and just a little bit on blends, because the blends answers almost never. So there’s just one use case, just one very specific use case.

So imagine this, this will probably be in the blog post, but imagine this Airbnb data, I think we can do it, right? So you’ve got, let’s say, we have five tables, right? And the five tables are one table contains a list of all the properties, say, in a city or whatever, it could be all of them, but with a city column, but like, let’s say, even for a city, and then, we’ve got reviews. Right? And so, reviews are at a different level of granularity than the properties, because one property can have many reviews, but a review can only be for one property.

So you don’t until Relationships came out, that’s a perfect example of tables, you don’t want to join, and the reason you don’t want to join them is you’re going to explode it, and then, you’re going to have to watch your level of aggregation, because you’re going to have many rows now for individual properties, because it had many reviews. So what’s just magical about Relationships, is you create a Relationship on those on property ID, listing ID, and now what happens, and, I mean, by individual units of analysis is I can ask questions about reviews without asking about properties, and I can ask about properties without asking reviews. So if I ask a question on either side of that, Tableau is only going to generate the SQL behind the scenes against that one table, like, no join, I won’t even look to join it. But then, what’s really smart about Relationships, if, let’s say, I want reviews on a given property, Tableau is smart enough to do, like, you know, that would probably be a right Join the way I’m describing it, it doesn’t matter, but it would create that join dynamically to answer your question, and handle the level of aggregation, so you don’t get it, because I think, especially for nontechnical users, like, understanding levels of aggregation, that’s like really hard to wrap your head around. And then, Tableau takes your mind away from that. So imagine those two takes.

Next thing, you want to do is, I’m going to bring in neighborhood information, right? So I want to bring in a shapefile of neighborhoods so I can map it, and then, I want to bring in the walk score and bike score or whatever, I can go get that off the internet. And maybe, I want to go get apartment information on how expensive apartments are by neighborhood. So I could answer the question, does it look like Airbnb you’re driving up the price of apartments, or, it’s a tricky question, that’s part of analysis, or, am I helping people afford it, because they can use Airbnb to help them offset. But those three – those could be three separate tables, three, yeah, one with the shapefile, one with the walk scores, and one with the cost of. So the temptation would be, oh, I should – and you could do this, but it would be a little bit complicated, you could bring all those in those relationships, because that would be the default, but if you think about it, those three tables are all about neighborhood information, and they’re all at the same level of granularity, which is one row per neighborhood.

JS: Right.

KM: So what I would do is, and this is why Relationships and Joins go together really well, I would join those three tables together, and then, I would create a Relationship, and those three tables join together, because Tableau would benefit actively three Join tables as one table, because it really should be one, but don’t we having to do a whole data engineering job to put those in one in the background. And then, I can ask questions just about neighborhoods or again, like, an ask neighborhood, how many reviews per neighborhood, how many listings per neighborhood or whatever. But it’s just an example of, if you think about it, in terms of is it a unit of analysis on its own, one is neighborhood, one is reviews, one is listing, as opposed to tables, and what their level of granularity is, that also takes the complications out of that a little bit.

JS: It’s also interesting from the perspective back to this single person in some small organization, back to they need to pull all these data together, maybe not even for visualization purpose, they just need to have these data together for whatever it is, and they could use Tableau to do that, because it’s so efficient at doing these different things.

KM: Yeah, a 100%, so that, and I still think in even a big org – some little org, you have no data engineering, right?

JS: Right.

KM: My experience, at least, maybe people have seen other things, the one place where I still see waterfall process heavy, slow things are data engineering teams. Like, Cloud Data Warehouses didn’t magically solve that problem, so sometimes, you’d be like, I need my data shaped this way, and they’ll be like, well, it’s got to go through this, you need to get this approval, it’s going to cost this much of a chargeback, and we’ll have it to you in three months. It’s like, well, you know what, I’ll take the data you’ve already given me, without going all the way back to source, and later in the data pipeline, I’ll clean it up. Right? So I talk a lot about, in a completely idealistic world, you wouldn’t want the analysts doing this stuff, but you have to, but you’re never going to get answers out to the organization, if you wait for the data engineering to do it or whatever, that would throw us back into the eight, and like, argh.

JS: The last part of the book, which, admittedly, I haven’t read, because it isn’t as useful for my use case…

KM: Yeah.

JS: Everybody’s got limitations. But the last part is on Tableau Server and Tableau Online, so what are the differences in that section versus, say, working in Desktop?

KM: Yeah, so the last sections cover, and I wish there was a word for Tableau Server and Online together, because they’re synonymous, and so I keep going Tableau Server or Cloud, and it’s like, the exact same, for all intents and purposes. There’s basically, that you can think of in three different ways, the book’s not exactly organized this. One is all about data security and data security at two levels, so one is who can see the data model.

And the next level is who can see data within that data model, because so imagine the first one, you just want only the finance team to see it, how do you make sure that only the finance team can see it, the next example of who can see it is like imagine you were giving this out to your customers, and you had 1000s of them, you would want to build 1000s of workbooks, you would want to say only the customer login can only see their own data and produce one workbook, so that row level security, so part of it’s about that.

So part of it’s about how do you secure these data models, and how do you secure the data within them. Part of it’s on distribution, so when to use published data sources versus embedded data sources. Another thing, lots of people have been using Tableau, including Tableau Server or Online Cloud for a long time, and don’t know the difference between an online versus an embedded data source and when to use one versus the other, and it’s important to get that right from a cost of ownership thing, because you could be rebuilding the same data model over and over again on one side; or the flipside is, you could be publishing a data model that was really only intended for one workbook, and people are trying to build workbooks on it, and a lot of them do, because someone’s very specific for the work, so they both have their place.

And Tableau doesn’t make it very explicit, although, at least, now, on Tableau Cloud, Tableau Server, you can say new published data source, so they at least have taken to that a Desktop, and now it feels like a distinct thing. And then, the last part is around the other things that come with Tableau data management, so how to schedule Prep flows, how to have data quality warnings and Tableau data catalog at lineage to see who else is using this. So there’s only one of 15 chapters on data management, but one of them covers all the things in data.

JS: Right. So before we wrap up, I want to come back to one of the things we were talking about before we actually started recording, which is the size of data, because you mentioned a couple of times when we were chatting, but you had mentioned a very interesting piece of extracting data in Tableau that I don’t think I even really recognize, because when I do the extract, I just do extract and good and publish, and I’m good to go. But I wanted to finish up with that data extract tip, because I think this is something that probably most people don’t know about the sort of way that you can modify or option in that extract menu.

KM: Yeah, and the background for this, I think I heard recently, I hope this is a true set, but that the average Salesforce deployment as an example has a 100 custom fields in it. I’m sure some people go, a 1000, I don’t know. But what happens is, anyway, that’s the kind of thing that leads to really wide data, even if all the fields are discrete, sometimes the data still gets incredibly wide with all these ways you could slice and dice the data. And that wide data definitely makes Tableau slow, especially, if they’re string fields, because they’re usually string descriptive type fields. And so, we’re working with clients, you often get, well, the business might want to analyze by all those different things. Right? Like, anyone could possibly fill for…

JS: [inaudible 00:32:16]

KM: Yeah, so, let’s say, whether using a published or embedded data source, but say you’re just using Tableau Desktop to make it easy. And then, what I say to people all the time, then, if you don’t know which of those columns you’re going to do, like, leave him in your data model, this is if you’re using an extract, leave him in your data model, build all your Vizs, and as your very last step, before you run your extract, just take the little down arrow, you make calculations and everything else, and go hide all on it. And it will hide all the fields you’re not using, and then, what most people don’t know is when you run your extract, it doesn’t bring all that data into your extract. So it’s going to perform way better, and no one’s using it, and then, people come back and go, well, what if I want to use those in the future, well, what’s slick is Tableau will bring in almost like a ghost field, where you can say show hidden fields, you just go to the field you want, you add it, of course, you can add to the visit at that point, because the data is not there, but you run the extract again, and then, you can add it.

JS: You could do it.

KM: So this is the surefire way to make sure that you’re not bringing in data from a width perspective that you’re not using. So it’s, again, I don’t think Tableau talks about it much, it’s just a terribly hidden feature, pun intended..

JS: No, it’s just like… Yeah, it’s just like this thing that kind of showed up that, like, oh, you have to do this, and that’s correct too.

KM: Yeah, it’s super.

JS: Yeah. Well, Kirk, thanks so much for coming on the show, the book is really great, the lessons are fantastic, and something I think more people need to learn and read about. So thanks so much for coming on the show, I really appreciate it.

KM: Well, thank you Jon, yeah, and I really enjoyed it.

Thanks for tuning in to this week’s episode of the show. I hope you enjoyed that interview, I hope you will go check out the blog posts we mentioned, we talked about, and, also, of course, Kirk’s new book, Data Modeling with Tableau, and, of course, his website, Paint with Data, a lot of good content there, lot of great ways to think about all the things that you need to be better, I would say, obviously, in Tableau, but also just a better person to work with data, just a better data visualizer, a better data scientist, a better statistician, anyone who works with data really needs to understand this content, and these lessons even better. So I hope you enjoyed that episode. I hope you will consider sharing today’s episode with your friends and your family. Put it on your social media networks, share a review or a rating on your favorite podcast provider. And until next week, this has been the PolicyViz podcast, thanks so much for listening.

A whole team helps bring you the PolicyViz podcast. Intro and outro music is provided by the NRIs, a band based here in Northern Virginia. Audio editing is provided by Ken Skaggs. Design and promotion is created with assistance from Sharon Sotsky Remirez. And each episode is transcribed by Jenny Transcription Services. If you’d like to help support the podcast, please share and review it on iTunes, Stitcher, Spotify, YouTube, or wherever you get your podcast. The PolicyViz podcast is ad free and supported by listeners. But if you would like to help support the show financially, please visit our Winno app, PayPal page or Patreon page, all linked and available at policyviz.com.

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Ellie Balk is an artist obsessed with color, pattern, data and mathematics. She creates large scale data visualization public artworks using paint, glass, sound and most recently ceramics. Community engagement and interaction is at the core of her work.

Ellie lives in Brooklyn, while working internationally. Her public artwork can be experienced across the United State, extensively throughout New York City and St. Louis, Missouri and Internationally in Buenos Aires, Argentina, Mae Rim, Thailand in Saint Louis, Senegal and Marrakech, Morocco.

Ellie has worked with High schools students across the United States in creating public art that visualizes mathematics and her ideas have been adapted for use in elementary and high school mathematics curriculum. Her work developed with her teaching partner Tricia Stanley (Brooklyn) in Visualizing Mathematics has been published nationally and internationally through the Bridges Conference (Sweden) and the National Council of Teachers of Mathematics (Connecticut, Chicago, New Orleans).

She loves when she can use data as a tool to bring people together. Her visualization workshops have strengthened groups with the Kemper Museum (St. Louis), teams within Google (New York), KOC school (Istanbul, Turkey) and with the National Academy of Design (New York).

Ellie holds a Bachelors of Fine Art from Bowling Green State University in Ohio and a Masters of Fine Art from Pratt Institute.

Episode NotesEllie | Website | Instagram | Twitter

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Episode #2: Dear Data

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Welcome back to the PolicyViz podcast. I am your host, Jon Schwabish. On this week’s episode of the show, I am very excited to talk with Ellie Balk about her work. Who is Ellie Balk you might ask? Well, you’re about to find out, obviously, when you’re going to listen to the show. But Ellie is an artist working in the confluence, the intersection of data and a variety of different mediums or media – I don’t know what the right plural world is here, let’s just stick with mediums. She works on pianos, she works with stained glass, she works with murals, ceilings, glass. It is amazing. Her work is fantastic. There is a link to her website in the show notes that you should definitely check out, and she takes data into the community and works with the community, which we know is so important, not just to accurately represent the data that we’re working on, but also so that the community will embrace and use our visualizations. So Ellie is working in this public space, something that I’m really excited about to see, and to hear more about her work and her background. So I hope you’ll enjoy this week’s episode of the show. Here’s my conversation with Ellie Balk.

Jon Schwabish: Hey Ellie, good morning.

Ellie Balk: Good morning.

JS: How are you?

EB: I’m good.

JS: Welcome to the show. I’m really excited to chat with you. I don’t often get to chat with artists. It’s usually data geeks and nerds and people steeped in their computer, and so, I’m excited to chat with somebody who has like stained glass right behind you, which is like, pop of color on the show. Is that a color wheel on the door? I just noticed the color wheel on the door.

EB: Yeah.

JS: Yeah, that’s pretty great. I always want to do something with the doors in my office, and I never know what to put on the back of them, so maybe a color wheel will be a good like burst of color.

EB: Yeah.

JS: So I saw your work on Twitter, really excited to chat about what you do, how you’re inspired, and how you combine the data with the art. So maybe we could start with a little bit of your background, talk about how you got to where you are, and how you sort of pull from these two – I don’t want to say polar opposites, but pretty different areas of art and data.

EB: Yeah. Well, thanks for having me, I’m excited. And I’ve never been on the podcast before, so I’m a little nervous, that’s okay.

JS: Well, the dozens of listeners will be very excited.

EB: So I didn’t have a drive to make art. When I was a kid, I didn’t take art classes. I didn’t even take art classes in high school. But when I was in high school, I met this art therapist, and it really kind of opened my eyes into making in order to heal. And so, that’s kind of where my background was, and I went to school at Bowling Green State University, and I originally went for art therapy in undergraduate program. But when I got there, it was like they were grandfathering it out, and the teacher was horrible. The program, the book was like this pink book and it had a duck on it, it was paperback. And it was just like the first class we just read, like the entire introduction to the book, and I was like, this is not going to work for me.

So I dropped the therapy part and just focused on the art part, and was able to do a study abroad program through Bowling Green, which was great, and I went to Italy for a year, and that’s when I was first studying aesthetics, and I learned about Walter Benjamin and The Work of Art in the Age of Mechanical Reproduction, and that’s all about the art and authenticity of a work when you see it as an original. So I had taken an anthology art history class, so I get to Italy, and I’m seeing all this work that I just studied. We’re seeing it come off the page in real life, and I had a really powerful experience. But then also, I’m seeing all this artwork behind scaffolding and being restored, and then, seeing the work of Giacometti, like, pristine and bright, and it’s like, this is not what it’s supposed to be. And as a normal 20-year-old learning something for the first time I was enraged, you know. I’m like, this isn’t right, we must take art off the pedestal.

JS: Right.

EB: But really that is, kind of, the seed of the work that I’m making now. I really wanted to make work that was interactive that you could touch. I started making 2D sculptures. When I was in Italy, I had a lot of really big ideas, and a lot of, I felt really seen there by my professors, and they gave me a studio. They were like, okay, let’s just put you an advanced painting or whatever, I’m giving you studio, but I wasn’t painting. But as soon as I got in there, I started painting. So I didn’t have a formal education, because when I got back to Bowling Green, I was kind of like, in advanced painting classes, so I got kind of fast tracked through that, and just really started a lot of collaborative work there.

JS: So when you were doing your studies, what type of painting were you doing? Was it abstract art?

EB: Well, when I first started painting, I was like, playing with anything that wasn’t a paintbrush. So it was really about like the physicality of it, doing a lot of impasto and painting with the palette knife. But, for me, I mean, I was doing oil painting, and that’s when I really realized that I had, kind of, an innate relationship with color. There was something, I could visualize a color and be able to mix it all the time. And so, my colors really like came through painting and oil painting specifically, which I haven’t done in 20 years.

JS: I got you. So before we get into the data piece, so a lot of your work, and a link to your website on the show notes, so people can – for folks who don’t know, there’s a big portfolio of images of your art. A lot of it is stained glass, so I’m curious, where did you add that skill into your tool.

EB: Like, a year ago.

JS: Wow.

EB: Yeah, now, the stained glass is like pretty new. I did a couple projects using stained glass with my private teaching partner in Brooklyn, and we worked with high school students, and I did a couple of stained glass projects with the high school students. But as far as my work, it’s only been in like the last year.

JS: Wow, that’s pretty cool.

EB: It’s a fun meeting, and I’ve been super into it.

JS: Yeah. So you go to Italy, you have the awakening, the art awakening, so where does the data piece come in?

EB: So I mean, I think it was kind of always there. I mean, I think when I went to grad school, so after Bowling Green, I took some time, then I moved to New York, I went to Pratt and got my Master’s in Fine Art there and I was doing a lot of the collaborative stuff, but I was really – looking back on it now, it seems really fresh. But when I was making it, it was like, I was just making work about things that were happening to me every day. So it was data visualization. But when you’re in the moment making work about the moment, it has no value.

JS: Right.

EB: So I kind of got rid of everything, and now I’m looking back, and I remember this one painting I made for my niece’s second birthday that I was missing, and she’s 20 now, and, like, how cool it would be to have this – it was really fresh. But in the moment I was working on sound objects, and so, the data kind of started there, or made it seem like it wasn’t so far off. I was doing a lot of collaborative work, my thesis show was like throwing a party, and I had people clocking in and out, so it was like collecting [inaudible 00:09:54] people roles, and they had to wear and made them crowns, and it was so fun. And we played games, and I was collecting data in that. But my first public art piece, which is called You Are Here, which is on the corner of Vanderbilt and DeKalb, like, where the neighborhoods of Clinton Hill and Fort Greene come together, I made this in 2008, and this is when there was just a lot of gentrification, a lot of change happening in the neighborhood. We had this flea market come in, the Brooklyn Flea, and it brought in droves of people from other neighborhoods. And from Manhattan, they’re looking around, like, oh, this is nice.

So I saw a lot of my neighbors moving out things that were cool about that, and I missed my neighbors, but they had bought those houses for $20-30,000, and they’re selling them for $4 million. They’re leaving a legacy for their family. But the neighborhood was just changing so much. I mean, the faces were changing, and the vibe was changing, so I wanted to create a piece that really celebrated home. So I decided to do a circle map, and I took out all the street name – actually, I have a tattoo over here.

JS: Wow, oh yeah, okay.

EB: I took out all the street names, and then, I invited people to come and put a dot where they lived. And what was interesting is that it really started to get people to talk about where they live, and it broke down all these barriers when people were connecting. But for me, it created my first data visualization, and I was looking at it, and really thinking about, like, how far people go to get to that spot, and really what defines community, what defines your area, because I live farther away, but I have to travel farther to get to that spot. And so, my community is bigger than [inaudible 00:11:59] across the street.

JS: Yeah.

EB: So it was really interesting, and that really started me doing data visualization.

JS: Yeah. So then, I’m curious then, when it comes to your work now, what is the process like, I mean, so, like, there’s one on here that I was trying to dig into, it’s called what’s your number, and it looks like a pedestrian walkway, like an elevated pedestrian walkway for commuters.

EB: Yeah.

JS: Yeah. So when you are commissioned to do a work, does the client say, here’s the data we want you to use, or do they just say, we want something data – what is that process like? And then, I just want to know how you actually get up onto the bottom of this elevated walkway to do the work.

EB: Well, really it’s different every time, but I do feel really lucky that most of the time people come to me and say we want something, and then we discuss what that something is going to be. So usually, starting with a specific space, like, we want something for this space, yeah, and then, I’m able to kind of go from there.

JS: So they say we have this huge, I don’t know, side of our building or area in our inside, and it’s this huge wall, why do they want data rather than just a piece of abstract art, like, what is that like, yeah?

EB: Well, a lot of people do just want a piece of abstract art or they want murals of people’s faces. It’s funny, I was telling a friend recently, I kind of stopped applying for things a few years ago, because I think that people have a very traditional idea of what a mural is. And so, unless I’m invited to something, it’s like, I don’t usually get it if I apply for some sort of mural, because if they don’t have this idea already about engaging community in this way or creating something that’s this abstract or this involved them.

JS: Yeah. So, I guess, more about the process, I’m curious…

EB: Yeah.

JS: So, let’s say, you go in and you have this conversation, and, I don’t know, I guess, coming from the data side, I’m curious, do you go in and find some data, and then, do you – I’m guessing you do a whole bunch of sketches before you actually build something, and then, like, so how does…

EB: Yeah.

JS: Yeah, so what’s that data to final product?

EB: Okay, so, I usually look at the space first, and sometimes the inspiration comes really fast in the space, and I can see it. You know, it can go two different ways. I don’t usually like when I see at first, because then I have to find the data to fit within this vision that I am seeing. So it’s much better for me, if I can just learn the limitations of the space. I’m very inspired by limitations. So I know that it has to be out of glass, or it has to be painted, and we have to take over the whole area, or, like, these are the goals or whatever that is. And then, lately, what I like the most is doing like a survey. So I’ll kind of talk with whoever’s paying for it, you know, like, what do we want to talk about, or if I am given free range, like, what – I did a project for Spotify that was really one of my favorite projects, and they really gave me free range, and I was able to just be inspired by the space. And I was really thinking a lot about color and sound, and so, I made a piece that tried to create a chromatic definition of sound. It was completely arbitrary and amazing. It was so much fun to make.

So in that case, I created a framework for the data where I made a list of words that describe both color and sound. And then, I attributed a specific color to each word, and those colors came directly from their color deck. So all the colors that Spotify uses, so I was able to relate all of that. So one thing that’s really important to me is that the work really fits in the space that it’s in. So for that one, it was important to use kind of their color palette [inaudible 00:16:27] color palette, which was nice, because it really does match the space. So then I took those colors and those sounds and I ended up working with this guy, Glenn McDonald, who is like, they call him the data alchemist of Spotify. He is the one who creates the algorithms for the Discover Weekly.

JS: Okay, all right.

EB: [inaudible 00:16:46] obsessively. He’s super cool. So I got to work with him, and he’s working on this other project, where he is creating genre, sub genres, and I don’t know where it’s at now. But when we were working together, the genres were like, 3000-4000 different genres. So I worked with him to create a specific song that would go with each color. We started with genre, and then we picked a song, and we worked on a playlist together, and then, we sent it out to Spotify employees, where they then attributed the color to the word, and then, a color to the sound. So the visualization was basically, like, how many people agree with me.

JS: Right.

EB: [inaudible 00:17:31].

JS: Right, you crowdsourced the association.

EB: Yeah. So it was really, really interesting. And then, so I created the mural, ended up being on the ceiling, and then, it bent over onto the wall. So it kind of like, the axis went straight down the middle, and then, the sound data was on one side, and the word data was on the other. And so, it was super complicated, and I sometimes wonder, do people ever really get it. Like, I look back on them sometimes, and I’m even confused, and I made it. But I think there’s something interesting about that.

JS: Yeah. Well, it is interesting, back to your point about community, right, like, if you are a Spotify employee, and you got this survey, or whatever it was, every day you walk in to the office, and you see this mural, it reminds you, I mean, can remind you of that experience of this data collection.

EB: Yeah, well, it’s yours.

JS: Yeah.

EB: And I think that that’s something I learned really early on, I was doing a teaching artist residency, and I was showing slides of this collaborative mural, I used to do collaborative murals with different organizations and stuff. And this girl was like, oh, I made that. And I was like, yeah, you did, like, that’s yours. And so, when someone paints a wall, it becomes theirs. When they’re involved in a project, it’s theirs. So I really love any opportunity that I can use this work to really bring people together.

JS: It’s really interesting, there’s a whole discussion in the research world about trying to understand people, and having them own a bit of the research in terms of not on the science of it, but on the outcomes, right, that there’s a recommendation, and that’s how you really connect with people. But to data, I’m curious about how you think about sort of engagement and inspiration. I mean, obviously, your work is going to be different than someone working in a Excel dashboard or an R, you know, a map built in R. But do you think about your work as being inspiring or being engaging or telling stories, or it’s just all of those things sort of wrapped together? And it feels like it’s very like, you trying to make it personal for people who are viewing it?

EB: Yeah, I mean, I hope it’s all the things wrapped together. I hope that there is an experience, and I want the work to stand on its own, as being just something really beautiful and interesting to look at, and then there’s that second layer to it, of it really telling a story. Yeah, so I want it to be all of it.

JS: Yeah. So I wanted to go back a little bit, so what is the in-between point, so, let’s say, the Spotify project, it’s kind of a perfect example, so you’ve actually collected all the data.

EB: Yeah.

JS: So then, before you go paint it on the wall, what is your process like – are you drawing a million different drafts, but are you working on the computer too, what is that process like?

EB: Yeah, I mean, this is – so I’m not in – I have no background in mathematics or data, like, I was not a good student in school. So a lot of stuff sometimes, it’s like, everything is a little bit backwards I think. But I do start with like, a spreadsheet. My best friend’s like a whiz with Excel, so she’ll often help me sort things and do all the crazy Excel stuff, if I want to see something in a certain way, so she’ll help me clean up the data. But it’s difficult, because, if I have an idea, and I want to see something, it’ll take me eight hours to visualize and to create the system and I’m doing most of my stuff in Illustrator, but I’m doing it all, you know, I figured out all the math for how a unit of 10, like, what’s the size of each unit, and how am I going to upscale that. And then I’ll do something, and then, I’m like, oh, I hate it, you know. And then another one and another one. So the design process for me is pretty slow, it’s great when it happens fast, but it just, it doesn’t usually.

JS: Yeah. Have you ever worked through that process and gotten to the point where you were maybe satisfied with something, and then, realized it doesn’t quite work in the space?

EB: Well, I think I’m always thinking about the space first. Well, that’s creating all my limitations, and that’s very important. All the work is made for that space that is…

JS: Right.

EB: Yeah.

JS: Okay, so then, when it comes to putting in the final piece, this is like me having no idea how this works, for the Spotify piece, I have this vision of you like Michelangelo on the scaffolding, painting on the ceiling. Is that how it’s working?

EB: Yeah. So that was my second ceiling piece, and I was like, I’m never doing this. But I did it again. Yeah, my friend Ali Meyer helped me, and we had scaffolding that was at six feet, and we stood on the scaffolding, and I think the most difficult – I’m going to say difficult, but it was also the most fun, I get really into the math, but in like A Beautiful Mind, sort of way.

JS: Yeah.

EB: I love getting, like, I just finished this piece in Morocco, I made a giant sundial, and I was like, for an entire week, I was just beautiful minding out, like, I was just like, and I had no idea what I’m doing. But it was, you know, the math on the Spotify piece was so complicated, and the measurements were so complicated. So I got one of those electric rulers that you could go to like them, because I do get really into the data, and it becomes super important that I just – you can’t fudge it, because if you fudge this one, then it’s all of that is off. And when I’m upscaling from a piece of paper to this, it’s like I can’t – I get really into it. But it’s often when I’m working with other people, because I often work with assistants or work with students, and I love when there’s the shift for everyone, where a color becomes a number, or we start speaking in this other language about making this piece, and so, that’s kind of, I don’t know, that’s like a little piece.

JS: Yeah, that’s really interesting, yeah. I mean, the upscaling is really interesting too, right? When you’re in the digital world, like most data visualization folks are, you just change the number of pixels on the dashboard, and you just go, and you don’t have to really think about paper to wall.

EB: Yeah.

JS: Or even desktop to mobile because the platform is going to kind of do it for you.

EB: Yeah.

JS: So do you have a favorite medium to work with or work on? I mean, I imagine, the Spotify ceiling is like drywall, but then, you’ve got a lot of outdoor pieces that are presumably not.

EB: Yeah.

JS: Do you have like a favorite that you like to work with?

EB: No, I mean, I was painting mostly murals for a long time, and I think I’m really excited about kind of breaking out of that. And since breaking out of that, I just finished a project, a mural project, and I brought a friend on with the project, and I was like, man, I haven’t painted in so long. I mean, it felt – but there was something like I felt really good about it. Painting is really, it’s easy. I mean, like, it’s not easy-easy, but it’s a lot easier than stained glass or ceramic, or whatever else I’ve been up to. But yeah, I mean, I’m really happy to be breaking out of it, and learning new things. I mean, before last year, I didn’t know how to do caming with stained glass. I never worked in ceramics before, and then, I got to make these two huge pieces, so I’m pretty excited about that.

JS: Yeah. The last thing I want to ask you about is color, you touched on it earlier. I mean, the one thing that really, I mean, literally jumps off the page, well, not literally, because that’d be scary, but really jumps off the page on your portfolio is you use a lot of bright color in your work.

EB: Yeah.

JS: And I’m not sure I really have a well formed question on that, but just maybe it’s – what is your thought process when it comes to using color, because in the sort of digital DataViz world, it’s always a challenge, and color is so important when it comes to making your graphs, but what is your – again, I don’t really have a well formed question here, but what do you think about when you’re working on all these colors?

EB: Yeah, I think we touched on it a little bit, and I think that the color – I really want the pieces to belong to a space, so color is really important in that. I can spend a lot of time, like, a lot of time making a palette, but I also love to go to a space and pick up the color from that space. I did a project over the pandemic with the residents at Gouveneur Hospital, and it was really powerful, and they’re so proud about their neighborhood, and they weren’t able to leave the facility at all, so I went around and I took pictures, and then, brought them back, and then, we picked the palette from there. So it was taking the colors directly from the neighborhood, and color matching it.

JS: And picked the colors with them.

EB: Yeah.

JS: Yeah. Okay, so I definitely see this theme of community and connections in all of your work.

EB: Yeah, and so anytime, you know, I’ve been really lucky to do a handful of residencies. And so, when I go, you know, I’ll just go around and start collecting color from things that I find or things that I see. Usually, I do that through physical objects, which I think is really interesting to like paint chips, and so, that really informs the color as well. But sometimes it’s intuitive too – I remember I did this project in 2011, it was called Soundwaves, and the color palette was just pretty intuitive. And [inaudible 00:28:24] starting out too. So I just made the color palette. And then, I painted this huge, a 160-feet under the BQE, in Brooklyn, and I painted it, and then, I looked around, and I realized that that building was the same color as [inaudible 00:28:42] that building which showed up in the mural, and I was like okay, I just – I wish I would have paid more attention for it, that would be have been fun. But I’m glad that it just came in anyway, so it ended up like seamlessly fitting into that space, so that was lucky.

JS: Yeah, that’s really cool, but it is a good reminder, I think to, again, back to the Tableau, Excel, D3 folks of the world, like, there are ways to pull colors not just randomly.

EB: Yeah, well, I mean, I think that that’s – I mean, it’s a huge challenge in creating a palette like I did that series of visualized pie murals and sound projects. And with pie, it was difficult, because I would create a palette that every color needs to look good next to each color, no matter where it shows up. So really thinking about tone, and so, I use paint chips when I [inaudible 00:29:33] so I’ve collected paint chips from every paint company, I just go and collect one by one. But it’s nice, because I can kind of spread that all out, and I can move stuff around and make sure that it’s going to work. I mean, if I’m working with a set of data where I know exactly how things are going to show up, I can be much more controlled like the stained glass project. But that’s different too, because of glass which is so exciting and why I’m very excited about it with the color. But I don’t know what that piece is going to look like until the entire thing is installed. Like, this last piece that I did – cat on my lap.

JS: We’re pro pet on the show.

EB: But yeah, I mean, that was a huge project, and I’m looking – you see that palette is actually right there behind. So I could kind of see what it was going to look like, but you put it up and then there’s a tree behind it or it’s not sunny, and then, all the color changes. So I’ve been thinking a lot about that, and I think I really want to play with layering color with glass, cause that could be really interesting.

JS: Yeah, and the color can change over the course of the day as the sun moves, yeah, that’s really interesting. You kind of have almost limited control, there’s the cat.

EB: It’s in front of the mic now.

JS: Yeah. Well, Ellie, this has been great. I’m big fan of the work, appreciate you coming on the show. And yeah, sharing all the process, and hopefully, folks can pull some out of this. For those who are more digitally minded, they can use some of this in their own work.

EB: Yeah, well, if anybody wants to collaborate and teach me how to use the technical tools, we can do that.

JS: Yeah, that’d be awesome, yeah, for those who are listening or watching, I put Ellie’s site and Instagram and email on the show notes so you can reach out if you want to be Ellie’s Excel go-to person.

EB: Yeah.

JS: Cool, Ellie, thanks so much for coming on the show. I appreciate it.

EB: Thank you.

Thanks to everyone for tuning into this week’s episode of the show. I hope you enjoyed that conversation, I really do. Hope you’ll check out Ellie’s website, check out some of her art, check out there’s some really great high resolution photographs over there of her different installations in her different projects. And I hope you’ll check out all the great things that she’s working on, on her Twitter feed as well. So thanks so much for tuning into this week’s episode of the show, lots going on, on the policyviz.com website from blogs and other podcasts and different resources, so please do check them out. And until next time, this has been the PolicyViz podcast, thanks so much for listening.

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Stefanie Posavec is a designer, artist, and author whose practice focuses on finding new, experimental approaches to communicating data and information. This work has been exhibited internationally at major galleries including the V&A, the Design Museum, Somerset House, and the Wellcome Collection (London), the Centre Pompidou (Paris), and MoMA (New York). Her work is also in the permanent collection of MoMA. Besides her new book with Miriam, she has also co-authored two books that emphasise a more personal approach to data: Dear Data and the journal Observe, Collect, Draw!

Sonja Kuijpers runs STUDIO TERP, her one-woman data illustration studio based in Eindhoven, Netherlands. She designs (data-)visualisations for a diversity of clients such as Scientific American, Philips, as well as small institutions, companies, and publishers. Recently the Climate Book by Greta Thunberg was published, for which Sonja (re-)designed the graphs.

Experimenting with shapes and styles, she also designs her own independent dataviz and data art projects. She received an Information is Beautiful Gold Award in 2019 for her personal project ‘A View on despair’. Creating data visualisation, to Sonja, is trying to locate herself in the data, making sense of numbers with a human approach, showing insights as well as the aesthetics of information and data.

Related EpisodesEpisode #187: Stefanie Posavec & Miriam Quick
Episode #2: Dear Data

Now, most studies, at least, what I’ve seen here, are less than an hour. They pay over $60 for an hour worth of work. Some studies are more of the focus group, the one-on-one conversations, and those pay even more money, up to several $100. So there’s some opportunity here not only to help shape the future of technology, but also to earn some money for your time. So if you’re ready to earn extra income for sharing your expert opinion on software development, engineer hardware/software, head over to userinterviews.com/hello to sign up and participate today.

PolicyViz Podcast Episode #232: Stefanie Posavec and Sonja Kuijpers.

Welcome back to the PolicyViz podcast. I am your host, Jon Schwabish. I hope the New Year is off to a good start for you and your family and your friends and your work. I am really excited for the beginning of the New Year, really great podcast episode a couple of weeks ago, if you didn’t check it out, you really should with Vidya Setlur and Bridget Cogley on their new book Functional Aesthetics. I’ve got some great guests coming up in the next few weeks, really excited about those. I’ve got a new newsletter that’s over on Substack now, because review, well, have shut down. I’ve been working on a variety of different longer blog posts, some bigger think pieces, I’m learning more about Tableau, and I just took an R course, I’m really trying to level up my skills this year, it’s an ongoing quest to just be better at the work that we do.

When it comes to this week’s episode of the podcast, really excited for my guests. I have two guests this week, Stefanie Posavec, who you may know from the Dear Data project and has appeared on the podcast in the past; and Sonja Kuijpers, who has her own freelance studio in Europe; they teamed up to create and design Greta Thunberg’s new book on climate change. And when I saw that come out, and their involvement in the project, I was like, I’ve got to have them on the show, I’ve got to learn more about it, how did it all work, how did it all come together, what were all the challenges. And you’re going to hear some interesting stories, in particular, where you should get the book or you should buy it in the UK, or in the US, so make sure you listen to that part, because it is actually kind of important. And it’s just a really fascinating story about how all this came together, and what it takes to create a book like this with so many different graphs about a really important topic of our time, perhaps the most important topic of our time. So without a delay, here is this week’s episode, my conversation with Stefanie and Sonja.

Jon Schwabish: Hey Stefanie and Sonja, welcome to the show. How are you both? Good to see you.

Stefanie Posavec: Good.

Sonja Kuijpers: Fine, yeah.

JS: Good to see you. Sonja, how are you?

SP: Nice to see you as well.

SK: Yeah.

JS: Haven’t seen you guys, I haven’t seen – well, Sonja, I haven’t seen you in what, should we say, like, four-five years?

SK: Yeah, about that.

JS: Or 10 years in COVID time.

SK: Just about 10, yeah, the COVID, yes, in between we’ll say 10, yeah.

JS: Yeah.

SK: It was five, I guess, yeah.

JS: Yeah, it was Information is Beautiful Awards in London where I think Stefanie you and I had done a workshop that week, and I was like, hanging out for a while, did the IAB awards, and yeah, that was a good time.

SK: Yeah, I met your mother.

SP: Yeah.

JS: That’s right, you met my mom there, yeah. My mom has been a frequent topic of conversation on the show recently, I don’t know why. So hopefully, she’ll like this too.

SK: A very lovely person, yeah.

JS: Well, thanks. So just for folks who are wondering, who are listening, like, what is going on, my mom does like to travel with me, back in the pre-COVID days. So London, she also joined me in Pamplona for [inaudible 00:04:29] so yeah, Miami a couple times, yeah, she’s a good traveler. So mom, if you’re listening, you’re fun to travel with. Okay, so let’s get back on track. So you both have this amazing new project out with Greta Thunberg on climate change, the designers of the book, and so, I thought we could just talk about it for a bit. So maybe we can start with this, just so folks kind of know who you are, just like little quick introduction, and then, we could talk about the project. So maybe, Sonja, do you want to start and give folks a little background?

SK: Yeah, I’m Sonja Kuijpers. People know me probably better as STUDIO TERP, that’s my studio. I work as a one woman company, and I DataViz, data art projects, mainly DataViz for clients and more data art projects are my own, of own personal interest. And yeah, like you said, we met in London, that’s where I received an award for my View On Despair project, which was a data visualization on suicide numbers. So that’s the sort of stuff that I create on personal basis. And I like to do more of projects that have an extra twist, yeah, experiment with shapes and colors, yeah.

JS: Yeah. And Stefanie and I go way back, of course.

SP: Yes, definitely.

JS: Definitely.

SP: But, I guess, I should say…

JS: I mean, say a little bit, yeah, just a little bit. I mean, yeah, maybe, because you’ve been on the show a few times, but maybe tell folks what you’ve been doing in the last year or so. You have some cool projects going on too.

SP: Yeah. So, I guess, I say, I’m a designer artist and author, I work with data, mainly experimental data design projects, and then I also teach workshops as well. So some of the stuff that I’ve been doing over the past year or so includes, you know, I do a lot of art residencies, like, it could mean drawing how data and samples come from a study participants all the way through to the data researchers who end up using that data to study medical and health outcomes, like, an art residency I did with a research group People Like You, or it could be making a participatory artwork for the Wellcome Collection from visitors’ perceptions of happiness and what makes them happy, to publishing a book with Miriam Quick, I Am a Book. I Am a Portal to the Universe, which we’ve definitely talked about, and which is a book that uses itself as a measure to kind of show the wonder of the world to you. So like every part of the book, from its volume, its weight, its page thickness, and more communicate data about our world. And yeah, I guess, that’s it. So yeah, I’m doing a lot of, using data in more participatory, playful, friendly, accessible ways to connect people together, and kind of to have them think about themselves and their lives and their place in their wider communities.

JS: Right. And so, I could see, just, I mean, knowing your work, but also just from the way you both describe your work, I can see how you both naturally could work well together, because you both work in that experimental, different kinds of shapes, not necessarily like your standard like, not that there’s anything wrong with it, but like the dashboarding world, you’re both trying different forms and different things. So I can see why this could be a pretty amazing partnership. So I’m curious about the book. I don’t have it yet. It hasn’t made its way across the pond, so I’m still waiting. So I thought we could start with how did it come about, and then, what was the work like? So Stefanie, maybe you want to start, and then, we could talk about how all the piece together?

SP: Yeah, I’ll give some background on the project. So occasionally, I will design a book for Penguin, it has to be a very special book, because in my job – previous to working in DataViz, I was a book cover and book designer. So I was asked maybe six or seven months before the project truly began, if I would like to design a book for Greta Thunberg, and so I said, yes, and I had to keep it a secret. But that was working under the assumption that I would do the text and the charts, because they know this is my realm. But then, when it came to it, it’s for her book, The Climate Book, which has, I think, over 100 of the top climate contributors alongside Greta, so it’s like a really big logistically complex book with lots of different people involved, and lots of charts, and a very, very quick turnaround.

And so, it was very obvious that that was not a one person job, it needed two people, and so, that’s when I asked Sonja to see if she would join the project and oversee the charts in the book, and I will hand it over to her.

JS: So how did that work, so Sonja, you get this call, did you just get a mass of data and graph like drafts, and you just went to work, like, what was the process of pulling all that together? Because it sounds like it’s across multiple authors, so, like, how does – yeah, maybe just talk about your process?

SK: Yeah, well, first, I want to share that Stefanie reached out, and I had to do a dance in my living room because, you know, Stefanie asking me to join her in this marvelous journey. And so, that was the first thing that got me very excited about taking this job, because – I’m going to say it again, Stefanie, you’re one of my heroes in DataViz. So yeah, that was…

SP: That’s very nice of you.

SK: Yeah.

SP: With this project, you were totally. You are more of a hero to me, I promise you. This is a very, very challenging…

SK: We are each other’s heroes now. We don’t need [inaudible 00:11:13]

JS: Yeah.

SK: No, but I didn’t exactly know what was coming my way at the beginning. It was just, yeah, there’s this book, and I was told it was by Greta, so I was also like, okay, wow, Greta. But the actual amount and size wasn’t that clear yet at that point. But I think the real thing hit me when we had this Excel file with all the graphs that we would have to incorporate. So yeah, we had this big file with the older graphs mentioned, picture of the JPEG or whatever image they provided, and they already had some other team work on Illustrator files of that JPEGs. So that’s when I realized, okay, I have to really pull harder on my graphs’ knowledge here.

JS: So I’m guessing that most of the authors, again, I don’t have the book yet, but I’m guessing most of the authors are scientists, climate scientists, maybe some advocates, so I’m guessing the data is pretty dense; the graphs, based on at least the economics field, the graphs aren’t great – so were you primarily trying to make them look better, or were you reimagining some of them? I’m guessing, like, hey, here’s a line chart, but could you make something different sort of more engaging, like, where were you thinking as you started going through it?

SK: That would have been great, but the time, it didn’t allow to really broaden anything. I think there was some small changes, considering which direction a bar would go, would it be vertical or horizontal, things like that, but not really in shapes or other ways of handling the graph, because there just wasn’t enough time to actually – we didn’t have the data available also, so there were just the pictures and the underlying Illustrator files.

JS: And so, then, how does the design part of this work, because there’s the design of the graphs, and the design of the book, so how did you two work together to do both of those pieces?

SK: Yeah, Stefanie is the lead hero, so yeah.

SP: Sure. Actually, I just want to interject one little thing, just to give you a sense of what Sonja was dealing with.

JS: Yeah.

SP: So it was like a 100, I mean, not everyone had a chart, but it’s like a 100 contributors who are pulling charts from everywhere. So it could have been using some scientific software or charts that they really wanted to use that were pulled from, I don’t know like the Washington Post…

JS: Some random source, right.

SP: Or some random source or from, like, the IPCC report, or it was just a JPEG, or it was like from PowerPoint, there’s all sorts of stuff, all like JPEGs, often not editable, and that’s why they all had to go – when Sonja was talking about Illustrator files – they all had to go and be artwork and redrawn and made editable. So like they started from like, not, like, really, really rough quality stuff, and all sorts of places, all sorts of charts, then that, like, those Illustrator files is what Sonja had to work with. So it was like a huge – it was a huge old mess. She’s [inaudible 00:14:57].

JS: Yeah, from an author’s perspective, I can imagine it being amazing, because you could just be like, I want a map, okay, I’ll take a screenshot of this thing from the New York Times, I’ll take this bar chart from the Journal of blah, blah, blah, and I’ll take this map from over here, and just send it to you two to let you go off, like, I can imagine for the author, it’s terrific, but from your perspective, that’s a huge undertaking.

SP: Yeah. So just to, I mean, I guess, you used the publishing before the listener, like, there was an art director, and then, there was the editor, there was also a team in the US that I think was overseeing it as well, it was like a joint publication in some capacity. And then, I think also you’ve got an image rights team that is checking, I guess, all the rights for everything is okay. I think that also included some of the charts that some people wanted to use from various newspapers. I mean, there was also an editorial assistant who was doing a lot of the heavy lifting, like, liaising with all of the different authors, that was I think, Sonja’s [inaudible 00:16:03] kind of like liaison with all those, like a hundred different people. So it was a super complicated thing to figure out, and then, manage all these charts.

But just to give you a sense of the way that worked with the book design, so I came up with a book design, and it’s never like, oh, here’s a design, let’s go with it. It’s probably like constant, constant, constant iterations, and back and forth, and back and forth, which then was sent to Greta, and, I guess, her team, and then they approve it. We get all the text and the content, and then, we start to drop it in design from there. But what I had, like, there’s a blue that goes through the book, a pantone blue, and some other colors, so I had to choose these overarching colors to be harmonious with this blue pantone ink that feeds through the book.

And then, also the typography, so I had to give that to Sonja, as well as guidelines for how she should set up the chart to fit into my layout grid to ensure that everything would align and be harmonious with the rest of the text. So I was just sending Sonja really annoying, but I hope, useful, being like, oh, it needs – you’ve got two sizes of box, you can fit in this – it has to be this size or this size, and things had to be fit into very, very precise sizes. Also to ensure that everything would fit in the 464-page book. So it’s like a precision process. Also, the page size changed.

JS: Oh no!

SP: And I don’t know, there were a lot of changes.

JS: Wow.

SK: Yeah. Can I jump in here because you were saying annoying, but, to me, it was very helpful, because I had all these graphs, and this was a very helpful way of working, because I had this framework that was there, and I didn’t have to think of all other stuff also. So it wasn’t – it really wasn’t the annoying, it was the opposite, yeah.

JS: But were there times, or were there examples, or are there examples of graphs where you’re in this box, and you want to fit something in, either an annotation or another data element, and you just couldn’t, or, can you give us an example of, like, where the size or the layout you had to do something different because of the actual, like, how it’s going to fit on the page?

SK: I think there was one map that we rotated. Remember Stefanie, it was the legend wouldn’t fit in if we – or the map itself wasn’t even clear if we put it in this box size. So I think we rotated the map on a page.

SP: But you could still read it.

SK: Yeah.

SP: You would still read it normally, but then the map was…

SK: Yeah.

SP: Because I think it was from – because it was the North Pole, so it didn’t matter which way it was.

JS: That’s so funny, yeah, it doesn’t matter, right, because it’s the North Pole, right.

SP: Yeah, that’s the only time that you can really do that, yeah.

JS: So then, when it comes to – I’m interested on the typography and the text, in particular, because I’m guessing that a lot of the graphs were pretty scientific, like, a lot of climate change journals. So how, and, I guess, this is first a question for Sonja, how did you think about making the text readable for non-scientists, and were you responsible for changing some of the words, and how did you think about annotating things, so normal readers could read, understand it?

SK: I think it was mainly the abbreviations that were used that I changed here and there, because, we all know carbon, carbon symbols and things like that, but there were some that contains abbreviations and things. I had to look up myself, and I’m thinking, if I have to look it up, imagine what other people have to look it up. So yeah, these were things that are checks and double checks, and I think there were a lot that text that guided the axis. I think there were a lot that were changed by me, because I thought, why use an abbreviation when you can write it out.

JS: Right. So it sounds like there was, I mean, sounds like there’s a big team around this whole project, but did you, either of you have conversations with the different authors, and like get into the weeds of things, or was it always this kind of level of separation, and was that good or bad?

SK: Yeah.

SP: Sonja you were sometimes – you had not direct contact, but you were able to query.

SK: No, so I had – there was Amandeep at Penguin who checked in with all the scientists, I guess. So if I had a question, I would write it down saying in this and this graph, what does this mean. Or, for instance, one had an axis which had years on it, jumping every 20 years, but then there was one jumping, yeah, a 100 years, and then, 20, 20, 20. I was like, yeah, but you can’t do that, you have to actually show, yeah, we all know that, we’ve all been there. So these were questions that I asked, can I change this, or this is how I look at this, what about if I add this or take away this.

JS: Right. So now you both do client work, and so, I’m curious, when you’re doing client work, let’s go away from the book for a second, when you’re doing client work in a similar sort of situation where you have, I don’t know, your project officer or the person at the client who you’re talking to, but they are pulling material from other folks at their companies, do you like to talk to the individual analysts or the individual people, like, so, I guess I’m asking like, is this buffer person, like, do you prefer to have someone like that, or do you prefer to actually talk to the people who are actually knee deep in the data or neck deep in the data, I guess, probably?

SK: I’ll start. Yeah, of course, I would like to have a conversation with the ones that collect the data or, yeah, they know what’s in there, and what they want to communicate, and I can check if it’s there, and I can double check with them. If there were mistakes, because sometimes there are mistakes in there, an outlier doesn’t have to be an outlier, it can be a typo. Right? So yeah, that’s what I like to do with my clients, but this was a, yeah, there was a whole different situation. And, I guess, in this particular job, it was better to have this person in between because I think they would have been a project of two years or, yeah.

JS: Yeah.

SP: I would say that they should have had another person, like, I think the project would have done with one extra person to manage the hundred people, like, because, I mean, it was incredibly complex. And Sonja’s main contact, Amandeep was incredible, but it was such a huge project, because there were image rights, there were the charts, there were texts corrections, editor corrections, subeditor corrections for hundred different people including Greta and her team, like, it was 464 pages, so it was like this huge, huge book.

JS: Right. Yeah, I guess, just the management of this kind of project seems pretty amazing. So let me ask, you’ve both done work that clearly has meaning for you as either professionally or personally. And I’m curious when you have a project like this, which potentially is has such a big impact, it is clearly so important to our lives and our kids’ lives and the future of the planet, does it have extra meaning for you when you’re working through it, like, I know there’s always drudgery in every project, but do you feel or when you’re able to take a step back for a second, do you feel like this is the kind of thing that you are excited to be in the field for? I guess, maybe, Sonja, you can start, I don’t know.

SK: Yeah, well, I try to stay away from the subject matter, because, yeah, it’s gloomy, and it’s doom. And so, I just focused really hard on the job just to make it insightful, and pretty as well, because that’s always my goal. But yeah, of course, as a subject itself, climate is really a thing, and as you mentioned, for my kids, I’m really proud that I had this opportunity to work on such an important book. And yeah, how Greta has spoken before on it, I think is truly amazing how these people continue fighting. And I am not one of these persons myself, but I am truly honored if I can help those people out by my work, yeah.

JS: Stefanie, what about you? I mean, you’ve done work with kids, and you’ve done all these different types of projects, but when you have a project like this, does it mean something extra?

SP: Yeah, I mean, I think there’s some – I think the thing that’s really nice about a project like this is that, you know, I think as a designer, it’s always nice to do a project that has some sort of longevity and utility to it, like, I mean, that’s the beautiful thing about a book, like a really lovely book will stick around for decades, people will keep it on their shelves. And that’s so different from, or, like, be considered like this canonic sort of piece of literature that changes things. So to be part of that, since paper lasts a lot longer than stuff on the internet, like, I think that is useful, but as a reference I think it’s a really nice design project. I mean, that’s why I love publishing, in general, even if it is a [inaudible 00:27:29].

JS: Yeah. Okay, so I’m going to ask this question, I think I already know the answer, but I’m guessing people want to know whether you got to actually meet or talk to Greta, or just her team – that’s for people who are listening, Stefanie and Sonja are both shaking their heads with sad faces that they didn’t actually get to.

SP: Yeah.

SK: Yeah, maybe just do a shout out to her, because, I would really love to have an autograph in my book.

JS: Right, yeah, absolutely.

SP: Yeah, it was all through the editor. So yeah, there was a little bit of a buffer, and also, I’m pausing, I mean, because I think one of her main advisors or an advisor is also her father. So I think they may work on it, like, yeah, like, he’s a strong part of her team. I think I’m allowed to say that. That’s probably…

JS: Yeah, I mean, I’ve read that.

SP: That’s common knowledge, okay.

JS: Yeah.

SP: I just, you know, in politics, I don’t know what I am allowed to say.

JS: Yeah, right.

SP: But then, they were in contact with the editor.

JS: So that was going to be my last question, but you said something that I wanted, this will be my last question, so clearly, for this project data and graphs are an important part of the storytelling, but lots of books, people just throw a graph in there, and they don’t do all of this work to think about laying it out correctly, or making them look consistent. And so, I want to ask whether you think data visualization, at least, in the publishing world, I guess, we’ll just stick within the publishing world, would you think data visualization is now at a point where it’s almost a necessity or a requirement to have graphs and charts and diagrams that look really good throughout and look consistent across the book, rather than I think the way a lot of books have graphs are just kind of thrown in there, like, it is a screenshot from the Washington Post or wherever, it’s just sort of thrown in there. But do you think it’s come to a point where there is more emphasis and a greater requirement for better graphs in books?

SP: I mean, just speaking from my experience, well, I would say, yes, and I will let Sonja expand upon it, but I’m just going to use the example of, you know, this was for Penguin Random House, this book, but also published by Penguin Press in the UK, so they do a lot of the nonfiction, and they do a lot of nonfiction science writing, and they also, I think, they have like a – they may have an Allen Lane is their nonfiction imprint under this, like, how it’s styled, and through that, I believe that they also have people who are making all sorts of charts consistent within that text design style. So I think Penguin is always really well known for their design, and I think that falls under, you know, the look of the chart, the styling of the chart, making sure that it’s consistent with everything else is just as important, and I’ll hand it over to Sonja.

SK: Yeah, it’s not that I do these things often, so I’m not sure if I can say something about it. But I would love to see more of it, I’m not sure, because which kind of books are you referring to – I’ve seen things passed by on scientific publications, and then, I’m still not very worried on the status of… And I know they’re willing, but yeah, there’s still lots of steps to make there. And yeah, I hope they see this book, and that they can see that it’s possible.

JS: I like that, that’s a good way to end, so it’s the doom and gloom of the subject matter, but maybe the presentation will inspire some people, so hopefully, we’ll get some of that. Well, congrats on the book, it looks amazing. I can’t wait to get it in my hands, and congrats to you both. I hope, Sonja, especially, I hope you get your signed version from Greta, I hope it shows up in the mail. And yeah, thanks to you both for coming on the show, I really appreciate it.

SK: Yes, thanks for the invite.

SP: Thank you.

And thanks for tuning in to this week’s episode of the show, I hope you enjoyed that. I hope you’ll check out both Stefanie’s work and Sonja’s work, and I hope you will check out the new book from Greta Thunberg. And I hope you will check out the policyviz.com blog for more tutorials and lessons on data visualization. I hope you’ll check out my YouTube channel. If you want to support the show, if you want to support it financially, you can head over to Winno where I have a text messaging app service where I’ll send out data visualization tips and tricks every week for a small monthly fee, for like a dollar a month, you can get DataViz tips and tricks to your phone. If you want to share the podcast with your friends, your family, your coworkers, rate and review it on your favorite podcast provider. And if you’d like to rate or review my book, Better Data Visualizations on Amazon, I’d really appreciate that, trying to get over the hump of the poor binding that occurred in a couple of printings that have sort of affected the stars on Amazon. So if you want to go over and give a give a good boost to it, I’d appreciate that. So until next time, this has been the PolicyViz podcast, thanks so much for listening.

The post Episode #232: Stefanie Posavec and Sonja Kuijpers appeared first on PolicyViz.

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Lilach Manheim Laurio leads the Data Experience Center of Excellence at Visa, where she helps data practitioners across the company to elevate the quality of their data products, and improve their skills in data visualization and data experience design. Lilach’s data visualization work blends together a background in art history, library science, and human-centered information design, along with a passion for visual metaphor and pun.

Lilach has served as a Tableau Zen master (2018-2019), Tableau Public featured author, and co-organizer of her local Tableau user group chapter. She has contributed as guest author to the Tableau blog and the Nightingale journal, writing about design and user experience in data visualization. She has also spoken on topics ranging from visual metaphor to dataviz critique at Tableau conferences and user groups across the U.S.

Lilach holds a Bachelor degree in Art History and a Master of Library and Information Science (MLIS).

Episode NotesLilach | Web | Twitter | Tableau Public

Visa Chart Components
Elevating Data Experiences framework
Chris DeMartini (Twitter)
Frank Elavsky (Twitter)
Data Visualization SocietyThe Shape Parameter of a Two-Variable Graph (banking to 45 degrees paper from Cleveland, McGill, and McGill)

Related blog posts:

PolicyViz: A Better Path Toward Criticizing Data Visualization
PolicyViz: Should we give awards for data visualizations?
PolicyViz: Critiquing a Data Visualization Critique
Fernanda Viégas and Martin Wattenberg: Design and Redesign in Data Visualization

Books

Functional Aesthetics for Data Visualization
Building Science Graphics: An Illustrated Guide to Communicating Science through Diagrams and Visualizations
Joyful Infographics: A Friendly, Human Approach to Data
Data Visualisation: A Handbook for Data Driven Design
Data Literacy Fundamentals: Understanding the Power & Value of Data
Discussing Design: Improving Communication and Collaboration through Critique
Primer of Visual Literacy

Related EpisodesEpisode #230: Vidya Setlur and Bridget Cogley
Episode #222: Richard Brath
Episode #213: Elevate Your DataViz Team

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PatreonWinnoNewsletterOne-Time with PayPalTranscriptThis episode of the PolicyViz podcast is brought to you by User Interviews. User Interviews connects researchers with quality participants who earn money for their feedback on real products. So there’s high demand right now for software developers and engineers to provide feedback on products that are being created for developers. So if you want to help shape the future of the tools that we use in the data, data visualization, data communication field, this is your opportunity to provide that feedback to product developers. So you can go in and you can sign up for free, you can apply for your first study in other five minutes, and they will send you updates for surveys that are going to be related to the work that you do, so you can actually customize what opportunities you’re going to see from User Interviews. Now, most studies, at least, what I’ve seen here are less than an hour, they pay over $60 for an hour worth of work. Some studies are more the focus group, the one on one conversations, and those pay even more money up to several hundred dollars. So there’s some opportunity here not only to help shape the future of technology, but also to earn some money for your time. So if you’re ready to earn extra income for sharing your expert opinion on software development, engineer hardware/software, head over to userinterviews.com/hello to sign up and participate today.

Welcome back to the PolicyViz podcast. I am your host, Jon Schwabish. Hope you’re having a good start to the year, I hope you enjoyed the last few episodes of the show. We’ve been dealing with some interesting pieces on different data visualization books, and today, it’s actually going to be no different because on this week’s episode of the show, I am fortunate enough to be chatting with Lilach Manheim Laurio who I’ve been chatting with a lot lately, because I’ve been thinking more about how we as a field critique data visualizations, and the different types of ways that we critique data visualizations, and so, Lilach and I have been emailing back and forth. She’s been so kind and generous to take a lot of time reading through this very long post that I’m publishing today, along with this podcast episode about the field of DataViz critique. And Lilach has a book coming out later in the year about this concept of critique. How do we do it? How can we do it better, both as a critic and as someone receiving criticism? And how can we do that not just as individuals, but also how can we do that within teams and within organizations?

And so, what you’re going to hear in today’s conversation is the start of Lilach’s work on this at Visa where she and her team created this data experiences framework. It’s like 10 pages long, it’s terrific, and it gives you a lot of ways to think about generating critique and to think about all the different aspects of data visualization that you might want to think about and talk about and try to improve upon. And I’ll link to that in the show notes, there’s a lot of books that we talk about here as well, not just in data visualization, but also in the UX/UI fields, so I link to those as well. So I’d really encourage you to listen to today’s conversation, I’d encourage you to check out the blog post that I’m publishing along with today’s conversation about my sort of view on DataViz critique and what we as a field need to do and what we need to do better, and where I think we need to focus our attention, rather than just saying, oh, this graph is garbage, this graph is great, what we need to do sort of to move the field forward.

So I hope you’ll listen to today’s conversation, hope you’ll check that out, hope you’ll check out all the other resources on policyviz.com, on my YouTube channel, my Twitter feed, my Winno feed, wherever you like to connect with me. And if you have comments or questions, please let me know, you can connect with me at all those different places. So here is today’s conversation of the PolicyViz podcast with Lilach Manheim Laurio.

Jon Schwabish: Hi, Lilach, good afternoon. Welcome to the show.

Lilach Manheim: Hi Jon. Thanks so much for having me.

JS: I mean, this is really exciting, because – I mean, no one really knows for us, but, like, we’ve been emailing at length for like a while about a lot of different topics, primarily what we’re going to talk about today. So this is very nice to actually see your face and chat in person.

LM: Definitely. And definitely, thank you for your patience and openness to my diatribes on [inaudible 00:04:23].

JS: No, it was great. I mean, this is the thing that is, I mean, I don’t know, I think everybody has their own fears of feedback and rejection and criticism and something like you have. For me, it’s always been writing, like, that’s always the thing. It’s like you just have to learn to embrace the feedback and the critique, and that’s the only way to get better. And that, of course, is a great segue to what we’re going to talk about today, because you are working on a book on critique and feedback, which I’m really excited to talk about, but you’ve already done a bunch of that work, and I’m sure, at least, some of the listeners of the show are familiar with the data experiences. Is that really a checklist per se?

LM: You can call it a critique framework.

JS: A critique framework, yeah, from you and Frank Elavsky, and a couple of others, I think. So I’d like to talk about that.

LM: [inaudible 00:05:15].

JS: Yeah, and talk about the book and how exciting that is that’s coming out. So maybe we can just start, you can talk just, sort of, give a little background and how you ended up at Visa, and what you do there on the DataViz side?

LM: Sure. So I think I have – I’m one of those, like, got into DataViz through a bit of a unique pathway. So I started, initially studied art history, and then, a few years later did my Master’s in Library and Information Science with a focus on information seeking behavior, which is really like a fancy, I guess, library term way of saying, it’s how people, when you have something to research, how do you go about looking for information. And then, I really actually have really got into, while I was doing that degree, kind of, how people deal with information overload, and how you kind of interact with a lot of information and decide when you’ve found enough information to answer your question or complete your task.

And so, during kind of the last year of my program, I kind of, a little bit, accidentally discovered DataViz and Tableau, and it was like the light from the heaven, like, it was just such a crazy mashup of everything that I loved, and kind of learned, or had been doing before that, I never really anticipated using anything I learned in my art history degree, outside of museum. But definitely, I love how I can bring all of those things into DataViz, so I spent a few years kind of building dashboards and doing the BI thing. And I think just as I got further into it, and especially getting more involved in the Tableau community, I really started falling more in love with the teaching piece, and how can I enable others to improve their, especially the design piece of the skill set.

And so, the opportunity opened up, I’d say, definitely a big part of it was the Tableau community, because otherwise, I would have known Chris DeMartini who’s running that group, but it was a really good opportunity to work in a center of excellence, which is kind of more the enabling piece. And I think that it really, really appealed to me, especially, about the Visa team is that it’s, we like to call it a small but mighty team. But we’re really, I think, partly because of the size of the team, but also, kind of, like the nature of working in a somewhat big company, we’re really focused on just kind of like tool agnostic data visualization.

So, I mean, I think that’s just a really, a super interesting challenge, a chance to be creative, a little not just in making DataViz, but how do you kind of abstract out a little, but not too much. Like, what makes something a good data experience, a good data [inaudible 00:08:38] and really helping people, because it’s not just tools. It’s even like the difference between BI folks who are making dashboards for other people to do interactive analysis, or someone like a data scientist presenting results [inaudible 00:08:58] these are very different types of things that we’re making, experiences that we are making.

JS: Yeah, and I assume, both internal like data scientists internal to Visa, but also communicating with banks and customers and the external piece too.

LM: Yeah, Visa does a fair amount of data products that are kind of sold to customers.

JS: Right.

LM: [inaudible 00:09:23]. I think you can kind of get a little sense of that. There’s a program that, I think there’s some, like, information on the developer Visa external website. It’s called VAP – I should know what that stands for, Visa Analytics Product. But also we have the VCC, which is Visa Chart Components, fairly recently put out there open source. So definitely, I think, because we have some, like a fair amount of the data product that are made are beyond just internal units, we have a bit more of the opportunity to do something like open source the [inaudible 00:10:11].

JS: Yeah, that’s great. So, I guess, we can start with the data experiences framework. So I want to start with where or why that came about, like, this is like a 10-page framework of doing a better job of providing feedback for all these different types of visualizations that you just mentioned across, I mean, I don’t know how many it is, I want to say, like, 20 different domains. So I’m curious, like, was that sort of demand driven within Visa, or was it supply driven where you and Chris and people on the team were like, we definitely need to do this better, let’s create this experience, this framework.

LM: Yeah, that’s a good question. So I think it’s somewhat demand driven, and then that we kind of got asked, like, within our org, like, how can we improve the maturity of data products, and, obviously, that’s part of our team’s mission, but how can we measure whether a dashboard is good or not so good, and kind of how can we [inaudible 00:11:25] have some consistency in saying, these are the things that should be addressed to improve the quality of the experience. But the great thing was that we had, we got a lot of flexibility in terms of like how to do that, and I think, especially, because of our focus on being tool agnostic, that was kind of like one of the challenges of like what are things that are universal. But still specific enough that, like, it can actually help a person to improve visualization.

JS: Right.

LM: And I think the other thing is that, like, we really, kind of, I think Visa generally has a pretty high level of maturity, at least, compared to most companies for the design maturity, so we have a whole team for the design system, and also accessibility. So there’s already, like, people are already used to doing a pretty thorough accessibility check for any product, not just this product. And so, we wanted to see how we can kind of infuse more of human centered design, so how can we – I think, generally, because we’re data people, we think about the data, so when we design, we’re thinking about how can we best present this data, rather than what does a human need to do with the data first, and then, what information can we help them give them to do that. And I think similar with critique, a lot of times we focus on the things in the data visualization, like, do you have a good title, or did you – I don’t know, did you pick the right chart. These are all very system focused. In our case, the system is also data [inaudible 00:13:19] focused.

So we really wanted to do something that was, instead of a checklist of going through, like, did you do this and this and this, like, being able to look at a dashboard or a DataViz, and saying, is it accomplishing these things. And so, it was great, because I kind of, I was like, oh, I wonder if we could take some of the concepts from heuristic evaluations, which are very well established in the UX field, but apply them more to a data product, and this is where I really love – this is where our team is awesome, because Chris was like, okay, what would it look like, just run with it.

And so, I looked at some of the key UX usability heuristics, and then, there’s also been, like, I’ll say, a good amount of work on how to apply some of those heuristics to dashboards. Some of them are like very domain focused, like, the one that I looked at that I cite in the framework is like the, I think it had to do specifically with a domain of health information in a hospital setting. But still, it’s a good foundation to extract from. So we kind of took that as a starting point, and [inaudible 00:14:54] a lot of kind of mapping very information architecture kind of bring storming work. I’d like, okay, if you take something like visibility of system status as one where you want to have filters or really anything that a user interacts with, like, to be free users to not have to work to find that, so obviously for…

JS: Right. So filter or a dropdown, yeah.

LM: Yeah. So for DataViz, obviously, it’s a little, like, there’s some unique things that that applies to, so that could apply to, like, do you have legends that are easy to find, or something like that. So it’s a process of kind of like coming up with what would some questions be specific to a DataViz that would test for that heuristic, and then, expanding on that to what’s maybe some additional heuristics that aren’t yet, you know, because we want to go beyond just usability. So one that I know we added was it kind of has a somewhat similar, not necessarily heuristic, but more principal in UX of you want something to be useful. So usable is important, obviously, but useful as well.

And so, we basically called it valuable, but we really tied it to what you learn, or what you’re able to learn from using a product. So does it answer the question that, or, what kind of business value does it add to a user if they were to use the product to – answer that question?

JS: So in the UI/UX field, the heuristic would be a button, or a toggle, or a switch, or a scroll, just examples. But when you think about heuristics applied to DataViz, in addition to those pieces that you might have a filter or search bar and a dashboard, but in just any general data visualization, a heuristic would be defined as all those elements on the chart space itself, the legend, the axis, all those pieces would be a heuristic.

LM: Kind of, so, I think the heuristic is the kind of test, and those elements are what you look at to see if they have achieved that heuristic, so, like, preventing errors, let’s say, that’s one of the common ones. So you could look at all the things, whether it’s how filters work, or a particular, like, a label on, and the option on a filter. So I think what expands is we still could use a lot of those basic kind of overall tests of, like, does the content on the page achieve the functionality, does it achieve these heuristics, which are kind of like outcome, the more describing what’s a good experience.

But then you evaluate it all a little, and I think that’s one of the challenges, which is kind of our critique frameworking was, we have a set of heuristics. And then, we also have what we call design pillars. So those are really the things that you – the actual objects on the page or [inaudible 00:18:30] look at the what of what you look at, when you are critiquing a DataViz. The heuristics are like goals, the principles of what we want to – it’s kind of like almost how we describe if it’s a good experience.

JS: Right.

LM: And I think the somewhat unique thing we did is that the original like Nielsen’s original 10 usability heuristics, we took those and we had an additional six, some were like a little bit invented, like, I added one for limiting distraction, which isn’t really a frugal heuristic anyway. But what we did is, so we took those, like, individual heuristics and said, like, okay – we categorized them into five broad categories, and the category is more like an outcome, and I think it helps you. So we call them human centered heuristics and outcomes, to really make the point of like, this is really describing what does a great data experience feel like for a human who’s using [inaudible 00:19:43]. So, like, if you think of something like efficiency is something we tend to think, [inaudible 00:19:51] in DataViz, we put that, what we call, productivity. So it’s kind of framing it a little bit more.

JS: Differently, right.

LM: This is what allows them the user to feel or two, and then, that rolls up to the focused and clear category. So if data experience is focused and clear, it basically allows a user to focus on the most important information, like, for completing their analysis. So the heuristics are really describing it in even more detail, like, what does it mean to be focused and clear, what allows you to be productive. It still provides flexibility is another one. And I think the other couple in there is limiting distraction and directing attention.

JS: It’s really interesting the way you describe it, because the change of the word efficiency to productivity, I think, is really smart, because efficiency in a lot of ways sort of implies speed, like, can I get the point of this graph as fast as possible, as opposed to how does this graph actually help me do my job or make a decision, which is different.

LM: Yeah.

JS: That’s interesting. I wanted to ask you, and then, and I want to shift gears and talk about how this launched into the book, but you framed the work on this as sort of tool agnostic, lots of people are using lots of different tools, and this could be used for any of us. And I am curious whether you think that approach was limiting, or it was freeing, because I can imagine if you’re like, let’s build this for Tableau, you might, in some sense, say, well, then you should use this filter type for this type of data, and this type of map for this type of data, even though those can be applied to different things. So I’m just curious, in retrospect, if someone said, let’s make this for Tableau, or for Excel, or for JavaScript, or Python, whatever, would you’ve been like, oh okay, yeah, that puts me in a fairly simple box, but also too constraining?

LM: So I think it probably just made the challenge really interesting, I would say. I think it did help to move away from building, thinking about building…

JS: Building, right.

LM: I mean, I think, one thing is, obviously, I think, through a lot of DataViz stuff as in Tableau terms, because [inaudible 00:22:25]. So when I think about something like negative space, a lot of times, I’ll think about in terms of like, did you add padding to a container.

JS: Yeah.

LM: But [inaudible 00:22:37] I think that can apply to, I mean, there’s ways to phrase that for like CSS.

JS: Oh sure, sure, sure, right.

LM: You know, anything, I mean, any tool will provide a way to create negative space with some sort of feature.

JS: Right. But it does sort of put you a little bit in a box of, am I thinking the way someone who’s, yeah, like, just the way we think about it is sort of different when we’re in our little toolbox.

LM: Yeah, totally. I do think though that what helped is, at least, on our team, and I think definitely the design team, we follow more of the design process of designing something outside the tool first, so that’s sketching, and also then using a design tool like Figma or something. And I know that’s kind of a little controversial, like, how much work do you want to put into. It’s a skill to abstract it out, and not get too detailed into, like, oh, what’s the realistic data distribution for this bar chart.

JS: I think that’s really what the challenge is, right? Like, I don’t know what my data would look like, until I make the chart with the data, and I can go draw what I want it to look like.

LM: Yeah.

JS: But then I throw the data on top of it, and it doesn’t work, because I have some huge outlier, and so, I need to use a log scale or something. Right?

LM: Yeah.

JS: I mean, you’ve very kindly sent me a couple of chapters of the book, which is, I am going to use this as a segue to look through, and, like, there are some, in just the two chapters you sent me, there are a bunch of sketches. While it is a little controversial because I think there’s just the data layer, I just, and I think a lot of people, and I’d put myself in this spot, are just like a little, not embarrassed, but shy about sharing our sketches, because, like [inaudible 00:24:25] but it is such an important part of the process, just to, like, to your point from earlier, to pull yourself away from the tool, because, I’m like a Tableau not quite a newbie but, like, half a step above a newbie, like, I’ll think like, oh, I just made this thing this morning. I was like, I want to make this thing in Tableau, it should be super easy, and it’s not, because I don’t know how to do it. Right?

And so, I think it’s just those different levels and those different steps, which brings us to your book coming out later this year. And I am increasingly thinking about this, like, new evolution of DataViz books that go beyond the 101. So we have the Bridget and Vidya book, Functional Aesthetics. We have Jen Christiansen’s book Building Science Graphics. I think your book is going to fit nicely into this new space. So your book is, Let’s Talk About Data Visualization, and it’s really focused on – I don’t want to short shrift it, but it is kind of like the more in-depth version of what we’ve been talking about, this framework, and it’s really like pushing people into a more, I don’t know, like, a more concrete way to think about critique and feedback.

And so, before I ping you with my questions, so people can hear what I was [inaudible 00:25:48] with the blog posts that I’m publishing today, along with this post, I just want to ask you to talk a little bit about the book and what you think it’s going to provide people with who are in the DataViz field.

LM: Yeah, definitely. So first, I’ll just say, thank you for including me in that category. I’m definitely honored, some of those are some of my new favorite books, and it’s exciting to see the field moving towards that. I think often, again, when I was talking at a conference, I’ve noticed a lot of times design gets put into the intro level stuff, and it’s really not like, I’ll give you one book example, one of my favorite books is a Primer on Visual Literacy, and that was written actually in the late 70s I think, and it’s still – I’ve reread it I think at least five times, and every time, there’s something else I take out of it, and it’s a little crazy how applicable it still is. But it’s very much not a beginner book. It’s foundational. I would say, the more you learn about design, the more you can go back and reread it, and get a lot more out of it. But it’s nice to see kind of more and more people, like, books being written on the kind of beyond the basics, but it can still be very foundational.

JS: Right. And I think there’s always going to be, I mean, I’ll say this as an author of one of those 101 books, there will always be a space for those because there’s always going to be people coming to the field new, they haven’t really thought about how to make a bee swarm chart or something like that. Right? And so, that’s sort of a new experience, but they, like all of us, are going to grow and going to make more and more, and what is the next steps, and I think one of the other things that we don’t see a ton about in the field maybe aside from Ben Jones’ books, and maybe Andy Kirk’s book is on teams and organizations, and sort of like a larger group, which I kind of feel like your book is really going to help people with is not just, you’re not just a person on your own making stuff and putting it out, and you’re being done. You are working in a team, maybe for a boss, but we all work for a boss, you’re working for someone. Even if you’re not working for someone, you’re a freelancer, like, you are working for your audience, or you’re working for your client. So there’s always more people involved, and I think that’s where the literature really quite isn’t at now.

LM: Yeah. And so, that’s kind of my goal with the book, I would say, I was very inspired by this book that’s more in the general UX field called Discussing Design, and that highlighted some issues in how general UX field was kind of, let’s say, had room to grow in terms of doing critiques better. And so, I think that’s a good example of the fact that it is a kind of growing pain. I think it was one of the ways the UX has matured as a design field, I mean, there’s many flavors of UX, obviously. So I think one of the main Manheim things, points that they make in that book that I’m really going to try to spend some time in my book expanding on what that might look like for DataViz is the idea that we pretty much agree in the design process, if you’re following a better design process. You’re separating it out, like, defining the problem, and coming up with solutions for the problem. And yet, a lot of times, when we critique, we just jump right ahead to the solution.

JS: Right, yeah.

LM: So It takes, I talk about it as like a skill and like muscles that you have to build, because when you’re so used to doing it that way, it takes relearning the habit, you’ll start out by catching yourself that, oh, what I just said, really if you look out for anything, I suggest or what if you did it this way, you know, those are all solutions, and that’s fine. So I think, like, starting to learn to kind of step back and say, okay, this is a solution that’s coming to my mind, that I’m saying, what’s the problem that I’m trying to solve with it, and then, kind of, like, learning to just kind of, eventually wean yourself to at least talk about the problem first.

This is something, I guess, I’ve kind of adjusted as a, cause I think, I probably do get to do a lot more critique in my current role than I did in previous roles, at least, my own stuff. That, like, especially the people that are on the beginning of their learning curve, and especially in design concepts, you need to give them some solutions, like, it’s too, they don’t really know how – you can help them understand the problem that you’re seeing, but, they can’t, if you say maybe the typography hierarchy is unclear, it could really help your design being much more scannable, and help people find what they need and, in the page, if you improved your visual hierarchy. But, like, what does that mean to someone who’s never looked – who doesn’t know there’s like best practices for a type ramp, and the fact that you’re probably better off to get a little bit into detail, you’re probably better off starting out in the middle, and then, going to the edges of what’s going to be the biggest, and what’s going to be the smallest, and then, kind of, feeling like that’s a very procedural, you know, it’s just, I don’t know where I got taught that honestly.

JS: Right, you just [inaudible 00:31:56] along the way, right?

LM: Yeah, but it makes it so much easier to do something like that.

JS: Yeah.

LM: So I think it’s both, but I do think that whole, so I’m not saying, I guess, I’m not saying don’t ever give someone, like, this is how you should do it, but just getting more aware of kind of starting out with defining the problems you see.

JS: Yeah.

LM: I think also, just generally, learning to see more when we look, that’s another big piece of…

JS: And by that, you mean, like, more of the detailed pieces of the visualization?

LM: Yeah, so, I mean, I think some of that is, we can take from kind of art history and visual analysis, being able to kind of identify, so let’s say you start with identifying what’s the focal point, what is it you’re most attracted to. And then, kind of, looking at what are the individual design decisions that are causing, what are the elements on the page that are causing your eye to kind of be move in a certain way, or be attracted to a certain part.

So I mean, I can give an – I think this is a kind of common example. I think it’s a pretty good, like, specific application of this. So if you think about rules that we have, that we try to follow, and whenever we see something in the wild, we are like because I follow this rule. So one, I think, fairly old, not old-old, but a few years old is the idea of how do you decide on the size for a chart. Right? And I believe it was McGill, I think, that came up with a whole measure of the banking to 45 degrees.

JS: Yeah, right, the banking.

LM: So that’s a very, like, specific rule, that’s really a – but if you think about it, that’s really, like, describing a solution. But a very, like, rule based one of, like, make sure it has the 45-degree angle. And so, the problem with that, obviously, is there’s going to be a lot of exceptions. Right? Doesn’t work when – it especially doesn’t work when you’re working with like a line chart that’s pretty flat.

JS: Right.

LM: And so, like, abstracting out a level from that would be, well, we want to size it in a way to make sure that it’s true to the data and it doesn’t distort, you know, make the data look the way that is not true to the actual true shape and meaning of data. So I think that’s definitely a step in the right direction, but, like, because I think it starts you thinking on what is the problem that you’re…

JS: Right, you are trying to solve, right.

LM: Yeah, like, you want to make sure that people aren’t having interpretation errors, because it’s like a weird shape. But then I think like there’s even like further, like, when I say, when I think about how do we see more when we look at it, so you could start thinking about also, and I think this is where it starts coming in that, like, we can’t really look at just one chart. So one of the big things that I think you need to consider when you’re deciding on the size is, like, what’s the relative importance of the particular, like, is it a primary chart, is it a secondary chart, and that should really drive your, how big you make one chart versus another chart.

But then, I think this is where my art historian, internal art historian comes in, is I always really tried to consider the wider frame. So in an interactive dashboard, I think we can usually assume it’s going to be – the shape of the page is going to be a product of your screen, or whatever screen it was designed for. But you’re rarely going to have like a perfectly square page shape, right, you like rectangular or long form. Even if it’s long form, you’re only looking at it like one screen at a time. So I think, a really important thing is, let’s say, it’s your primary chart, thinking about like, what’s the shape of the frame, and is the shape, so basically, aspect ratio is the shape of the chart echoing the shape of the frame, or contrasting to it. So, like, I think if you have, speaking in really broad terms, if you have a rectangular shape of the chart, and it echoes the rectangular shape of the frame, the big frame, then it’s very – it’s a kind of like static design, it’s like, there’s not a lot of movement in the design.

JS: Yeah.

LM: And so, that could make sense, but then, just kind of like thinking about, if you make something, and maybe it’s a chart, maybe it’s something else, like, if you make, like, if you think about maybe like a menu, that’s a tall shape, that has a kind of vertical…

JS: Yeah, vertical.

LM: [inaudible 00:37:27] yeah, if you have one chart that’s very wide, that could lead the eye left to right, but that could also depend on other, you know, what are the shapes of the other charts, and are they competing with the kind of shape that your main chart is creating against the [inaudible 00:37:49] frames, things like that, they’re just very, like, it’s really visual analysis, which.

JS: It is, but it’s also, I mean, one of the things that we can talk about this in a second, because I think it leads back to the post that I published today that you helped me with, I mean, I think the other piece of it is recognizing that different creators have different goals. Right? When I think about a columnist at the Times or the Post, their goal is to get eyes on the page. Right? That’s what matters. Whereas someone working inside Visa or someone who’s providing a memo to whoever their boss, their goals are just different. You are creating something for your colleague at Visa, you know they’re going to read it. The goal isn’t to get people to click on it, because the goal is to make a decision, or, as you mentioned earlier, to increase productivity, as opposed to efficiency, which I started at on my notes.

I just think that’s really smart, I mean, there’s been for a long time, this discussion that sort of ebbs and flows on impact, and how do you measure impact, and can you even measure impact. And I think the efficiency thing sort of goes hand in hand. I think the other thing that goes hand in hand with that is speed. It’s one of the things that I just, like, there’s this obsession with, do I get the message of the graph, just like as fast as possible, as if that should be a metric.

So I guess, the sort of next question I want to ask is, when you see people doing critique today, and we’ll keep it sort of in the Twitter world, so public critique, not within, and not within teams, but in the public sphere, what do you think is the thing most people are doing the most wrong? Is there an aspect of critique generally that you think people are just missing the point where they’re critiquing the wrong team?

LM: Yeah, so how long do you have? I mean, obviously, I am writing a book about this, because…

JS: Right, writing a book about it, right.

LM: Right. I think you did hit on something that’s really important is there is a big difference between critiquing, like, critiquing someone’s work, and critiquing work with someone.

JS: Yeah.

LM: And really the critique that I do on a regular basis at work, and that sometimes I’ve also done in with public work, like with people I collaborate with is the second, and it’s really a conversation. Sometimes people make themselves available, and you can ask clarifying questions, but really, if you’re doing the second kind, where you get to have the conversation with someone, that’s why I call my book, Let’s Talk About Data Visualization. I mean, you should start, and that should always start with just kind of like when we start on developing data visualization. We’re trying to discover and ask questions [inaudible 00:40:58] you’re trying, but you’re trying to discover what was the creator’s goals and constraints. And then really trying to understand what are the choices they made [inaudible 00:41:14] design, kind of, how those work or don’t work for what they’re trying to do. And that’s really, you do that before you go onto, like, more, like, evaluating…

JS: Evaluating…

LM: That you think isn’t working, right? Like you have to really understand, seek to understand what it is, you know, what the current design is, I think which we probably don’t do enough of that. The thing that, I mean, I think there’s definitely, like, room for the other kind of public or more you don’t get a chance to maybe talk to the creator. But just kind of organizing, that’s a slightly different thing. It’s almost like criticism, and I don’t know if that’s what, like, if you think about our history field, like, yeah, there’s critics that go look at artwork and write out what they think of it. But that’s almost like a different goal, like, so if you’re having more of a conversation with someone, your goal is to help them figure out how to improve their data visualization. Or you could be trying to kind of evaluate your own work, but your goal is to figure out what’s not working, and how to make it better.

JS: Right, and to help other people do better, when they’re…

LM: Yeah.

JS: Right, yeah.

LM: I think the one thing that really is a bit of a – it’s a pet peeve, like, the thing that I actually like is starting off things a little wrong is when someone posts all feedback welcome. And I’ll be honest, I fell into that, sometimes I still have to catch myself. And I think that’s one of the things that I really loved about the Discussing Design book, they brought [inaudible 00:43:00] really, it’s a two-way street. So if you’re not getting useful feedback, like, if the feedback you’re getting is all, like, you need to make this button bigger or wider, some of that might be coming from the, like, you’re not asking for the feedback, it’s kind of like, and the person asking for the feedback. Obviously, critique on Twitter doesn’t usually end well, someone [inaudible 00:43:29].

JS: Yeah, well, there’s that.

LM: I have a whole chapter devoted to that as well.

JS: Yeah.

LM: How can you articulate and describe what is the feedback that you’re looking for. So what’s the feedback you’re not looking for?

JS: Right. I don’t care about the size of the buttons or the color of the buttons, I know I need to fix that. But like, so instead of all feedback welcome, do you have like a good pithy phrase for people to like, this is like the hook, so they have to wait a few months till the book comes out, but do you have that phrase in mind for people?

LM: I probably should have a good pithy phrase. I think I, yeah.

JS: But the point is maybe there isn’t a good pithy phrase, because…

LM: Yeah.

JS: Sometimes maybe you do want to know, like, are the buttons in the right spot versus the colors of the line chart, because I can’t control that, because this is what our company follows, and it’s a red line, and that can’t be changed, but the button is the button in the right spot. So maybe there isn’t a phrase, and it’s just, it’d be more focused in your soliciting.

LM: Yeah, and obviously, this is tougher to do in Twitter, like, what we’ve done at Visa, and I’ve seen something similar in the DVS channel on critique is we have like a set of three questions that when you come in and ask for critique, you kind of fill out ahead of time to explain to people what type of feedback you’re looking for. In the book, I kind of outline, like, there’s two main questions that you as a reader should kind of insert to help people understand what type of feedback you want. The first is just kind of what’s your goals and objectives to whatever level of detail you want to get, like, what is the design trying to achieve. And the second is what are the elements that you want to have evaluated. So I think it can be helpful to kind of think about it in terms of – I use design layers in the book, so is it like something to do with the information architecture, or is it the thing that we usually tend to fix up to focus on, which is the chart design, so is it the information visualization layer.

So I do, I guess, give some tools for describing what parts of the information product you want, like, you want to get feedback on. And it may be like a specific part of it, right? Let’s say you want help with the interactions, interaction layer, so a really broad question would be how well does the interactive features that I have, how well does that support the analytical flow, or the questions that I want to enable people to…

JS: Right. The [inaudible 00:46:28] make you more productive.

LM: Yes.

JS: Make you more efficient, and make you more productive.

LM: Yes, or do they give you interesting, actionable answers.

JS: Yeah, right.

LM: Like, a lot of times the classical example is having a million filters. They don’t really help to come to new answer.

JS: Right.

LM: Yeah, but the other one would be like maybe, do I have clear feedback, where it’s clear that when you interact with some feature, it’s clear what happened.

JS: Yeah.

LM: Right, so that’s like a more specific one.

JS: Yeah.

LM: Or even, like, is this button difficult to find?

JS: Right.

LM: Right.

JS: Or the outcome’s obvious is what I have to do obvious right.

LM: Yeah.

JS: So I think in the short term, before the book comes out, and you have all these checklists, by the way, everybody, for those of you listening, watching, the checklists, I don’t want to call them checklists, because they’re not really checklists, they are frameworks, and they are cues, are amazing, they’re going to be super helpful to you, so be ready. But here’s a good, in the meantime, till the book comes out, a good lesson for folks to keep in mind, like, when you are asking for feedback, be specific, and be purposeful, so that you can get the feedback that you want. And because we’ve already been talking for an hour, I want to wrap up, but I think also as the critic, to be a little bit more purposeful and thoughtful, and I like this idea of critiquing someone’s work versus critiquing with someone are two very different approaches. So Lilach, thank you so much. I mean, we could keep going, but, at some point, people are going to, like, they’re going to hit like two times speed, yeah.

LM: [inaudible 00:48:10] because I really, I know this, kind of, started a big Twitter conversation, but I think I still think it really crystallizes what, I guess, my book tries to do and what the critique framework also tries to do. It was by, let me see, I wrote it down, it was by Dr. Cat Hicks, and she said, we try to make often, we try to make complex problems easy, rather than making it easier to work on complex problems. So I think if there’s any one thing that I think we can, like, push us forward in how we critique, is like we do need to think a little deeper. Like, I think we’ve done some initial really, we have a lot of really great work on very specific rules that kind of cover the easier things. But, and I totally get the instinct to want just a rule, like a do and a don’t. But it really is about, like, how to think a little deeper about all these things.

JS: Yeah.

LM: And so, I do think there’s space for tools, like, I hope the critique framework is the beginning of that.

JS: Yeah, the four tools.

LM: Yeah, that help you to think through some of those deeper things, without giving you a set of simple yes-no answers.

JS: Right. I mean, I think that’s the challenge with the checklist, where there’s a box, where it’s like you check a box, check a box, check a box, because, as you said, sometimes they don’t apply, and some things, I would say, are more important than other things. I mean, I don’t know, like, integrity of the data is more important than the font size of your title. Right?

LM: Right.

JS: I guess, if I had to come up with that, it’s not a pithy statement per se, but I think the message here is to think more deeply, I think on both sides is what I’m hearing from you, right?

LM: Yeah.

JS: As a critic to think more deeply about what you are critiquing, and as the person being critiqued or soliciting critique, being more thoughtful about what you want people to say, and maybe how you respond, even to those, what I kind of call the hit and run or drive by critiques, it’s like, this is garbage, and you’re like, maybe you respond in kind of a different way to be like, well, I know that you don’t like the colors, but that’s the branded colors, and that’s what I use, but is the graph type, like, is that useful, so I like that. My wife would call it a compliment sandwich, so you give a compliment, and then, some critique, and another compliment in that.

LM: Yeah.

JS: Okay, so the book comes out when, like, in the fall of this year, summer?

LM: I’m not sure that…

JS: The publishing world of – the mystery of publishing world.

LM: Yes.

JS: Right. Okay. But in the meantime, people can find you where, on Twitter, for sure?

LM: You will find me on Twitter. I have my regular handle, where I will be honest, I tweet about probably politics as much as DataViz. But you can also, for updates and sneak peeks about the book, you can follow DataViz Crit.

JS: Okay, great. I’ll put all this and links to everything that we talked about on the show notes, so people can check it out, because there’s a lot here, and a lot for people to think about, and hopefully do better individuals and as teams. So this was great. Thank you so much for coming on the show, taking time out of your day, and yeah, I’ll talk to you soon.

LM: Okay. Thanks, Jon.

JS: Thank you.

And thanks everyone for tuning in to this week’s episode of the show. I hope you enjoyed that. I hope you’ll start to think a little bit more in depth, a little bit more creatively, a little bit more with sophistication and purpose about your efforts in critiquing and receiving critique in your and others’ data visualizations. Be sure to check out all the links on the show notes. I’ve got links to all these different checklists and frameworks that we talked about in the interview and links to all the books that we talked about as well. There are some great new books out on the market, and I hope you’ll check them out. And, of course, don’t forget to read the blog post that is up at policyviz.com where you can sort of get my take on DataViz critique. So until next time, this has been the PolicyViz podcast. Thanks so much for listening.

The post Episode #231: Lilach Manheim Laurio appeared first on PolicyViz.

Data Visualisation: A Handbook for Data Driven Design

Data Literacy Fundamentals: Understanding the Power & Value of Data (The Data Literacy Series)

Discussing Design: Improving Communication and Collaboration through Critique

View Details

*Vidya Setlur* is the director of Tableau Research. She leads an interdisciplinary team of research scientists in areas including data visualization, multimodal interaction, statistics, applied ML, and NLP. She earned her doctorate in Computer Graphics in 2005 at Northwestern University. Prior to joining Tableau, she worked as a principal research scientist at the Nokia Research Center for seven years. Her personal research interests lie at the intersection of natural language processing and computer graphics to better understand data semantics and user intent to inform the meaningful visual depiction of data.

Interpreter turned analyst, Bridget Cogley brings an interdisciplinary approach to data analytics. As Chief Visualization Officer at Versalytix, her role uplifts data visualization within the org and helps shape the vision. Her dynamic, engaging presentation style is paired with thought-provoking content, including ethics and data visualization linguistics. She has a deep interest in the nuances of communication, having been an American Sign Language Interpreter for nine years. She is currently a Tableau Hall of Fame Visionary. Her work incorporates human-centric dashboard design, an anthropological take on design, ethics, and language. She extensively covers speech analytics and open text. Prior to consulting, Bridget managed an analytics department, which included vetting and selecting Tableau, creating views in the database, and building comprehensive reporting. She also has experience in training, HR, managing, and sales support.

Episode NotesFunctional Aesthetics for Data Visualization
Webinar about the book

Paper: Striking a Balance: Reader Takeaways and Preferences when Integrating Text and Charts by Chase Stokes, Vidya Setlur, Bridget Cogley, Arvind Satyanarayan, and Marti Hearst

Versalytix
Stroop Effect
Tableau User Groups
VisComm
Information is Beautiful Awards

Other recent books

Jen Christiansen, Building Science Graphics: An Illustrated Guide to Communicating Science through Diagrams and Visualizations
Nigel Holmes, Joyful Infographics: A Friendly, Human Approach to Data

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Episode #209: The Flerlage Twins
Episode #202: Lindsay Betzendahl
Episode #201: Leland Wilkinson

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PatreonWinnoNewsletterOne-Time with PayPalTranscriptThis episode of the PolicyViz podcast is brought to you by PartnerHero. PartnerHero provides customer service outsourcing that’s built for the needs of small businesses and high growth startups. So if you’ve ever needed help with your data visualization work the way I do, sometimes I need someone to help me scrape some data or make a graph or edit a blog post, I use PartnerHero. They have flexible terms, they can help you scale quickly, there’s quality assurance baked into every product and every program, and they have offices around the world which can also help you with different languages and different nuance in your writing or your visualizations. So if you’re ready to bring an outside customer support to help your startup that feels like it’s part of your existing team, check out PartnerHero. Head on over to partnerhero.com/policyviz to book a free consultation with their solutions team. Mention you heard about PartnerHero from PolicyViz, and they’ll waive the setup fee.

PolicyViz Podcast Episode #230: Vidya Setlur and Bridget Cogley

Welcome back to the PolicyViz podcast. I am your host, Jon Schwabish. Happy New Year, everybody. I hope you had a great New Year, a great holiday. Glad to have you back listening to the show for another great set of episodes, I’m going to take you all the way through June of this year, I’ve got a whole set of great guests coming your way to kick off the new year. I’m really excited to bring you the authors of the new book Functional Aesthetics for Data Visualization, Vidya Setlur from Tableau, and Bridget Cogley, we talk for a while.

I won’t lie to you, when I get folks to come on the show, I say, yeah, we’re going to chat for 25 or 30 minutes, and then we have a little bit of a chat before we actually start recording, make sure we’re all on board of what we’re going to talk about in the topics, and sort of have a list of questions we’re ready to do. And we were chatting for a while before we recorded this, and then we recorded, and I couldn’t stop, it was just the conversation was so great. I think Vidya and Bridget are onto something with this book, sort of, moving the data visualization field into the next stage of its evolution.

Obviously, a lot of my writing is on best practices and step by steps and introductory pieces. But there is a need and there is a market for that next phase of the field – how do we sort of think about data visualization, less as here’s a chart, go read it; here’s a bar chart, see if you can get in five seconds, to thinking about the next form of data visualization as a language, not just as visual icons or that sort of content.

So Vidya, Bridget and I talked for almost an hour in this, but I think it’s a great conversation. I think you’re going to really enjoy it. So again, Happy New Year, I hope you’ll enjoy this first episode of the PolicyViz podcast of 2023. So here’s my conversation with Bridget and Vidya.

Jon Schwabish: Hi Vidya. Hi, Bridget. Good morning early for you, Vidya right?

Vidya Setlur: Hi. Yeah, it’s 7:40, it’s not too bad.

JS: But you’re already at the office and ready to go.

VS: Yeah, looking forward to this.

JS: That’s a pretty good start. Well, thanks so much for coming on the show. I’m going to, for folks who are watching the video, I’m going to hold up your new book, Functional Aesthetics. Great new book. Love it. I’ve already ruined it with all of my writing and my tags and folded pages and everything. I think it’s great. I want to dive into a few sections of it, but I thought we’d start with just introductions, maybe just tell folks who you are and where you’re working, and then, we can talk about how you two got hooked up. You tell the story in the book, but maybe we’ll give a preview for folks, and then we could talk about some content. So Vidya, do you want to start?

VS: Yeah. Well, thanks for having me here. I am the Director of Tableau Research. I have been at Tableau for 10 years. I lead a really awesome team of interdisciplinary research scientists in the areas of data visualization, multimodal interaction, applied ML and NLP. I got a PhD in computer graphics and NLP in 2005 from Northwestern, and I was at Nokia Research seven years before I joined Tableau.

Bridget Cogley: I’ll also just add, she writes a lot of papers and collaborates on a lot of papers, so she’s really good at bringing together these teams of people that wouldn’t necessarily normally collaborate. She’s like, hey, let’s work on this, and I’ve gotten to be a part of it once, which is really nice.

VS: Yeah, and hopefully there will [inaudible 00:04:43].

BC: I’m Bridget Cogley, and I’m the Chief Visualization Officer over at Versalytix. I’m also the cofounder; and then, I am a Tableau Hall of Fame visionary, so I’m old enough in Tableau world to be retired, or at least semiretired, and put out to the pasture as far as like recognition goes. I started kind of using Tableau itself back in 2010, and before that did a lot of analysis in Excel. And then, I started my career path as an American Sign Language interpreter. So when you read the book, I’m the practitioner voice in that book, whereas Vidya is the researcher, and really, together we come up with a lot of these spitball theories where it’s like, well, I think this thing is true.

JS: Right. And you have this model in the book that I want to get to, that tries to piece these together. But maybe we could start with how you two got together, and teamed up to actually write this book.

BC: So it started with a Tableau user group over in Wisconsin, and so, they reached out to me and said, hey, you know, and I was doing this logic of dashboards talk, which was my whole, you know, how do you build dashboards, kind of, with a logical frame in mind. And we were doing kind of a two-TUG tour in Wisconsin. So we started out in Madison, and then, the next day were due to show up in Milwaukee, and they had mentioned Vidya was going to be there, and they were like, well, we’re trying to figure out transport and stuff. I’m like, well, she’d just ride with us, because we drove our teeny tiny Fiat, all the way out there, because this is what we do. It’s the Midwest for us, and so, it’s like, you just get in your car, you drive for however many hours, and what you do.

And so, we get there, and what was really cool is Vidya went first, and she presented, and I was just watching her talk. I mean, I can remember vividly getting chills, because I was like, this is the missing piece to my talk, because I was really going from a semantic lens of, like, you think about describing a room, and how do you do that, and calling out where people miss certain features of it, or they don’t do certain things grammatically, whereas in American Sign Language, there’s actually a grammatical convention to how you describe a room. And Vidya, you definitely need to add to this.

VS: Yeah, it was my first TUG, Tableau user group, so I really didn’t know what to expect, and my talk was not a traditional DataViz talk, so I had stuff on semantics and user intent, and I had a stroop effect exercise, which we talk about in the book, just showing people how our brain works, and how, if information does not align with or semantically align with how we see the world, our brain starts playing games with us.

And so, it was sort of this hands-on, participatory exercise, and so, I did my thing and then Bridget came up, and she had this, like, lovely picture that kind of instigated the need for creating not only beautiful dashboards, but something meaningful, and all of a sudden, for me, I was like, wow, this is such a beautiful segue from what I presented to what she’s doing. And yeah, she was gracious enough to take me in their little Fiat car to Milwaukee…

BC: And I want you to picture that, so my husband’s driving, because I’m lazy, and then, I’m in the passenger seat, and then, Vidya is behind my husband, her suitcase’s in the back, and I swear, she’s like packed in there, a little teeny tiny sardine, because it’s a Fiat, it is [inaudible 00:08:24] and she is happy as a clam back there, just smiling, and grateful. I would just be like, oh, but…

VS: Yeah.

BC: We were happy, and we talked the whole time.

VS: Talked the whole time, yeah, I feel bad for Mike, but he was a good sport. But I will say that, in addition to the similarities of the way we thought about data visualization, we also bonded over food, I mean, I kid you not.

BC: Which is the important part, let’s be real.

JS: Obviously, absolutely.

VS: We’re both vegetarian, we both love Indian food, and we started searching on Google Maps where is the closest Indian restaurant to go to, and that was the whole premise of that book cover. There’s like this map of all the Indian restaurants. That’s our conversation thread above the Fiat car, and [inaudible 00:09:19].

BC: And I just want to point out, like, these are actual Indian restaurants, this is real, this is not…

JS: This is like the Easter egg in the book.

BC: It is. This is the [inaudible 00:09:30].

JS: Yeah.

BC: And if you get the story behind the cover, and that’s why it’s like it’s not a traditional DataViz cover, like, they kept coming back to us with like these real futuristic, like, the dashboard samples you always see, it’s the Pixabay, it’s the Unsplash, you go there and you buy, like, the five dashboard templates that they have. And we really wanted a lot of personality, and we wanted to kind of just showcase the journey. And so, it’s like, we’re just going to do this, so it’s a very metaphorical DataViz without being a DataViz.

JS: So in this car trip journey, did you at the end of that – I don’t know, what is it, an hour and a half between Madison and Milwaukee, something like that – at the end, were you like, we need to team up on something, we don’t know what it is, do we need to, like, where did you end up at the end of that weekend?

BC: So we were going to – we chatted back and forth, and then, it’s like, well, we should do a 2020 talk together. And so, we were really working…

VS: We were also thinking about the Tableau Conference.

BC: Yeah.

VS: Right. And then, the pandemic happened, and the conference really didn’t happen. I’m trying to recall the exact set of events that happened, but yeah, go ahead.

BC: You’d started the talk, and then we kept having more content and more content, so we shared this outline, and it’s like, well, we should just do a book. And I love Vidya for this, because she’s got some hustle, she’s like, okay, we are putting together a book proposal, she sent in publishers left and right. And, I mean, she’s just on it.

JS: Just on it, yeah.

VS: Well, the story there was we formed a social bubble with my son and his friends and their parents, and we all went to Lake Tahoe, and we stayed in this huge house. This was all before we were all vaccinated. And the world was falling apart and it was like, okay, what’s the worst that can happen. So, wrote up a book proposal based on the content that Bridget and I had come up with for our original talk, and then, just sent it to publishers, various publishers all over, and we were like, okay, let’s wait and watch. And then, within a few days, we started getting responses from these publishers, and we’re like, whoa, okay, this is actually…

BC: It’s happening. It’s really…

JS: Yeah, that’s exciting. So let’s talk about content of the book, because in that story, you both use the word semantics. And so, I want to start there. So can you and maybe, I don’t know, I’ll try to pick people so that we’re not over talking, so we’ll start with Bridget maybe. Can you define for folks, what you mean by semantics as it applies to data visualization?

BC: So semantics is the study of how we draw meaning in communication, and the whole premise of my initial talk is dashboards are a form of communication. A lot of times we think we’re making a [inaudible 00:12:25] or we’re making a widget, and they’re really communicating. And so, that’s a part of it, Vidya, I want you to chime in on this as well, because like, we really went kind of back and forth, and what I find is that most people are focused on the perceptual side of this, that this is a perceptual creation. And it’s like, no, no, no, no, there’s so much semantic resonance. When Vidya showed the stroop effect, it’s like, that’s the thing I’d been running up against, and I didn’t have a word for it. I just knew that every time if I use the color over and over and over again, or if I used – if I crossed the wire, if you will, on color, it got really confusing, and I kind of found this little secret sauce, where if I did certain things with color, it worked extraordinarily well to an effect that I couldn’t explain.

VS: Yeah, I mean, so that is the formal meaning, and what we try to explain in the book is, as humans, we are trying to make sense of the world, we’re trying to figure out what things mean, what two people imply by saying something, doing something, seeing something, and semantics is a way of just formalizing that notion of meaning. And we often do this in various forms of communication, and this is also, I think, one common thread that Bridget and I share where she comes from the American Sign Language point of view, and there is a certain type of rhetoric that goes on with respect to communication, but there’s also communication in the form of language that arises from my background in natural language processing.

And so, we really wanted to impress on the reader that visualizations are a form of language used for communication, and there is a set of practices, some of them are taught through design, because when you think about graphics design, we are taught how to emphasize what is most important and deemphasize what is less important. And so, we kind of took that notion which primarily resided in the area of perception and design, and brought in semantics and understanding user intent, which is a way of expressing your goal when you’re looking at information, so we could provide a deeper understanding of how visualizations work, because they are not just a simple graph, right? They contain a message, and they’re trying to communicate something to us, and how do we actually use that form of communication to get people to take action. And so that was kind of where we were going with the book.

BC: And I really liked that Vidya introduced this idea of analytical conversation, and you think about how we converse back and forth, both with ourselves, so we start this initial pass with the data as an analyst, where we have a conversation with the data, and that happens on a very intimate register, where we’re digging into it, we’re exploring it, and we have a bunch of shortcuts, because it’s only us in that conversation; so we can really embed a lot of that shortcut information where we understand what we’re saying, but nobody else does.

The challenge is then when we take that intimate conversation, and we try to present it to the world, and we don’t put in the affordances to make other people able to understand it and navigate it, they struggle. And in the book, we highlight this in part, it’s the paper towel problem, it’s like you have this great experience at a restaurant, you go to the bathroom, and this is like a huge problem for me, you wave your hands in front of that cute little paper towel machine, and you don’t get any bloody paper towels. So then you sit there and you do all these antics to get paper towels, and it finally spits out like maybe an inch or two our paper. You pull at it, you’re wiping your fingertips, and then, you do what I do, which is the toddler thing, you’re wiping on your pants.

And it’s just like, and then, you know, here you are in this nice restaurant with wet pants, and it’s like, yeah, that’s how, again, but we do this to our users all the time in that our intent is we want them to do a certain thing, we want people to use less paper towels, but we still want them to have paper towels. It’s just that the signals get mixed up, and the wrong thing happens. And that’s, you know, we have that intimate conversation, when we’re exploring the data, we don’t put out enough exposition to the users to truly follow the conversation, nor do we do it in a cohesive manner. I mean, it ends up like charts on a page, which makes sense to us, because we have the verbal linkages that they do not.

JS: Right. Do you think that’s just human behavior that we just get so deep into, in this case, our data, that we just forget that our user hasn’t been neck deep in the data for six months the way we have?

BC: I’m actually going to push on that a little bit where it’s we’ve not been trained in DataViz to expose the information. You think about children go to school, they learn an essay writing template, they learn all these ways to expose information, that’s a literate society, and we are entrenched in a literate society. You cannot go anywhere without seeing something in writing. You see signs. You see all sorts of things – even in my car, I have all sorts of stuff that’s written, and it’s really, really hard to navigate the world if you can’t read extraordinarily.

And with data visualization, this is another competency skill, so with data graphic, where we talk about like numeracy as the basis, then literacy, and then, this is the third here. And so, I constantly mention data visualization is the third tier, because it’s not just, okay, we’re getting information from it, but it’s becoming a primary source of information. We are actually learning directly from the chart, i.e., COVID, where we are starting to see case trends, we’re starting to see a lot more visualization incorporated often as the lead in a news story, rather than the supplement. So that is the primary way of getting the information.

VS: I do also want to add that with data visualization, rightfully so, it started by helping people understand how our human visual system works, and what are some core perceptual principles that come into play explaining, for example, when a bar chart should be used, or when a pie chart can be used, or like stack bars may not be good for comparing values. So we have been taught some of the do’s and don’ts from a perception standpoint, but that’s kind of where the message has just been, and we wanted to take that to the next level, because there are some higher order cognitive processes that go into play, including thinking about dashboards and communication as a conversation.

And so, that goes beyond just the sheer perceptual qualities of a chart, because now, barring paper, most visualizations are interactive. There is this back and forth, and so, how does a person, how does the user who is interacting with a dashboard that an author has spent time on, walk away with some mental model of understanding what the data is about, and that happens through that back and forth interaction that tends to not be expressed as clearly when you just stay in the lair of perception.

BC: And that’s also where I kind of channel Marshall McLuhan a little bit, because the medium ends up the message. And so, when you’ve got interactive dashboards, that’s a very different message than something delivered on paper, it transforms how we communicate, it transforms the ways in which I can communicate. So if I’m printing on paper, that one chart really needs to suffice. If I’m building out an interactive dashboard, I can actually split the task amongst several charts. And so, that’s why I don’t often need to try to make this really complicated nested chart. I can split that task up, and let people dig into that, drill into that, and get that in a very different manner.

JS: Right. So you’ve both mentioned different types of modeling and thinking of ways to sort of maybe structure or formalize the way that we create, and then, ultimately, consume data visualizations. So I wanted to ask about this model that you have in the book that is sort of almost the through line, it’s the sort of conceptual and visual model that sort of comes through in this triangle. And I don’t want to describe it, I want to let you all describe it, but I thought maybe Bridget, you could start with this model and how people can think of implementing that into their own process of creating their own visualizations, but also working with their colleagues and their teams; because I think that’s the other piece that I really pull out of this book is that, yeah, you could go off and work on your own, but there is a part of this that requires a team, and if you have these different elements and think about it in that way, you can ultimately be more successful.

BC: So one of the figures we have in the book is kind of it’s a pseudo-triangle is really what it ends up being, and it represents to me the shift. So you start learning charts, and it’s a very elementary, just as Vidya had talked about, these are the types of charts you have, here’s your library of charts, and it’s a very pictorial representation. And to me, this is when you think about learning to read, you’ve got doctors, so you’ve got a lot of these very elementary books that supplement with pictures, and it’s not pictorial learning stage. And so, you start learning kind of these real basic graphics, and these real basic words, and that’s the pictorial stage.

And usually, as a practitioner, when you’re in that stage, it feels overwhelming, because it is. You are often trying to operate at a much higher level than you truly have the skills for, and I know because I was there. It was really intimidating, and then, you get all the books, and there’s a lot of resources, as you’ve mentioned, to learn “how to do it right”, how to refine, how to reduce, how to remove, and that ends up being that perceptual stage. And Vidya, you hit on this as well, and I’ll let you expand further on that.

But then we start shifting into where it’s like, we’ve done this drastic shift, and so, you actually see literally that pendulum shift from pictorial to perceptual. But then, there’s this other shift that happens, and that’s where we propose that instead of trying to move back and forth, what you actually do is you move up to a higher plane, and that’s that semantic phase. And so, then the graphic kind of draws down, and it’s fuzzy. I mean, it’s a really fuzzy kind of graphic intentionally. And then you finally wrap around everything with intent, and this is, particularly, Vidya, you can really talk about this, because that’s where a lot of your work lies.

VS: Yeah, I mean, Bridget, you succinctly described our model. What is kind of interesting with the semantic layer is there is this spectrum of very concrete concepts, which I think someone who has been doing data visualization, even for a few years, can understand or grok. But there’s also these fuzzy notions of language that need to be expressed through visualizations, like, if I am looking at a neighborhood of houses in Seattle, and I want to look for the best house to buy, what does best mean. The author who creates the dashboard might have their own mental model of what best means, but best could mean very different things for even the three of us. Right? Is it a good walk [inaudible 00:24:12]? Is it proximity to restaurants? Is it a good school district?

So that is where intentionality comes into play, like, what is the goal of this visualization, what sort of audience does it need to reach, and what is the audience’s goals in terms of how they want to consume and interact with a visualization or a dashboard to meet their needs, and there needs to be a way to embrace that fuzziness in semantics, where there’s either clear directive, and we might get into this topic of using text with charts, because I think text is a very effective way of either enriching visual communication, or it could exist on its own, kind of, going back to what Bridget alluded to with the medium is the message.

We are exploring other types of media beyond just the traditional dashboard, and so, other forms of communication might come in to bear, and that is why intentionality is sort of the glue that helps the author with certain directors in terms of how the dashboard needs to be crafted to meet a certain goal, and also provides guidelines or scaffolds to the user or the interactor, so that they understand what the goal of that dashboard might be.

BC: And what’s really neat about this whole process is I was able to draw in interpreting models, and you think that you’ve got language transfer, and how does this relate to data visualization, but it really, really does, because to me, I see my work, not as, oh, this is so different from interpreting, but I’m actually interpreting from data through charts. I’m rendering a message that somebody else is designed to take, and that takes into account where it’s happening. So where is this message occurring? When it’s interpreting, it’s occurring at a doctor’s office, or maybe in a court of law.

When I’m doing data visualization, it may be occurring at a business, where I’m giving this to a high level executive, or maybe this is on a flat panel screen where people are walking by it daily. All of that informs how I create that message. So that setting, and that kind of place matters. And then, you’ve got tone, what is the intent or the tone of this, and you really want to set that mood, and we really care a lot about that. And you can do that by color, you can do that by arrangement, there’s all these – and that was my logic of dashboards talk was really how do you create that mood, how do you create that message, and that also affects looking at, am I putting this on a telephone. And so, I’m scrolling this way, and that affects interactivity. So am I using a mouse where I’ve got a lot more refined clicking space, or am I using my finger where it’s actually a really kind of fuzzy, not very specific space. All of that has to be taken into account.

JS: You have both, so far, used terms like evolution, the next tier, kind of, looking ahead, and I wanted to ask that your book, along with some others that are either just out or coming out, Nigel Holmes has a book that I think just came out, Jen Christiansen has a book that’s on its way – these books feel like they are the next evolution, or the next tier of the data visualization book, the visualization library, as it were. There are and always will be the needs for the intro books for the grammar, the language, like, we need punctuation, so where’s the rugged spot, and how do you push the boundaries. And maybe Vidya, we’ll start with you, like, do you view this book as that next evolution? I don’t want to say next level, because that’s not really fair, but next evolution in the DataViz library, the DataViz field?

VS: Yeah, I think there are few levels or layers of data visualization that we’re trying to pull on. First of all, at least, from my standpoint, data visualization is no longer a field just for academia. I come from research, and we often get into, you know, let’s run a perceptual experiment to assess how useful this chart is, and I’m not trying to discount that it’s not important. It’s absolutely important, but it’s not just that. And we have reached a point where there is so much of wealth of knowledge that practitioners have brought into the field with Bridget, and so many more, I’ve seen this particularly with the Tableau community that I have been part of, and there hasn’t really been much effort in trying to bridge the two worlds.

I feel like the academic community and the practitioner community, we do talk and care about similar stuff, we just have different ways of expressing it, and there are kind of different sides to that same coin, so to speak. And so, we wanted this book to sort of bridge those two worlds together, where we come together on these common topics, and we share these different perspectives. So that was, I think, the first step, because I feel like there’s a lot of books that are either skewed more towards the practice side or more towards the research side, and so, we wanted to help bridge that.

And then, the second aspect is, yes, I mean, perception is just one form of that equation, right? There’s obvious questions that people need to understand, you know, how do you actually discern different magnitudes of values, compare different values, when is the bar chart more effective than a pie chart or vice versa. But to us, I think the most interesting set of questions is when we actually think of visualizations as a form of communication, and with our kind of diverse backgrounds that both really consider visualizations as a form of communication, we really wanted to bring to bear that it’s a language.

And yes, the punctuation is important, but let’s not stop there, let’s try to come up with ways in which we can string words and phrases together and come up with actual sentences that help assign meaning to what we see, and how do we use icons and colors, and have – and really paint a very deep understanding of how visualizations work. And we have also moved to a place where we are thinking of other forms of seeing and understanding data, and it’s not just through visual form, we have chatbots, we have Slack and Microsoft Teams, where people are asking questions, and sometimes you may not need a chart, you may need text or a different type of modality to bring insights to people.

So we really want people to kind of understand the breadth and depth of the field, and provide some sneak peek into where the field is heading towards as we share in the latter parts of the book.

BC: And I want to pull on a couple of threads there, because there’s a broader data representation that we’re starting to hit into. And I’ve seen that term kind of mentioned by a few other people as well, so it’s not my term, so don’t – but when you think about data sonification, when you think about physicalization, and just being able to represent data in a myriad of ways, I mean, some of this is really, really old. We’ve done this for countless of millennia. But then, some of this is really, really new. And so, intersecting a little bit of that, to me, is also part of the conversation.

I do want to go back a little bit where Vidya was talking about kind of research and practice, and what I found for me, it’s like, in the practitioner community phase, we’re always looking at research as proof, like, well, I did this thing, and I want proof. And so, we see research as that definitive proof. And what was really cool is we were talking one time, and working on a chapter, and it’s like, for Vidya, it’s like, something from research rolls out into practice, that is proof. And it was just like this lightbulb moment for me, like, we really are two sides of that coin, and that was the beauty in working together is really being able to kind of see these things come together, really see how they play together.

And then, what’s been really fun for me, at least, is seeing the research projects that come from the book, so we’ve written this book, we already did one research into text and charts, and it came initially from something I thought was throw away. We had talked about this, I put a segment in the text and charts kind of chapter just about over-texting. And a lot of times what we do is we divorce the text from the visual, so you have this huge long paragraph, and then, a chart, and they’re separated. And it’s not really useful, and so, you just have this big block of text on a chart, and they’re not really playing together. And so, we broke it up, and we had an example where you could see the text in the chart, and then, when we did the research, we really didn’t find that there was a limit to the text. I mean, we were using line charts, and I do think that that has a potential effect, but we were testing how much annotation could we put on this thing before people said it’s too cluttered, and we never hit that point. I would say we were modest.

VS: Yeah, there was no notion of over-texting when we actually did the research, which was kind of interesting.

JS: Okay, so I want to make two points, and then, move on to another question. So first is I would be remiss if I didn’t mention the VIS Con workshop, which is one of the IEEE workshops, that is trying to do this bridging. So I’ll put that in the show notes for people who want to check it out, but there is, I think, there is this clear movement to try to bridge the gaps. And Bridget, to your point you just made, I mean, this is a point that I talked about with all of my people I work with, and all my clients, I mean, it is funny to me, when I talk to people about let’s make your chart title more active, let’s tell the story, the argument in the chart title, and they’ll say, especially government folks, they’ll say, well, we can’t do that, because we’ll be deemed as being partisan or not being objective. And I’ll say, okay, yeah, I get that, let’s not do that, but let’s see what you wrote about it in the report. And Bridget, just like you just said, like 99 times out of a 100, 999 times out of a 1000, the sentence in the report, in the text is the argument, and then, they move on to the next thing. And there’s still this separation, I think between the visual and the text, which is amazing to me.

BC: It doesn’t surprise me at all, because we actually have this exact same problem with interpreting, it’s like, oh well, but I’m not in the room. It’s like, no, you really are. And there’s this whole model from interpreting called demand control schema. And what it does is it acknowledges your impact on the message, and so, you’re not this neutral party, the whole myth of neutrality is just that, it’s a myth. It’s a story we like to tell ourselves, and console ourselves that, oh, it’s our way of exiting harm, and it’s not.

And so, you’re an active participant in crafting that message, and regardless of who you are, you’re in the room, and typically, neutrality is only afforded to certain types of people, and that’s the other thing that we’re not necessarily discussing. But you are an active participant, and you are shaping that analysis, and in the interpreting world, you’re responsible for crafting a message that people understand. I can remember very early in my career, when I was still an interpreting student, I was sitting with a friend of mine, an interpreter came in to interpret forum, and she was explained to him that they were going to do tests to figure out whether the tumor he had was malignant or benign. And she spelled those two words, she literally spelled malignant or benign, and when she left, he looked at me and said, what did she say. And I had my friend, we don’t know if you have cancer or not, but they’re going to test and find out. And that’s what we do with data visualization all the time, we take no ownership over the message, we simply pass through, and we do a disservice to our users, because we’re not helping distill that message. I mean, that is the goal.

VS: Yeah. Okay, so on text, so I want to read this for listeners, because I think this might be my favorite sentence from the whole book. So this is in kind of towards the beginning, okay, so you both write: charts are not intuitively read, instead, consumers rely on outside narration, expanded supplemental text, and numeracy to navigate what the visualization shows. I mean, I think this is so important, and so great, and I want to give you just – I mean, I don’t even know if I have a question here, other than maybe my question is, do you think that when people say, a chart should be instantly understood, or like the three-second rule, I’m going to guess you both think that that’s not true. I mean, I don’t think it’s true, because of this exact point about text. So I don’t really have a broader question here, other than just give you a chance to talk about the importance of text, you’ve already mentioned a little bit, so maybe we’ll start with Bridget, I see you’re like, you’re raring to go on this question.

BC: Yeah, it’s [inaudible 00:37:25] bouncing back and forth, so I have a few things to say about that. So charts to me are like classifiers, and in English, a classifier word is a word like bundle. So if I talk about a bundle, you have no clue what I’m talking about, you know I’m talking about agglomerate of things, but it could be a bundle of words, it could be a bundle of software, it could be a bundle of books, and you have no context for what it is. [inaudible 00:37:48] tool for expressing data, and we fill them with intent, and we use perception to help guide users through it. But you have to have the words to convey what that thing is, you have to have the numbers to really provide a sense of scale.

And so, that’s where, to me, charts are a form of classifiers, and I can take that one step further, American Sign Language, we have these classifiers, where it’s like, I can take a car, and I’ll make this what I call a three-handshape, my thumb, index, and middle finger all three out while the other two are closed, and I can drive this thing around. And I can either, if it’s a car, I have to tell you, it’s a car and provide some context that if it’s bouncing up and down, maybe I’m on hills. But if I just do this by itself, it’s not meaningful. I have to tell you this is a car or helicopter for it to have meaning.

And so, that’s kind of part one. The three-second rule, I have a lot of probably personal rants about that, because our communication isn’t that efficient, and we have this, you know, and that’s where you end up [inaudible 00:38:53] bar chart hell, where everything is a bar chart, because that’s the fastest thing to understand. But back to the kind of the thinking fast and slow methodology, you want to have people be able to dig in and have that deep dive, you want to be able to unfurl information. And when you think about text expositions, we’re not just writing five-word sentences all the time, you have to vary your sentence, like, you have to give people something interesting to chew on. So that’s kind of my quick version of the rant, and Vidya, please feel free to chime in.

VS: Yeah, I mean, in the research community, I would say that visualizations are often contrasted with alternative forms of representation, like, tabular forms or written descriptions. But in reality, most charts are displayed with some accompanying charts, whether it’s titles, annotations, or captions. And so, there has been an actual push within the research community that text should be considered co-equal to visualizations, and calling on researchers to devote more attention to readability and how do you actually integrate both text and charts in terms of their takeaways, users’ takeaways.

And so, there’s been kind of growing body of work that explores that role of text that plays in visual analysis, and, in fact, there have been studies that have shown that users don’t always prefer charts, especially in chatbots, they actually just prefer text; and with modalities like voice, I mean, there’s no form factor that affords for any sort of visual display. And so, coming up with really pithy ways of sharing insights about the data becomes very pertinent when the modality is not conducive for any sort of elaborate visual representation.

BC: Which also intersects with accessibility, I mean, making sure that when people are using screen readers, they’re getting an equivalent message, and this is another area where we’ve historically fallen flat. So it’s that modality and the more you kind of think multimodal, the more inclusive you make that message.

VS: Yeah, and it’s inclusive for everybody, yeah, so I think accessibility is one big piece. And then, our technology has been moving towards automated or semi-automated data narratives that either accompany these charts, or they’re just shared with readers on a regular basis. So textual description has shown to be pretty influential with respect to these visual components, and that’s why we decided to have a chapter dedicated just for texts and charts. And as Bridget mentioned, we had a paper that was presented at the IEEE visualization conference that really goes into further understanding when is text preferred over charts, and how do the various semantic levels of text influence both the readers takeaway of what they’re getting away from the data, but also their own preferences, you know, is it text just describing statistical features in the chart, all the way to higher level takeaways? So it’s a very interesting field, and I feel like we need to pay more attention to text, because it is a first class citizen.

BC: And what was really interesting about that paper is we found that certain levels of text worked better in certain locations. So it’s like, if you’re making a very general statement, that’s a great place for a headline; if you’re starting to know physical notes about the trend, so it’s like it’s trending up, or this is a peak or – and you’re talking about what transpired, it’s best to do that in place. And to me, this really aligns well with American Sign Language, if I’m talking about certain things, I’m going to tightly reference so that deictic referencing, I’m making a space for it, I’m pointing, I’m using a lot of close and space behaviors of this incident right here. And all of that helps foster that communication.

I want to hammer one point about text a little further, and that is that I had a couple of conversations on Twitter somewhat recently around I don’t have success deploying scatterplots, is what I saw other people saying, and I’m like, I’ve never had a problem deploying a scatterplot. But I always annotate less than more, or I provide additional contextual clues. And then, I’ll also supplement with additional charts, so that way, when people are hovering over this piece, they’re getting additional information about what it is. And all of that’s just, you know, it’s that land marking, it’s the, you know, am I truly going the right way and deciphering this in the way that I should.

JS: Right. We’re coming out of the Information is Beautiful award, and I was fortunate enough to be able to judge a couple of the categories. And the thing that came out for me this year was the writing around some of these longer scrollytelling pieces was just really not that good. And it just, I think reinforces your message here, which is, the text in and around the graphs is just so important, and it’s like another skill set that we as DataViz creators need to have.

VS: Yeah.

BC: Absolutely. And we need to provide voice for it.

JS: Right. I mean, this whole, like, three-second rule thing or whatever, however many seconds people want to put on it, you know, if I showed you a bar chart with five bars and no text on it…

BC: It’s meaningless.

JS: It’s meaningless, right, exactly. So there needs to be some text around it, and how much text and where you put it, depends on all these factors that you’ve been talking about.

VS: Yeah, and it goes back to the conversation metaphor. I mean, text is an effective way to ground the conversation. You need to provide context so that people can be successful, and they are having a conversation with others, and it’s a very similar metaphor.

JS: Yeah, it’s interesting the parallels between the way I think we traditionally think about what language is versus DataViz, which is a visual language, and maybe we just haven’t been thinking about it in kind of the, I don’t want to say the wrong way, but we haven’t really been thinking about it in sort of a, I don’t know, merged way.

BC: We’ve treated it as a pictorial representation, and it’s not. It’s a lot more nuanced than that. It’s got a lot more systematic capabilities. I mean, we’ve seen that as far back as Grammar of Graphics, and being able to formulate it so that you can construct a wide variety of visualizations. And so, to me, moving into the next step of how do you construct these so that multiple charts are working together to have that conversation, integrating intent because that intent piece, we’ve really underestimated. And then, we really underestimate the semantic systems that allow us to express that message.

JS: So we’ve been going for a while, and I feel like we could keep talking for a while. I do want to end on one last thing, because at the last chapter of the book, you provide, well, it’s across several pages, but you provide essentially a big planning critique type of grid. And I was hoping you’d talk just a little bit about what the grid is, and how you thought about it, because it’s different than some other ones I’ve seen out there, this is very binary. It’s like did you do this thing, yes or no. And I also am curious about how you’ve used it, probably, Bridget, I think the question’s sort of different for each of you, Bridget probably in your own work or in work with clients, and then, Vidya, if you’ve used these sorts of things in teaching, and how students have sort of reacted to that. So I don’t know who to start with, maybe – I don’t know, who wants to start about talking about the grid itself?

VS: Bridget would start with how the whole thing came about, and then…

JS: Okay, yeah.

BC: So for me, the book is it’s a big book, I mean, there’s a lot there. And, for me, sometimes there’s a challenge of how do you take something very conceptual, and put it into play, how do you do it, how do you make it work. And I love books like Switch by Chip and Dan Heath, where you can download a workbook, you can literally do this process. And so, to me, that was a part of my model, and then, I used to train and mentor interpreters, I did a lot of training. And so, I actually took some of the materials from certification exercises, and had actual training benchmarks, and I liked the very Boolean yes or no, or it’s not applicable in this case, so we’ve got this 108-point checklist, if you will, of, did you do this thing or not.

And if you give people a fudge factor of, well, maybe I did this thing, you end up with these really fuzzy numbers. And so, I didn’t want it to be a score, because, A, the score wouldn’t be the same, and I feel like it’s a false equivalency. I wanted to focus more on, yes, this thing was done, no, it wasn’t, and these are literally the things I can either go fix, or I, at least, need to have a reason for why I don’t think I should do it.

JS: Yeah.

BC: So it’s more of a conversation piece than a grading piece in my mind.

JS: Yeah.

BC: Now, I have, I will say, I have used it for grading, I have literally made it zero and one, and I have occasionally put in a 0.5 just to evaluate maturities of organizations, or to look at a workbook and say, this is where you are today, and this is where you can go, and particularly, highlighting certain sections. So we’ve gone in, every chapter has parts pulled from it. And then, we’ve also pulled in these triangles, so we have these landmark triangles calling out certain key points, and we’ve actually put it right in the tool as well. So you can go back and find the thing that it references. So it’s not just they pulled it out of a hat, this is real in the book, and truly, every point can be kind of tied back to something.

JS: And to your point, and then Vidya, I want to give you a chance to talk about too, but to your point about how a grid like this can be used, it’s like any other skill, right? You can use it at the very beginning to be like, did I do this, yes or no, to a more nuanced thing as you become more experienced and maybe your data get more complex or something, where there’s [inaudible 00:49:16] but yeah, I think you’re right on, Bridget, that you’ve been talking about this whole hour, it depends on who the audience is, depends on your experience, and all of these things that a grid like this, you can use it to help grow and you might not, you know, you start today as a data visualization person who just learned whatever tool or Tableau or whatever it is, and you use it, yes/no, and five years from now, you’re like, well, I’ve expanded this, I’ve grown it in very different ways, and now I use a scale, and I’m focusing on different things.

BC: And you can see it by section, which to me is what’s key, because you can trace it back to, I’m not using text enough, my cohesion systems aren’t there.

JS: Right. So Vidya, I wanted to give you a second here to talk about whether you’ve been using it in either research or teaching.

VS: Yeah, I think, I mean, actually in both. So in research, we actually took a bunch of these heuristics from the checklist, and we kind of appropriated that to how to evaluate dashboards for facilitating what we call kind of a cooperative conversation between the dashboard by itself and the user, and I hesitate to say, reader, because these dashboards are interactive and they’re non-static.

But most recently, I just came back from a wonderful trip to India, and I used a lot of the book as a textbook in class, where I taught 60 students, who had no background in data visualization, over five weeks. And I use the checklists for two main goals, one of them was, most of these students, even though they did not have any formal training or any class on data visualization, were familiar with charts, because of how prevalent these charts are, you know, as simple as looking at a map, or looking at the news channel where they see political information and trends.

So I really wanted them to understand how to even critique visualizations that they see around them, when do you trust a visualization, how do you understand it. So we used the checklist or a modified form of that, so that I could get them to kind of talk about the visualization and critique it in a way with some guidelines, some guardrails. And as we moved along the course, and as these students learned how to create their own visualizations, either using Tableau or D3, I got them to self-evaluate their own creations through the checklist, and given that they had done this exercise on other people’s dashboards, they were familiar with the language and the expectations of the checklist to try it on themselves, and some of them as part of their final group project, took existing dashboards, identified certain places where they could improve the dashboard, and then, reran the checklist on their improved dashboards to see if it actually swayed the needle.

And we found in general that, and I did this checklist of exercise even with practitioners out in the world beyond just students, and what we found was people were pretty good at understanding kind of basic graphic design when creating charts, because there’s so much prevalent literature out there, but the use of icons, the use of semantics across charts, thinking more deeply about color, thinking about the placement of text, the use of scaffolds to guide the user in terms of where they should look at, being more thoughtful about which charts should be made larger, and where they should be placed, I think were things that sort of came out when they looked at the checklist, which I thought was kind of fascinating.

BC: And what I like as a practitioner is a lot of times, in the practitioner space, we provide feedback on our opinion, what we like, and it kind of becomes I’m going to make you in my image, and from an interpreting standpoint, you don’t like that. I mean, you just, because you have a rendition, you have a reason for the rendition, and I like that this tool puts the control back into the author’s hands. It lets you think about this is where the system is struggling, and these are the things you can think about doing, but I’m not explicitly saying make this green, make that purple, do this thing, this thing needs to be the size. I’m empowering the author to make those decisions so that they can take into account their intent and the semantic systems they want to leverage for this particular rendition. You’ll notice I’m going to keep hammering the word rendition, because it’s not – and if you put seven chart makers together, or seven data visualization people, you’re going to have seven different renditions, and it’s not that one is right and six are wrong.

JS: Yeah, right. Wow, this was fantastic. Not surprised. Great book, love it. Love where you’re headed with this in terms of where you’re providing, I think, a resource and service really to the DataViz field to sort of help move us forward, in the way we should be thinking about DataViz. So Bridget, Vidya, thanks so much for coming on the show. Thanks for staying longer than we’d planned. I know I usually say, like, oh 25-30 minutes, we’ll just chat, but this was so interesting, I couldn’t stop this. So thanks so much for coming on the show, really appreciate it.

BC: Thank you.

VS: Thank you so much, it’s a lot of fun.

And thanks everyone for tuning into this week’s episode of the show, I hope you enjoyed that. I hope you’ll check out their book, Functional Aesthetics, great book. Also check out the website, and, of course, check out all the links in the show notes. There’s a lot of stuff in there, so go explore it. Take a look. And, of course, if you have a chance, go check out PartnerHero, the sponsor of this week’s episode of the show; and if you’d like to support the show, please consider reviewing it on your favorite podcast provider. If you’d like to sign up for the Winno app, you can do a free version, you can do a paid version; or if you’d like to support the show financially check out Patreon, PayPal, or any of the other ways that I am providing content and connecting with you. So until next time, this has been the PolicyViz podcast. Thanks so much for listening.

A whole team helps bring you the PolicyViz podcast. Intro and outro music is provided by the NRIs, a band based here in Northern Virginia. Audio editing is provided by Ken Skaggs. Design and promotion is created with assistance from Sharon Sotsky Remirez. And each episode is transcribed by Jenny Transcription Services. If you’d like to help support the podcast, please share and review it on iTunes, Stitcher, Spotify, YouTube, or wherever you get your podcast. The PolicyViz podcast is ad-free and supported by listeners. But if you would like to help support the show financially, please visit our Winno app, PayPal page or Patreon page, all linked and available at policyviz.com.

The post Episode #230: Vidya Setlur and Bridget Cogley appeared first on PolicyViz.

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Edith Young is an artist, designer, and writer from New York. Princeton Architectural Press published her first book, Color Scheme: An Irreverent History of Art and Pop Culture Through Color Palettes, in 2021.

This is the final podcast episode of 2022! I hope you have a wonderful, safe, and healthy holiday season. I look forward to good things coming in 2023!

Episode NotesEdith’s work: www.edith.nyc
Edith’s palette prints: www.edithyoung.com
Book: Color Scheme: An Irreverent History of Art and Pop Culture Through Color Palettes
PolicyViz blog post on color

Related EpisodesEpisode #203: Alli Torban
Episode #226: Abby Covert

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PatreonWinnoNewsletterOne-Time with PayPalTranscriptThis episode of the PolicyViz podcast is brought to you by PartnerHero. PartnerHero provides customer service outsourcing that’s built for the needs of small businesses and high growth startups. So if you’ve ever needed help with your data visualization work the way I do, sometimes I need someone to help me scrape some data or make a graph or edit a blog post, I use PartnerHero. They have flexible terms, they can help you scale quickly, there’s quality assurance baked into every product and every program, and they have offices around the world which can also help you with different languages and different nuance in your writing or your visualizations. So if you’re ready to bring an outside customer support to help your startup that feels like it’s part of your existing team, check out PartnerHero. Head on over to partnerhero.com/policyviz to book a free consultation with their solutions team. Mention you heard about partner here from PolicyViz, and they’ll waive the setup fee.

Welcome back to the PolicyViz podcast. I am your host, Jon Schwabish. On this week’s episode of the show, which is the final show of 2022, I’m very happy to chat with Edith Young. Edith has written this really fun book called Color Scheme: An Irreverent History of Art and Pop Culture in Color Palettes. We talk all about her background, all about her work, all about her excitement about color in art and in paintings. And then, we also talked about how she actually got all of the more than 500 colors out of the different paintings and into this book. It’s a really fun book, I mean, I’m not an art history person, like, I don’t have any training or really any knowledge about art history, as I admit to her straight up in the conversation that you’re about to hear, but I really did enjoy this book. It was really fun to go through all the color palettes, and go through all the color shades. And if you’re really interested in learning more about those color shades by the way, I’m going to have a separate blog post on policyviz.com that will give you the CMYK, RGB and hex codes for all of the more than 500 colors in the book. So if you have the book and you like the colors, and you want to use them in your work, I’ve got them all sitting for you elsewhere on the PolicyViz site, so you can go in and grab them.

So as I mentioned, this is the last episode of the show for 2022. I hope you have enjoyed all the various guests and conversations along with the show. I am so grateful that you spend every other week with me listening to folks working in the fields of data and data visualization, and design different authors, different designers, folks doing amazing work in and around the field of data visualization. And so, I hope you’ll enjoy this last episode of the show for this year, I’ll be back in January with a whole bunch of new great guests. So here we go, last episode of the year, here’s my conversation with Edith Young.

Jon Schwabish: Hi Edith, good evening, how are you, a nighttime broadcast interview?

Edith Young: Hello, yes.

JS: I don’t do these very often. It’s like post daylight savings, so it’s like dark outside.

EY: I know, it’s a little bit terrifying.

JS: It’s really not that late, but it feels like a nighttime episode – PolicyViz Podcast after dark – definitely a different kind of show. So I’m excited to chat with you about your – now, do you still call it a new book?

EY: It’s now a year old, yeah. So I don’t know what that…

JS: Yeah.

EY: Toddler.

JS: Not quite, right, just the toddler, right. Really interesting book, especially, I think folks who are into art history are going to get a ton out of it, which is not me, but I got a lot out of it and really enjoyed it, but I’m not an art history buff. So I think those were like artists are going to get a ton out of it, so I want to ask you a few questions about the book in the background, and then, I want to ask you a couple of technical questions about it. So maybe we could just start simply, just talk a little bit about your background and what led you to write this kind of book.

EY: Of course, thank you so much for having me. And I do not have a classic data visualization background, whatever that may look like. I work in art, in design, and I went to art school, which is important because that’s where this idea germinated. And so, basically, the sort of origin story for the book is that while I was at art school, we would have these long studio classes and these critiques, and so my friend and I would go to the local movie theater to blow off some steam. And we saw one day this documentary, I think it was from 2011 on Diana Vreeland, who was an editor for a long time at Harper’s Bazaar and at Vogue, and she had sort of a grandiose, larger than life personality, and said all of these very quotable things mostly about aesthetics.

And so, in the movie, they quote this excerpt from her autobiography where – I’m going to read it – she says – I wouldn’t dare misquote her – she says, “All my life I’ve pursued the perfect red. I can never get painters to mix it for me. It’s exactly as if I’d said, “I want Rococo with a spot of Gothic in it, and a bit of Buddhist temple” – they have no idea what I’m talking about. About the best red is to copy the color of a child’s cap in any Renaissance portrait.” And I thought that was very compelling in a sort of sitting in the dark theater, thinking about how it was both like incredibly inexact and sort of ludicrous sounding, and also kind of charming and true, and how those two things could be possible at once.

JS: Right.

EY: So then I was just sort of thinking about how you could sort of debunk and reinforce that idea at the same time in a color palette that draws from all of these paintings from the era that he’s talking about, all these Renaissance portraits, and thinking about how you designed out like a color chart, from Benjamin Moore [inaudible 00:06:21]. And so, I wrote that idea on my phone during the movie, and then put it away. And then years, years later into my school experience, I made this print of the reds of the red caps in Renaissance portraits. And I stayed up late, collecting all these paintings from that era, and trying to be very organized, and then, selecting these colors and putting them in this gridded array. So that was the beginning of the project.

JS: What drew you to that quote, was it this idea of trying to obtain, like, this perfect color, or was it the fact that there is no such thing, like, what drew you to it?

EY: Yeah, I think, I mean, in the print that I made, there are 20 examples of these caps that she’s talking about, and so, that, I mean, from that example alone, it’s not – what she’s saying is not possible, there’s no such perfect red. But at the same time, like, if you have a little bit of familiarity with the concept she’s talking about, you also know, you can kind of get a general sense of what you mean. So I think it was sort of the duality of those ideas, and holding them both in your mind at the same time.

JS: So, like I said, I’m not an art history buff, although, as I was telling you before we started, my mom is, and my mom was loving the book, because it’s like, it’s just – I don’t know how to describe it, like, the book, you kind of have this whimsical combination of these historical artwork, and then, let’s pull out this color, this color from each of these paintings, and I’m wondering, when you think about your reader, what do you hope that they get out of it?

EY: So I think it’s a book that can work both for people who are kind of art history buffs, and people who feel pretty intimidated by it. I think my inspiration at the beginning was someone like my brother, who is very smart and didn’t have like sort of the knowledge that I have about this subject, and thinking like how can I make it so that he would be very intrigued by it and find a very accessible entry point to jump in. So I think the palettes each have their own page, and can be enjoyed on their own as this isn’t very humble, but as their own piece of art I think. But ideally also it might trigger or pique the curiosity of someone who says, oh, I don’t really know what this is talking about, I don’t know why she’s talking about this painter, Wayne Tebow, and why are there the greens of the garnishes throughout this page. And so, then you look up his work, and that sort of puts you on a little bit of a goose chase to figure out how this theme has emerged in his work. And so, I just like the idea of, and I think color, you know, there’s so much levity to color in most cases. And so, it feels like a really nice way to segue into this subject matter.

JS: Yeah, it’s interesting, because the way I hear you describe it is if you were to teach a class, an art history class, then it sounds like the way you would bring people into that is through color and not necessarily through form or curvature or the different types of painting that seems like colors are so accessible for people.

EY: Yes, I mean, I think a good teacher would teach you all of those things, but I think that that is the angle that I found that I haven’t seen that’s existed elsewhere.

JS: Yeah. So how did you pick the spreads in the book, because there’s, I don’t know, about 20 or 30 different spreads, so how did you go through and pick those?

EY: I think there may be 40 palettes in total, and they’re very subjective, I mean, I would say that I think the whole project is fairly subjective, and I think that’s a bit of an interesting attention to me, especially, when it comes to data visualization, which I can get into a little bit in a bit. But I think that it started off, they’re really led by the titles of the work, so once you’re starting with reds of the red caps in Renaissance portraits, it’s a bit of a mouthful, but I think there is something a little bit, a little irreverent and humorous about it. And I liked the idea of trying to find these other things that are a little bit like punch lines or zingers when you see the palette itself.

So basically, I mean, this definitely started as a conceptual art project, and it was this system where I created these rules, and then I had to find things that applied within my system of rules. And so, sometimes that was like, if I were at a museum, and I saw a piece and it sort of gave me this idea, I wonder if there are more paintings like this, were they, I wonder what the wings of the angel and all the other annunciation paintings look like, or something like that. And sometimes, it was a little bit more obvious to me what someone might think was a little bit funny about an artist body of work. But yeah, I would say, definitely through a somewhat personal lens of art that I like or art that amused me.

JS: Okay, so let’s talk about the practical piece, because this is the question – my mom’s going to listen to this episode, and she’s going to give me a hard time about keep referencing her. But this is the question my mom asked, and it’s a question I had written down, so I can’t give her full credit – but how did you build it? Because getting those exact colors out of a painting in real life into digital print or into print, it’s got to be not easy.

EY: Yes, so it is and it isn’t, I would say, like, I consider the process of this work much more about the idea and the research, than, ultimately, the technical aspect, which I think is fairly simple, if you admit to yourself that it would be somewhat impossible to get the perfect hex code of whatever Bonacelli was painting with at the time. I’m in, when hex codes were not even a glimmer in his eye. When I’m working on one that has to do with art history, I try to work with these images that are from the museum in our as white balanced as possible, and directly from their art archive where they’ve been photographed with a gigantic Hasselblad camera.

And so, that’s about as far as I can get with accuracy, and then, from there, I would say, I’m mostly, I’m like, in an Adobe program, and there’s an eyedropper tool, and I’m using that and going in and once you zoom in too far, there are so many reds in this one cap, but I am looking for a red that feels the most representative, like, it feels like it would be sort of the average if you were to average all these colors together, and also, if you walked away from the painting, what you would think kind of resembles what you saw, if it were just in your mind’s eye.

JS: Yeah, oh interesting. So practically, it’s the Eyedropper tool that everybody has and their regular PowerPoint tool or whatever. But then this last part is interesting, so when you do that, and you find the color, did you kind of zoom all the way out and sort of have the painting on one side of your screen and the red that you chose on the other side of screen, just sort of like getting that feeling?

EY: Yes, I mean, definitely, sometimes it takes a few tries, because I don’t feel like I’ve accurately picked the right one. But yes, I’m looking at them both at the same time.

JS: And did you feel in that process that you engaged with the art and the artists in like a different way than when you’re just looking at it on the wall – because you’re diving in, it’s like, you’re like the Ferris Bueller movie in real life.

EY: Yeah, I mean, I think I do. I don’t know – I’m not sure that I have like a spiritual moment with the colors when I’m working with them, digitally. But I think often like the surprises that I find are more so, especially when I’m proved wrong by an idea, so there’s this one print that I referenced before that I worked on after the book came out, but it’s the Wings of the Annunciation, and that’s a biblical scene that has been depicted in art history, many times and is one of my favorites as someone who learned a lot about religion through art history. But I would say, I had this idea where I would pluck the colors from the wings. And then I sort of said, while I was making the book, it was on my list of ideas, and I said, well, it’s going to be all white, I’m not sure that’s the most compelling palette, I’ll move on to the next thing.

And then, I went to the Cloisters last spring, and I saw an altarpiece where they had Gabriel the angel and his wings, and it was a totally different color than I would have imagined. And so, then I went back to that idea, and saw there, like, there are kind of these groovy colors that everyone uses in that era, because they’re just, you know, it’s what they’re envisioned to be happening in that moment, and they can’t draw from a photograph or something like that. So they were like incredibly modern colors that they were using, and so, I think in those instances, I’m especially excited when it feels a little bit like a discovery made through the typology.

JS: Right, that’s really interesting. You’ve talked about a few that you really liked, but is there a favorites spread or a favorite painting or artist that you have?

EY: Well, one thing I think we haven’t really talked about is there’s kind of pop culture section at the end which is visual culture, and, I would say, there’s a real emphasis on sports. And so, one of my favorites is the spread of Dennis Rodman’s hair dye over the course of his NBA career. I mean, it’s the only one that’s two pages, there’s so much to draw from, and, I mean, I just thought that was so fun. But in terms of artists, I mean, it’s hard to narrow it down, but one that I loved especially is named Alex Katz. He is a painter who actually – his retrospective just opened at the Guggenheim, I haven’t seen it yet. And there’s a little bit of a nod to him in one of the palettes, which is of seascapes from the 20th century. And I would say, that’s one where I played around the most with sort of the visualization format, where most of the palettes throughout the book are in this square or rectangular composition in the grid. And in this one, there are these rectangles of the colors, and they’re organized – basically, they’re all on the same horizon line, but they’re in a different place, depending on where the seascapes horizon is in that painting. So that’s my little homage to him, because I couldn’t really figure out how to fit him in otherwise, he’s one of those paintings.

JS: Right. But it’s also interesting to link it back, as you were talking about earlier, back to DataViz, because you’re kind of taking color and matching it to a visual, you know, you’re using the color to actually create a visualization out of these various paintings. When you think about color, and you think about people making charts and graphs and diagrams, do you look at color in a particular way, when you see people using a shade of blue in that graph in the Washington Post, and like, ah, that’s not the right shade of blue?

EY: Yeah, I mean, sometimes, certainly with many things in the world, you think this could be more aesthetically pleasing. But I also think, they’re trying to get an idea across, in a way that is pretty clear. The thing that interests me about the way that these palettes do relate to DataViz is that the format makes it look, and I read about this a little bit in the book, but the format of a palette is sort of a very persuasive image, like, it looks very factual. It’s just sort of, like, I think it’s a fairly convincing visual, and I think that tension is interesting with this subject matter that is actually fairly subjective and very perception based. But I think it does make an argument for how DataViz can be such a strong kind of way of getting ideas across, and possibly a little bit persuasive, whether or not what it’s telling is true.

JS: Yeah. So when do you thought about organizing them aside from the seascape one, when you thought about organizing them, did you play with other layouts for some of the other paintings, or was it just pretty natural, I’m going to do this as a grid, because I’ve got X number of paintings, and it kind of makes sense to do it that way?

EY: Yeah, I think because they were mostly referencing the color charts, I think the paint charts that you would get at the hardware store, I kept them fairly in line with that. I’ve played around with it a little bit since then, but I think they still, they’re not far off from what you would pick up from your paint store. I mean, it’s definitely, the whole project has made me very curious about other ways of working with DataViz, so there’s definitely a lot of potential.

JS: Yeah, absolutely. So when you envision your core reader, and they’re sitting down with this spread, and they’ve got the paintings on the one side, and they’ve got the boxes of the colors on the other side, how are you hoping that they interact with that spread?

EY: I mean, I think, ideally, everyone wants someone to look at their work for more than two seconds, but that’s not always what happens. I think it kind of depends on the type of reader, but I do like the idea of it as a gateway of that – I don’t want to create a reading experience where you’re constantly going back and forth between your iPad and looking all these things up. But I think, kind of, making a list of these things that interest you, and then, looking them up later and kind of delving deeper into those artists and those artworks, definitely would be a win for me. Like, if it just made someone engage more with this thing that felt unfamiliar with them before.

JS: So before we wrap up, so you mentioned your favorite spread is the seascapes, but is Cats your favorite artist in the book, or do you have a favorite artist there as well?

EY: I mean, I would say, Cats is my favorite artist and that the seascapes is not my favorite palette.

JS: Yeah.

EY: Yeah, I mean, I love a lot of the artists in the book, but there are, I mean, a few that, I don’t know, Fernando Botero is one that has a really kind of like punchy tropical palette in the book, and he is known for – he’s a Colombian artist painter who’s known for painting people and objects in really exaggerated forms. It’s pretty recognizable – there’s a very popular meme of his work. And so, definitely, I would say, for the most part, the palettes all revolve around artists who I really like for one reason or another, yeah.

JS: I was kind of hoping you’re going to say Dennis Rodman is your favorite artist, because…

EY: He’s up there, for sure.

JS: He’s up there, yeah. This is great. It’s very cool. Edith, thanks so much for coming on the show. I really enjoyed the book, it was just a really nice read, just enjoy all these colors, and thanks so much for taking some time out on an evening to chat with me.

EY: Well, thank you. Thank you so much.

And thanks everyone, for tuning into this week’s episode of the show. I hope you enjoyed that conversation. I hope you’ll check out Edith’s book. I hope you’ll check out the other blog posts that I wrote, where you can go grab all those color palettes. If you would like to help support the show, you’ve got a little time now, maybe you’ve got a break in these last couple of weeks of the year. If you’d like to support the show, head over to your favorite podcast provider, put in a review, put in a rating. If you’d like to support the show financially, head over to Patreon or Winno, or you can even go to PayPal for a one-time donation to the show that helps me cover the editing and the transcription and all the good stuff that I need to bring this show to you every other week. So again, I hope you enjoyed this week’s episode. I hope you enjoyed the show. Thanks so much for listening. Have a great holiday season. Have a great New Year. And until next time in 2023, this is the PolicyViz podcast. Thanks so much for listening.

The post Episode #229: Edith Young appeared first on PolicyViz.

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Ethan Mollick is an Associate Professor at the Wharton School of the University of Pennsylvania, where he studies and teaches innovation and entrepreneurship. He is also the author of The Unicorn’s Shadow: Combating the Dangerous Myths that Hold Back Startups, Founders, and Investors. His papers have been published in top management journals and have won multiple awards. His work on crowdfunding is the most cited article in management published in the last seven years.

Prior to his time in academia, Ethan co-founded a startup company, and he currently advises a number of startups and organizations. As the Academic Director and cofounder of Wharton Interactive, he works to transform entrepreneurship education using games and simulations. He has long had interest in using games for teaching, and he co-authored a book on the intersection between video games and business that was named one of the American Library Association’s top 10 business books of the year. He has built numerous teaching games, which are used by tens of thousands of students around the world.

Episode NotesEthan’s UPenn Website
Ethan’s Personal Website
Ethan on Twitter

Dall-E Tweet
Google Drive folder with Ethan’s Dall-E images
Wharton Interactive
2022 Hugo Awards

Related EpisodesEpisode #121: Erin Hengel and Paul Goldsmith-Pinkham
Episode #4: Ben Casselman

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PatreonWinnoNewsletterOne-Time with PayPalTranscriptJon Schwabish: Welcome back to the PolicyViz podcast. I’m your host, Jon Schwabish. On this week’s episode of the podcast, I chat with Ethan Mollick from the Wharton School at the University of Pennsylvania. And you’re thinking, why am I talking to a professor at the University of Pennsylvania? Well, you might recall from a few months ago, if you’re in the date of his world. From a few months ago, there are a bunch of really cool images created from the Dall-E artificial intelligence tool that replaced on data visualization created by famous artists. And those were created by Ethan. And so my instinct was to reach out to him and talk about how does Dall-E work? What does it take? What do you need to do? And he wrote back and said, I’d be happy to talk about it, but there’s really not much to talk about it. Just kind of throw it in there and you just see what it does. And so that was kind of fun to hear that. So we do talk about his experiments with Dall-E, and we talk about that work, but we also focus on what he does with Twitter. And of course, Twitter’s going through its own changes. And we’ll see what happens with Elon Musk taking it over. But I find Ethan’s Twitter feed really interesting because he so much time summarizing academic papers. And so he’ll take a screenshot of different parts of a paper, including graphs and the abstract or some text, and we’ll summarize it for folks really quickly.

And so it’s really interesting from someone like me coming from the economics field, you know, writing and working in the academic literature. But I wanted to pick his brain about how academics could do a better job with the data visualizations in their academic writing. And so we spend most of our time actually talking about his Twitter feed and talking about some of the solutions that maybe academics can take part in to improve how they communicate their work. And we also talk about some of his other work at Wharton, including Wharton Interactive, which is working on the entrepreneurship space using games and simulations. Well, of course, that has a direct link to the field of data visualization and data communication. Now, before we get into my conversation with Ethan, let me tell you a little bit about this week’s sponsor of the show Partner Hero. Partner Hero is a outsourcing firm that’s built to meet the needs of scaling high growth startups. They offer flexible terms, they offer fast onboarding and the ability to scale your teams quickly. They have quality assurance baked into all of their different programs. They have offices around the world, so you can work in a variety of different languages, and they’re aligned with positive, accessible, equitable values so that you can make sure that you are not exploiting or taking advantage of workers from around the world.

And that’s what I really like about it the most. It’s also super flexible. It’s built for the needs of startups in particular. So that allows you to scale up and scale down really quickly. It also has a really fast onboarding process, which of course is going to be helpful for those of us who don’t have the time to really be going through all these different processes with invoicing and contracts and all of those different things. So if you are a small business in particular, or a freelancer, if you’re ready to bring in outside customer support to help your startup and feel like those folks are a part of your team, check out Partner Hero, head on over to partnerhero.com/policyviz to book a free consultation with their solutions team. Mention you heard about Partner Hero from PolicyViz, and they’ll waive the setup fee. So that’s partnerhero.com/policyviz. So here we go. On this week’s episode of the show, here’s my conversation with Ethan Mollick from the Wharton School at the University of Pennsylvania. Hey Ethan, good afternoon. How are you?

Ethan Mollick: Excellent. And thanks for having me. I’m excited to be here.

JS: Awesome. A beginning of the semester for you?

EM: Yes. I’ve got a couple weeks left till the next quarter kicks in and I start teaching, but it’s certainly on my mind.

JS: Wow. Quarters. Quarters must be hard. I feel like quarters, like once you, like, it’s like flying from like DC to Philly. Like you, you kind of don’t even get up to cruising altitude. You just kind of like…

EM: Oh, I kind of like it. Like, you get, you get, it’s like delivering like a, it’s like a short set right? For a college. I got, I got 12, 12 sessions, I got to deliver. Type 12.

JS: Type 12. Right, right. So I reached out because you did this really cool thing with this Dall-E artificial intelligence tool that came out with some data visualizations, which I have actually, by the way, a couple hanging on my wall. Because they’re pretty cool. But I thought maybe we’d start just by talking about what you’re working on. You’ve got some really cool initiatives going on at Wharton and Penn. So I thought maybe we’d start with that and then talk about some of the other things. So do you want to maybe give folks just like your quick, like bio little background and what you’re starting the semester with?

EM: Sure. Yeah. So I’m a professor of innovation entrepreneurship at Wharton at the University of Pennsylvania and trained as an economic sociologist. And I teach a lot of the intro courses, or at least I did in entrepreneurship. And I’ve been thinking a lot about how do we trade these kind of things at scale and how do we do teaching at scale. So I’ve been really super interested in teaching, especially how do we kind of teach in new ways, reach new people, teach the kind of lessons like data visualization lessons that are contractually hard to teach. And, you know, really been thinking about this a lot. I have, you know, a mass violin course of MOOCs, these kind of Coursera courses. 400,000 people have taken the MOOCs that I’m part of, but they’re still kind of watching videos of people talk, right?

JS: Yeah, yeah.

EM: So there’s something, that’s ultimately frustrating about. So a lot of my effort has been into launching something called Wharton Interactive, which is a game studio at Wharton, effectively that tries to teach using games and simulations and to try and do teaching at scale with all the instructional pedagogy built in from the beginning. So that’s been a lot of my time on that, although also as you’ve noted, a pretty avid Twitter user and somebody who’s dipping my feet into things like AI generated images and stuff. So having to discuss any of those things.

JS: So tell me a little bit about the Wharton Interactive. So is this for business school students getting them training that they’re not getting in the core courses? Or is it those core courses, but trying to develop a way to do it at scale?

EM: So it’s a little of both. There’s a phrase in, in Silicon Valley eating wood zone dog food, right? So I experiment on my students that tell them that, hopefully…

JS: Yeah, hopefully they won’t listen. Yeah.

EM: Like, I want your podcast succeed. So hopefully they listen, but then are tolerant.

JS: Right, right.

EM: But, no, I mean, I have been running experiments. I wrote a book on games and education. I’ve been doing it for over, wrote that like over 10 years ago. And so I have a class that’s basically by experimental, a 100% games class. So from a teaching perspective, it’s great because I just sit there, and my students play games, my games the whole time. So the intent is that there’s a few things that motivate this. One of them is like, we actually know a lot of pedagogical science just as professors. We don’t really, we’re not really taught in ourselves. But there’s actually a lot of like, we know how to make people remember stuff and do stuff. So part of its baking that in. And then part of it is like experiential learning is great, but has project based work has all these weird outcomes, right. And you probably see this yourself and certainly anyone who’s teaches those, like a team goes great. Like I’ve had, I think people have from my class and the class taught my colleagues at Wharton over the last 10 years, people have raised like the entrepreneurship classes, like $2 billion in venture capital. Great, right?

JS: Yeah.

EM: Except that those are the projects that have really succeeded, right?

JS: Yeah.

EM: And for every successful projects, like one, the team goes bad. So they like, how do we give people project based work where the projects are always going to be interesting and where failure is interesting and where they all have the same kind of experience rather than hope that the, you know, the gods of teams and projects work out on their behalf. So that’s another big motivating factor. And the third one is democratization, right? So there’s all this evidence, and I know the same stuff happens and, you know, in thinking about things like, you know, being a good statistical physical thinker, but just small amounts of statistical education, business education make huge difference in people’s lives. So, you know, there’s these great studies, one that really motivates me. There’s a study Uganda done by the World Bank that’s a randomized controlled trial. Oh, this is my Twitter feed by the way, of people want sites. But that took a randomized sample of high achieving high school students in Uganda and put them through a three week entrepreneurship course, sort of the equivalent of what we’re doing in the game. And afterwards, three years later, they were like 10% more likely to launch ventures, you know, 12% more likely to employ people, had 18% higher salaries, like little bits of intervention at the right time, do the right thing. And like, it’s great, I got really talented people coming to be Wharton, but how do we get blow up in the doors and do this around the world? So it’s those motivating factors of like, games teach in a way we can’t do otherwise. We could build pedagogy in a way we couldn’t do otherwise and we could democratize. So those three things are really motivating me. And anyone can play these things. Like some of them are free and you can play them down, play them online. Some of them have, you know, the standard kind of charges that we would charge much less than a textbook, but, you know, charges says doing and so on.

JS: Right. Yeah. So can you give folks a sense of what a game might be as part of this?

EM: Yeah. So it’s interesting. We’ve been playing a lot with the philosophy of this. So let me give you an example of the three quick examples. Okay. So one example is a mindset game. We want to give people an experience doing something they may not otherwise do. In this case, it’s actually data analysis and coding. So we actually partnered with Evite and you, in this game, it’s a light fix. You play as a consultant who has to help. There’s like an hour countdown, someone’s on a plane and has to do a big presentation, but you’re actually given 3 million lines of actual Evite data and you actually code in Python in the game and get all this kind of assistance to solve a bunch of problems, and do, you know, statistical analysis and things like that. A second game is a, is you, one where you actually run a startup in real time over the course of three weeks. And we filled the internet with fake information about the technology that doesn’t exist. But you do everything from negotiate with customers, develop prototypes, and again, it’s all simulated. Like we built fake Gmail, fake slack, fake Zoom calls. We have actors that appear on these Zoom calls and, you know, it’s a whole all at interactive. And then we’ve realized that there’s some value in completely fictionalizing the setting. So we have a game set in 2087 on a dub space mission to Saturn, where everything is going wrong. We’ve worked with the Disney imagineers escape room designers. I got help writing for the guy who won the Hugo Award this year for science fiction, which is like the Oscars of science fiction for the non-nerds out there. And so, but it’s really, it teaches you strategic, organizational, individual leadership because the lessons are exactly marketing lessons, you know, statistical lessons and things like that. So it is really attempt to try and do a lot of different things, not just my stuff, but we’re working with lots of other professors to teach these things. And try to come with a method of doing it as well.

JS: Got you. And do you foresee, or can you foresee how it might be used in other disciplines? I mean, I think the way you’ve described it seems very business, math, economics, you know, startup, that sort of thing. But have you started sort of pushing the boundaries and what it might look like for other disciplines?

EM: Very much so. I mean, we can tell any story, right? It’s interactive fiction engine. And the whole idea of it actually was to move away from the mathy piece because there’s lots of mathy simulations up there where it’s like, what spending do you want to spend? We’re making 4%, 4%, 5%, and then Excel spreadsheet or assist dynamic models chugging our number. That’s not how the world works. What happens if you increase our R&D spend by 4%? The head of R&D is going to email you and say, why not 6%? The head of sales is going to email you and say, well, you fool, you’re doing this. Like, that’s the interesting piece. So the whole idea is we built like this fake inbox so we can play all kinds of games where you literally are getting messages from people. We also have all the stuff, if you want to run a fully interactive picture game, we want to teach the Odyssey by actually putting you on board as one of Disney’s crew members trying to desperately convince him to not listen to the sirens. Whatever you want to do, we can build those kind of settings. So the intent is to build around that humid interaction rather than just the math piece.

JS: Right. That’s very, very cool. So let’s switch gears a little bit because a few weeks ago, months ago now, you put out this, I guess, collection of data visualizations rendered by the Dall-E artificial intelligence tool. And so I’m not going to ask you to go into all the gory guts of how Dall-E works. But I guess I’m interested in why you decided to do that, how it worked, from your perspective, just what seems to be kind of a more casual user and were you surprised at the reaction that you got on Twitter?

EM: Yeah. So I mean, there’s a lot of interesting things. I used Mid Journey, which is basically a Dall-E, a different Dall-E. They’re all kind of the same. I just, this one I got access to more easily. So I used that. You know, I study technology also, and there’s always these sort of false starts and advancement in fields, right? People get really excited, you know, self-driving cars are going to be there, whatever the new technology is, right, you know.

JS: I’m still waiting for my hoverboard, right? From Back to the Future, right? Yeah.

EM: But there are moments where things are accelerating and you really should be part of it, because what they’re doing is not just the technology. There’s no threshold we have to reach. It’s fundamentally changing, I think, how we get to interact in an interesting way. And the suite of sort of AI meets human technologies, right? So there’s a whole bunch of things that generate, I have a colleague at Harvard who demonstrated a couple of fake Harvard cases that he had a different system generate, and they’ve, like Harvard cases, they were reasonably sensical in the same way. You know, I, you know, I really enjoy art data visualization, you know, I’ve got graphics, you know, in my papers. I just am not that good at it, right. I’ve spent time over stata, desperately trying to tune, you know, something. So I get the right, I’ve read all the books, I’ve got books on fonts, I’ve read your stuff. Like I am, you know, I’ve, I’ve like, I’m like, oh, this is so clever. I don’t have the full chops to pull that off, right. But suddenly here I can, it’s a different thing. It’s a medium where I can write something and have it happen and tune it with words. Fundamentally expanding how something operates, right. Using a human vocabulary to kind generate things. And I think that, you know, it was interesting that it took off. I think that there is this hunger for seeing different ways of visualizing seeing the world, right. Its why, you know, I’ve got a, like you have a decent Twitter following, right? If you look at the tweet that go most viral or not the academic papers or anything else, those do fine. But it is any time there’s anything with a visualization, right? That is literally what makes a Twitter tweet go is a visualization. The more understandable the visualization is, the more likely it succeed. And of course, drives me crazy that academics refuse like myself, honestly. But we refuse to kind of put the good visualization there. We don’t have the money, the time, the stuff to do that. But now we’re, we’re on the cusp of something new. So I think the idea of seeing something visual and you could, the set of styles that I was able to, you know, do 24 styles or whatever in the course of, you know, playing with on and off over the course of a day. And they represent something that’s fundamentally different than what we’ve seen. You know, they’re aping the style of Mondrian (ph) or whatever, but it’s not, there is something that a human wouldn’t have necessarily come up with any of these, right.

JS: Right. And you don’t need to dive into Illustrator and do it pixel by pixel, just let it go.

EM: It takes seconds, right. Like I’m just literally saying, you know, chart, you play with a little to get the numbers working. And I’m not good at this. Like I’m a naive person writing this stuff down. Like there is a pseudo code language you can apply, but who cares. It works, right. And I think there is something so exciting about that, and I think anybody who is interested in the visual space at all, in the policy space, in the data space needs to spend an afternoon playing with these tools. I just can’t emphasize enough that there is something really transformational there. And even if you bounce off it, you won’t regret it, I think.

JS: Yeah. So that leads to the other part that you already sort of alluded to, which is your Twitter feed. You spend a lot of time summarizing academic papers, which can’t be easy, especially because you have to read some or most of them, which can’t be easy. But I guess I’m curious about, as you mentioned, like your thought about the graphic space in, in academia and is the reason why the graphs aren’t better is because there’s just not enough time, there’s not enough skill, it’s the editors, it’s like, is it just the whole system?

EM: So the short answer to all of your questions is yes, obviously, like it’s all these things, but I mean, I mean there, you know, look, as academics, there is a disdain for public, you know, interaction that is not actually like, there’s a purity argument. And my mentor, Ezra Zuckerman has talked extensively about this at MIT, you know, there is a desire for purity in our field, right. And there is something impure necessarily about talking to a general public that, you know, again, you could cross over that line. You could be one of the people who’s, it’s okay to do it. But there’s no doubt that as a junior faculty member, you’ll be warned away from doing that distraction. And the same way, right. There is a, you know, there’s this, there’s this theory I think about a lot middle status conformity, which is that if you’re competing with people, you’re better off if, if you want to maintain middle status to look like everybody else. Violating those norms is a way to get either elite or get punished, right. That’s what elites have punished. The elites who get away with it, they can violate a norm or not. So in the same way, like, you know, I want my paper accepted, so I’m going to use default data graph or autographs to do this. And maybe if I’m really fancy, I’ll change the background color, but like showing them any more time than that to do that or like, you know, is an indicator of a lack of seriousness or indication, right. So what ends up happening is the only good, really good graphics come out of, you know, some of the, you know, like science or a few other places seem to have graphic designers who help out with these things, you know, or graphical, you know, abstract. But the result of something on Twitter, I’m sure you’ve seen the same thing, is that if you want something graphical, that huge makes a huge difference. And the graphs that people tend to have that are most visible are scatter plots. And scatter plots are in many ways the worst graphs to show because amateur critics attack them the most. It lends itself to bad statistical analysis, right? Because a scatterplot is not the same thing as a controlled OS, right, regression analysis. So, you know, so I think that there’s some very simple ways to improve this and there’s some more complex ways, right. Simple ways, anything showing magnitudes, right. Any of the graphs that show confidence bands above and below zero for effects, you know, with a table of effects. But I just would beg people to do this. This stuff matters. Like, I now have, it’s been really funny, I went to the big academic conference for the Academy of Management this year and, you know, I’ve never been close to pseudo celebrity before, but people are like, oh, I read your Twitter feed. Like people clearly care about this. They send me articles they’d like to see, tweet it out, this little bit of extra work though they’re not willing to do. So that’s my feeling on the graphic side. It’s like it is way under counted the difference between a good one and a bad one. And when it gets noticed at a place like Twitter or somewhere else, they get more citations. They get press reach outs, it makes a difference.

JS: Right. So there’s definitely like a line of research there, right? Like just the way you said is like to actually quantify the impact of having better graphs. Of course you have to sort of define that in some way. But if you were the editor of some journal, say Ethan’s Journal of Management, what would your first step be to make the graphs in the articles in your journal better?

EM: So I think there’s a lot of halfhearted pushes to do some of this, right? I think first of all, you need one graph that communicates your, your key point, right? Like, you know, and I proposed this before, but like, you know, there’s a few, you know, heroic graphs that do that, right? There’s a famous study on the price of wholesale fish. I don’t know if you’ve seen this, the current nature of economics, again, I’ll put the link so people want to do this. But it shows the fish prices before and after cell phones were implemented in India, it’s really about, you know, coordination and pricing. But the chart is like up and down, up and down. And then suddenly the instant cell phones, the chart becomes completely flat, but all the fluctuations disappear. Instantly get like, oh my God, I got this, right. Like there these kind of graphs, you know, some of the graphs on income inequality, you’ve got them. So, you know, having something, spending the time to think what’s, you know, often it involves taking something attorney into an order of magnitude that matters to people or an impact. If I take, people are already using back of the envelope calculations, you know, this would cost $20 billion, this would save 2000 lives. There needs to be a graph of that. The so what moment as opposed to just a graphical check on what you’re doing, which is also important, but it’s important to recognize that these are persuasive arguments. We’re not wrong. We don’t feel better about persuading through our theory section. We don’t feel bad about persuading for evidence. We should not feel bad about having a persuasive graph and arguing, it’s a persuasive graph.

JS: Yeah. It’s always shocking to me when I argue to folks, you know, make your, the title and your graph active and tell people what the argument is in the graph. They’ll say, no, no, we can’t do that because of this, that, and the other. And I’ll say, okay, well let’s see what you’ve written in the text and maybe we can find like a middle ground between the descriptive title and the active title. And 99 times out of a 100, what’s in the paper, what’s in the report is, you know, that active statement that’s making an argument. And somehow there’s still this break between the visual piece and the text.

EM: People are scared of it in a way that it’s a real problem, right? This should be the moment that you are able to kind of show why this matters. And so what I think a lot of people know what that graph would be. And I think maybe having a special way of labeling it, right? Like this is my, you know, persuasive inclusion, right, in some way or another, because you know, they do that secretly through other graphs. You’re only showing the ones that are really showing what you want, what you want to show anyway, right? Like, it’s not like those are not selected out a set of graphics.

JS: There’s all bias on there somewhere at some point. Yeah.

EM: And then there’s the self-sabotaging stuff, the people like acronyms in your graph, like just the worst part like, you know, and this is CBR6 compared to CB4735. You’re like, you know, and then I end up. I mean, so there just is this, you know, and, and I think people get caught up a lot on visual niceness, which is I think, important, but I think you would do just a lot with like, what are the two things that matter? What are the dependent, what’s the dependent variable? What’s the variable that matters? You know, and you’re allowed to be straightforward about this, show me your 95% confidence interval. Like, do that, you know, all that’s great, but like, show me something that is interesting or shows change.

JS: Yeah. So let’s bring these two pieces together. So when you think about the Wharton Interactive and the simulator and the games, do you see, or maybe you’re doing it now, but do you at least see sort of training for future academics to help them think more? I don’t even want to say more visually, because it’s not just the visual piece, it’s how people write as well, right. But do you, but do you help them think about those sorts of pieces in their research?

EM: I mean, I think that the idea is thinking about how you apply stuff and what lessons people are really trying to learn outside of purely academic framework, what we call clear closing the theory practice gap, right? Like we do it in our classrooms who are often afraid to do it to the outside world. So part of what we do is help people tell stories, literal stories, right? So we’ll meet with, you know, an academic, it turns out that when they’re telling us something really important about the world, but they’re kind of afraid to close that gap. But if you put it into a story format or similar like a graphical format, suddenly this stuff becomes apparent, right? Like put people on the grounds of trying to, you know, and the great thing about games, just like graphics, is you could straight the ground wrong. Like I could set up a situation where your research matters and make it clear, these are boundary conditions where it matters. But now I’m in a world where I wish I knew that paper. I could put you in that world, right. Where it matters that, you know, that result or that answer. Where you can play with alternative outcomes, another thing that we do that actually we’ve done graphically in the game is like what, you know, actually letting people modify projections. And you can see the effect on a long term graphic. The world is advancing very quickly like that world of interactivity and graphics and, you know, we have so many other problems with how academic papers are formatted and published and KOLs and everything else, and, you know, just to refuse to take it the advance what’s happening here of storytelling and like, it’s a problem and it’s a growing one.

JS: So do you think, you had mentioned earlier the danger of junior faculty being that different person doing, you know, the better grasp, but do you think that junior faculty or, you know, people who are coming into the academic field in the next few years, that they will ultimately be behind if they’re not thinking in, in the ways that you’re arguing more visually, better writing, you know, thinking about broader audiences?

EM: I mean, I think they already are, right? Like I think the issue is it’s already a two-sided game, right? People may frown on, you know, on Twitter engagement or something else, right? And I dread the how do you have the time? It’s because I an academic who just gets easily bored during things and that reads a bunch of stuff. I actually find everything we do interest. Like there’s so much good work out there. Like that’s the thing I’ve learned so much good work, right? I mean, I could show you all the stats and now we’re drowning in science, but like I’m amazed, and that’s not even counting working, but like there’s so much good and important work out there that nobody will ever read or care about, right? And so it already is that, look, if you can get, you know, we may not like it, we may not agree with, you know, but like, you know, we may not tell people it’s true, but if you get a New York Times piece covering your research, it’s going to get cited more, people are going to pay attention. You might have to deal with some jealousy, but like, it matters, right? But we tell people it doesn’t matter, but it obviously matters. So now people have to play a two-sided game. Where on one hand they have to say, I don’t really care about what people think of this. I’m purely interested in the life of the mind. And then on the other hand, you know, they’re trying to, you know, you’re trying to putting this together in an honest way would make sense, right? I was just, you know, um, reading about the late 15th century scholarship and there were all these traveling scholars who would, you’d try and get hired by patrons, right? In Europe to, you know, teach their children. That’s how you’d make all the money. So you had to become prominent to do that. The only way to be prominent was to pick very public fights with other scholars. So that way you’d be noticed or be like, oh, he’s, you know, he is controversial, than they hire you. And I feel the same sort of stuff happens. Like there is advantage of being picked to being prominent and being noticed. And I think acknowledging them, and it starts with graphics, right? It honestly does, like graphs are the persuasive connection between the general public and academic work.

JS: Yeah. Love it. Love it. I’m with you. We’re fighting the same battles. I love it. Ethan, thanks so much for coming on the show. I feel like we covered a ton today. I really appreciate it.

EM: This is fun. I can’t wait to keep reading your stuff, which I always think is awesome. And, you know, and also by the way, some of your exercises are like, you know, on teaching the stuff is great and I just, everyone should check you out if they haven’t. It’s terrific. I’ve thought about it a lot, especially, you know, introducing these concepts is wonderful.

JS: These games. Yeah. Yeah. That’s great. All right. Well, thanks again. I really appreciate you coming on the show.

EM: All right. Thank you. Bye-bye.

JS: And thanks for tuning in to this week’s episode of the show. I hope you’ll check out Ethan’s website. Check out the simulation tool that he has Wharton Interactive, really interesting work there. Check out his Twitter feed, especially if you’re interested in academic research. And maybe there’s some ways that you can help folks improve how they communicate their data visually. So until next time, this has been the PolicyViz podcast. Thanks so much for listening. A whole team helps bring you the PolicyViz podcast, intro and outro music is provided by the NRIs, a band based here in Northern Virginia. Audio editing is provided by Audio editing is provided by Ken Skaggs. Design and Promotion is created with assistance from Sharon Sotsky Remirez. And each episode is transcribed by Jenny Transcription Services. If you’d like to help support the podcast, please share and review it on iTunes, Stitcher, Spotify, YouTube, or wherever you get your podcast. The PolicyViz podcast is ad free and supported by listeners. But if you would like to help support the show financially, please visit our Winno app, PayPal page or Patreon page, all linked and available at policyviz.com.

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Richard Brath is a long time visualization designer, researcher and strategist. At Uncharted Software, Richard focuses on the creation of high-value visual analytic applications that solve real-word problems in capital markets, supply chain and healt-care analytics. These solutions in use by...

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Danielle Alberti is the data visualization editor at Axios. She was previously a front-end web developer at Pew Research Center and is a journalism and anthropology graduate of the University of Colorado at Boulder. She worked her way through nearly...

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Aliza Aufrichtig is a graphics and multimedia editor at The New York Times. In addition to covering the coronavirus and elections, she designs and develops stories that demand a bespoke form, often with audio and video. She’s created and maintains...

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Duncan Clark is a co-founder of the data and storytelling tool Flourish, which is now part of the Canva family. By background a data-driven author, journalist and publisher. In this week’s episode of the podcast, I talk to Duncan about...

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Michael Friendly is a Fellow of the American Statistical Association, a Professor of Psychology, founding Chair of the graduate program in Quantitative Methods at York University, and an Associate Coordinator with the Statistical Consulting Service. He received his doctorate in Psychology from Princeton...

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Julie Terberg is the founder of Terberg Design, a creative studio focused on crafting presentations that better communicate with audiences. With decades of experience in the presentation industry, Julie has trusted partnerships with other presentation professionals and valued clients around...

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Dr. Claire McKay Bowen is a principal research associate in the Center on Labor, Human Services, and Population and leads the Statistical Methods Group at the Urban Institute. Her research focuses on developing and assessing the quality of differentially private...

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Philip Bump is a correspondent for The Washington Post based in New York. He largely focuses on the numbers behind politics and he is the author of the weekly newsletter, How To Read This Chart. In this week’s episode of the...

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Karla Starr is a columnist for Medium and write the newsletter The Starr Report on Substack. She has appeared on NPR and CBS Sunday Morning and has written for The Atlantic, Slate, Popular Science, and The Guardian. She won an award for the Best Science/Health story from the Society of...

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Making Numbers Count: The Art and Science of Communicating Numbers

Made to Stick: Why Some Ideas Survive and Others Die

Switch: How to Change Things When Change Is Hard

Can You Learn to Be Lucky?: Why Some People Seem to Win More Often Than Others

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On this week's episode, I chat with the four founding members of the new data visualization mentorship community, Elevate Your DataViz.

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Fullstack D3 and Data Visualization: Build beautiful data visualizations with D3

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Dr. Cedric Scherer is a graduate computational ecologist with a passion for design. In 2020, he combined his expertise in analyzing and visualizing large data sets in R with his passion to become a freelance data visualization specialist. Cédric has...

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Jock D. Mackinlay is the first Technical Fellow at Tableau Software. He believes that well-designed software can help a wide-range of individuals and organizations work effectively with data, which will improve the world. He is an expert in visual analytics and human-computer...

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Dr. Tyler Morgan-Wall visits the PolicyViz Podcast to talk about 3D and animated 3D in data visualization.

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Ken and Kevin Flerlage visit the PolicyViz Podcast to discuss Tableau and some of the challenges and successes they've had, and how you can go about using the free materials on their website.

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This episode of the podcast wraps up 2021. I’ll return in January with all new episodes from the world of data visualization and presentation skills. I hope you have a healthy and happy new year! Frank Elavsky is a software...

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Tom works at RStudio, and is obviously very active in the R community. He runs the TidyTuesday project and developed the Grammar of Tables package in the R programming language.

The post Episode #207: Tom Mock appeared first on PolicyViz.

Show Me the Numbers: Designing Tables and Graphs to Enlighten

The Grammar of Graphics (Statistics and Computing)

Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

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Zen Faulkes, author of Better Posters visits PolicyViz Podcast to talk about creating an infographic or presentation slides or data visualization. Even though it's specific to this one part of data communication, I think there's a lot going on here that a lot of us can learn about data communication. So we talk about Zen's background, coming into this poster area. He talked about his interest in design, working with data academics, what it's like to actually be in a conference poster session if you've never been to one. It's quite an interesting experience, and Zen will talk a lot about that in the interview today about what it means actually to sit in this huge room of so many different people talking about their content.

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Better Posters: Plan, Design and Present an Academic Poster

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In this week’s episode of the podcast, I’m playing the recording from the opening moderated panel discussion between myself, Jen Christiansen, and Steve Franconeri at the 2021 VisComm workshop at the IEEEVIS conference. We (the workshop organizers) asked Jen and...

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Rebecca Pazos is a mum of one and a senior data journalist with The Straits Times for six years. Recently, she completed her Masters in visual tools with the University of Girona. She is dedicated to telling compassionate, human-centered data...

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https://policyviz.com/wp-content/uploads/2021/09/PolicyViz_EpisodeNo203.mp3

The post Episode #203: Alli Torban appeared first on PolicyViz.

Learning to See Data: How to Interpret the Visual Language of Charts (The Data Literacy Series)

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Lindsay Betzendahl is a Tableau Zen Master and Tableau Public Ambassador, join us for her visit The PolicyViz Podcast.

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Visualizing Health and Healthcare Data: Creating Clear and Compelling Visualizations to "See How You're Doing"

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Leland Wilkinson is Chief Scientist at H2O and Adjunct Professor of Computer Science at the University of Illinois Chicago visits The PolicyViz Podcast to discuss The Grammar of Graphics, coding, and much much more.

The post Episode #201: Leland Wilkinson appeared first on PolicyViz.

Visualizing Time: Designing Graphical Representations for Statistical Data (Statistics and Computing)

Visualization Analysis and Design (AK Peters Visualization Series)

The Idea Factory: Bell Labs and the Great Age of American Innovation by Gertner, Jon 1st (first) edition [Hardcover(2012)]

Semiology of Graphics: Diagrams, Networks, Maps

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In the 200th episode of the PolicyViz Podcast, we discuss the recent Do No Harm Guide about taking a racially equitable lens with your data.

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Associate professor in the School of Computing at the University of Utah and a faculty member in the Scientific Computing and Imaging Institute visits the PolicyViz Podcast to talk about the Visualization Design Lab.

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Author and speaker Scott Berkun visits the PolicyViz Podcast to talk about his new book How Design Makes The World.

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Data journalist Casey Miller visits the PolicyViz Podcast to talk about her work at the Los Angeles Times.

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Francis Gagnon, founder of the information design firm Voila:, visits the podcast to talk about building his own information design company.

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Data Sketches authors Shirley Wu and Nadieh Bremer visit the PolicyViz Podcast to talk about their work, their process, and the future of dataviz.

The post Episode #195: Nadieh Bremer & Shirley Wu appeared first on PolicyViz.

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New York Times graphics editor Charlie Smart visits the podcast to talk about his work creating the NYT Covid dashboard and more.

The post Episode #194: Charlie Smart appeared first on PolicyViz.

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Kaz Sakamoto teaches Urban Planning and GIS techniques at Columbia University. He visits the podcast to talk about his work and teaching.

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Eva Murray visits the podcast to talk about her work and her new book, Empowered by Data

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Sarah Williams from MIT, and author of the new book Data Action, visits the PolicyViz Podcast.

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Rhea Boyd is a pediatrician, public health advocate, and scholar who writes and teaches on the relationship between structural racism, inequity and health. She visits the podcast to talk about her work and how she writes for different audiences.

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Artist, coder, and researcher Mimi Onuoha visits the podcast to talk about her work, her process, and how we should think about missing data.

The post Episode #189: Mimi Onuoha appeared first on PolicyViz.

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Chantilly Jaggernauth, Tableau Zen Master and founder of the nonprofit Millennials and Data group visits the PolicyViz Podcast to talk about her work and process.

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Authors of "I am a book," Stefanie Posavec and Miriam Quick, visit the podcast to talk about their new book and their process pulling it together.

The post Episode #187: Stefanie Posavec & Miriam Quick appeared first on PolicyViz.

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Data visualization designer krisztina Szűcs joins the podcast to talk about her work and her process.

The post Episode #186: Krisztina Szűcs appeared first on PolicyViz.

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Arathi Sethumadhavan, Head of User Research for Ethics & Society at Microsoft's Cloud+AI, visits the podcast to talk about her work and what it means to use data ethically.

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Alvitta Ottley and Robert Kosara visit the podcast to talk about the 2020 IEEEVIS virtual conference. We discuss favorite sessions and papers, and our take on the virtual experience.

The post Episode #184: IEEEVIS Recap appeared first on PolicyViz.

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Author and professor Safiya Noble visits the podcast to talk about the intersection of race and technology, and how "technological redlining" will impact people of color today and in the future.

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On this week's show, I talk with Aaron Williams about his work as a data journalist, scientist and visualization expert tackling inequity in data and design at scale.

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On this week's episode of the show, I chat with Virginia Eubanks about how high-tech tools and software profile and punish people of color and low-income people and families.

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Zach Norris, Executive Director of the Ella Baker Center for Human Rights, author of We Keep Us Safe: Building Secure, Just, and Inclusive Communities, and co-founder of Restore Oakland, visits the PolicyViz podcast to talk about working with and for people and families across the country.

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Kandrea Wade visits the podcast to talk about her work on algorithmic identity and the digital surveillance of marginalized groups. More information and links in the full show notes.

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Valentina D’Efilippo is an award-winning designer, creative director, and author based in London. Working across formats and industries, her work takes many forms – from theatre productions and exhibitions to editorial content and digital experiences. She has co-authored “The Infographic...

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Christine Zhang just joined the Financial Times as a data journalist on the US elections team for 2020. Previously, she was a data journalist at The Baltimore Sun, where she used numbers, statistics and graphics to tell local news stories...

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Manuel Lima is the author of three books and a leading voice on information visualization. He has worked with an array of organizations designing digital experiences and leading product teams. On this week’s episode of the podcast, I’m reposting a...

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Mike Bostock is the creator of D3, a popular open-source JavaScript library for visualizing data and Observable, an open-source notebook for exploring data and thinking with code. Previously, Mike was a graphics editor for The New York Times, where he helped produce a...

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In case you don’t know, for the last 12 weeks or so I’ve been hosting live video chats with experts in the fields of data, presentation skills, and data visualization. This Data@Urban Digital Discussion series gives (I hope!) people an...

The post Episode #174: Graham MacDonald and Claire Bowen appeared first on PolicyViz.

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Niklas Elmqvist is a full professor in the iSchool at University of Maryland, College Park where he directs the Human-Computer Interaction Laboratory (HCIL). His research area is information visualization, human-computer interaction, and visual analytics. He was elevated to the rank...

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Alper Sarikaya is a Research and Development Engineer at Microsoft working on Power BI and its visualization features. His experience has focused on enabling people to create summary visualizations for the purposes of effectively communicating trends, patterns, and distributions in...

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Jake Berman is an artist, historian, and attorney. His celebrated project The Lost Subways of North America has been featured in The Guardian, The Daily Mail, and Atlas Obscura. In this week’s episode of the podcast, Jake and I talk about his work and get a...

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As the world hunkers down to slow the spread of the coronavirus, a lot of information is making its way to our information channels. Some of this information is true, some is false, and some is misleading in one way...

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Meagan Longoria is a business intelligence consultant with Denny Cherry & Associates Consulting and a Microsoft Data Platform MVP. She helps people understand their data and use it to make better decisions. While her skills include data modeling, data warehousing,...

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Over the past few years of doing this show, I’ve made a whole new bunch of friends. Honestly, some guests I would call ‘acquaintances’, but for others, we’ve either become new friends because of the show or they were friends...

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In this week’s episode, I start to celebrate my new book, Elevate the Debate: A Multilayered Approach to Communicating Your Research. Co-authored with my colleagues in the communications department at the Urban Institute, the goal of the book is to...

The post Episode #167: Elevate the Debate: How to Drive Fact-Based Policy Conversations appeared first on PolicyViz.

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Dr. Jessica Witt is a Professor in Psychology at Colorado State University. Dr. Witt received her Ph.D from the University of Virginia in 2007, and has been at CSU since 2012. Dr. Witt recently won the American Psychological Association’s Distinguished Scientific...

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Happy New Year, everyone! I hope you had some time to rest and relax with friends and family. Thanks for tuning into the show. On this week’s episode, I’m excited to chat with Amelia Wattenberger, a developer and designer focused...

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Cheers to the last episode of The PolicyViz Podcast for 2019! I’ll be taking a couple of weeks off before coming back with brand new episodes in January. In the meantime, please take a peek at my forthcoming book, Elevate...

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Molly Bauch specializes in business transformation, innovation, and strategic communications. She has more than 15 years’ consulting experience helping clients launch new programs, transform service delivery, and embrace fundamental, sustainable change. She’s a Climate Reality Leader, an International Society for...

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I try to spend time watching great speeches. It helps inform how I give my own talks and how I think about teaching others. This past week, I watched Sacha Baron Cohen’s remarks at the Anti-Defamation League’s International Leadership Awards...

The post Episode #163: Anti-Defamation League appeared first on PolicyViz.

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With a decade of experience as User eXperience Designer, Stéphanie Walter helps her clients in the banking, healthcare, automotive and financial industry deliver successful projects to their audience, all the way from strategy to the final products and services. She...

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Robert Simmon is a data visualizer and designer working with Planet. He focuses on producing visualizations that are elegant and easily understandable, while accurately presenting the underlying data. He helped create some of NASA and Planet’s most widely-seen imagery, including...

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Kenneth Field is a ‘cartonerd’ with a Bachelors degree in cartography and PhD in GIS. A former academic who grew tired of admin, he ditched his 20 year career, moved to the US, and talks and writes about cartography, teaches,...

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Sandra Rendgen is an author, consultant and concept developer with a focus on data visualization, interactive media and the history of infographics. She is the author of several books about historical and current developments in the field, most recently History...

The post Episode #159: Sandra Rendgen appeared first on PolicyViz.

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Ronald L. (Ron) Wasserstein is the executive director of the American Statistical Association (ASA) and he joins me this week on the podcast to talk about the 2020 Census. In this role at the ASA, Wasserstein provides executive leadership and...

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Welcome back to the PolicyViz Podcast! I hope you had a great summer and got some time to rest and relax. I’m really excited for this season of the show–I’ve got some great guests lined up to talk about their...

The post Episode #157: Alberto Cairo appeared first on PolicyViz.

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Ben Jones is the founder and CEO of Data Literacy, a training and education company that’s on a mission to help people learn the language of data. Ben is a highly experienced and passionate instructor, having taught data to thousands...

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Part of the FT’s interactive news team, John-Burn Murdoch works as a journalist alongside developers and designers to produce a mix of long term data-driven projects and same day interactive news stories. Other activities include presenting to domestic and overseas...

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Michael Kubovy is Professor Emeritus of Psychology at the University of Virginia. His publications in cognitive science span the fields of decision-making, visual and auditory perception, the psychology of art and the psychology of pleasure. In his work on perception he...

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Claus O. Wilke is a computational and evolutionary biologist and chair of the Department of Integrative Biology at University of Texas at Austin, where he is the Dwight W. and Blanche Faye Reeder Centennial Fellow in Systematic and Evolutionary Biology....

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Cal Newport is an associate professor of computer science at Georgetown University and the author of six books, including Deep Work and So Good They Can’t Ignore You. You won’t find him on Twitter, Facebook, or Instagram, but you can often find him at home with...

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Kassia St Clair is a writer based in London. She studied women’s dress and eighteenth-century masquerade at the University of Bristol and Oxford.She has since written about design and culture for publications including the Economist, the Telegraph, TLS and Architectural...

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Welcome back to this, the 150th episode of the PolicyViz Podcast! With four years of episodes (almost to the day!), I’ve had a lot of fun bringing you insights from people all across the globe doing amazing work in the...

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Michael Freeman is a Senior Lecturer at the University of Washington Information School, where he teaches courses in data science, interactive data visualization, and web development. Prior to his teaching career, he worked as a data visualization specialist and research...

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International speaker, Adobe Creative Residency alumna, and award-winning infographic designer, Jessica Bellamy, graduated Summa Cum Laude from the University of Louisville (UofL) with degrees in Drawing (BFA), Graphic Design (BFA), Pan African Studies (BA), and a minor in Communication. As...

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Lazaro Gamio is a deputy managing editor at Axios, where he oversees a group of visual journalists that make charts, maps, interactive graphics and editorial illustrations. He previously worked at the Washington Post as an assignment editor on the graphics...

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Alyssa Fowers is a Ph.D. student at the University of Miami where she studies research methods, applied statistics, and data visualization. She writes about graphs (and sometimes swords) at Data & Dragons. Before returning to graduate school, she worked in...

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RJ Andrews is a data storyteller. He is the author of the new book Info We Trust, a lavish adventure exploring how to inspire the world with data. RJ blends creative arts and data science to inform. As an independent creative...

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Happy New Year, everyone! And welcome back to the PolicyViz Podcast! I hope you had a happy and safe holiday season. I’ve got a great lineup of guests coming your way, so stay tuned to hear discussions with authors, designers,...

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Another year has passed and a lot has happened in the data visualization world. For this final episode of the PolicyViz Podcast for 2018, I’m reposting the year in review episode from Data Stories, in which I joined hosts Enrico...

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On this week’s episode of the PolicyViz Podcast, I’m excited to welcome Catherine D’Ignazio and Lauren Klein, authors of the new book, Data Feminsim. The book is currently open for public review and comment, so you can head over to the...

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On this week’s episode of the show, I chat with Cole Nussbaumer Knaflic from Storytelling with Data to talk about the recent Tapestry Conference held in Miami, FL. A day-and-a-half of great talks and great people, Tapestry is a small...

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Scott Berinato is is the author of Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations. Even though he’s a writer, he’s also a self-described “dataviz geek” who loves the challenge of finding visual solutions to communications and...

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Michael Brenner is a designer and educator. He is currently Head of Design at DATA4CHAN.GE, which is a non-for-profit organization that helps civil society organizations create data driven advocacy campaigns. Before this he was Design Director at Beyond Words Studio....

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Minhaz Kazi is a Developer Advocate for Google Data Studio. A business intelligence veteran, Minhaz is always exploring new ways for developers to collect, analyze, and visualize data. He is available for long discussions on circular reference errors, benefits of pie...

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I spent the weekend-before-last in Potsdam, Germany attending the Information Plus Conference. In its second iteration (the first was in Vancouver in 2016), Info+ brings together designers, academics, and practitioners to discuss the current state of data visualization work and...

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Welcome back to the PolicyViz Podcast! I’m just back from Germany attending the Information+ Conference in Potsdam and am excited for this week’s episode. This week, I chat with Britt Rusert, one of the editors of the new book, W. E....

The post Episode #136: W.E.B. Du Bois’s Data Portraits appeared first on PolicyViz.

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Kerri Ruttenberg is a partner at Jones Day and the Head of Litigation for the firm’s Washington, D.C. office. She has tried cases in state and federal courts around the country, representing clients in a broad range of cases including...

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Welcome back to the show. On this week’s episode, I sit down with the Chair of the Council of Economic Advisers, Kevin Hassett. Prior to his current stint at the CEA, Kevin was at the American Enterprise Institute. He was...

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Welcome back to the PolicyViz Podcast! After a lovely summer break, I’m back with new episodes focusing on data, data visualization, and presentation skills. I’m slowing things down a bit this year and going to an every-other-week format. Still on...

The post Episode #133: Hilary Mason appeared first on PolicyViz.

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On today’s show, I’m very happy to welcome best selling author and speaker, Carmine Gallo. Carmine is the bestselling author of Talk Like TED, The Storyteller’s Secret, and most recently, Five Stars: The Communication Secrets to Get from Good to Great. He is...

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Dan Roam is the author of five international bestselling books on business-visualization which have been translated into 31 languages. “The Back of the Napkin: Solving Problems with Pictures” was named by Fast Company, The London Times, and BusinessWeek as ‘Creativity Book...

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Michael Cristiani passed away at the end of May of this year. Michael was a well-known and well-loved member of the Tableau community. I didn’t know him well–I only met him in person once. But we communicated via Twitter and...

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David Johnson is the Deputy Director of the Panel Study of Income Dynamics at the University of Michigan. Prior to that position, Dr. Johnson was the Chief Economist at the Bureau of Economic Analysis. He also served as the chief of the...

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Andrew M. Ibrahim MD, MSc is a House Staff Surgeon at the University of Michigan and Chief Medical Officer at HOK Architects. He completed his undergraduate and medical degrees education both with Honors at Case Western Reserve University with a year...

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Welcome back to the podcast! On this week’s episode, I sit down with three amazing people from different parts of the data visualization world to recap the 2018 OpenVisConf conference held in Paris, France earlier this month. Who do I...

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Alex Selby-Boothroyd joined The Economist as an interactive visual data journalist in 2015 and now runs the data journalism department. His team writes data-driven articles and creates all of the static and interactive charts and maps for the website, apps and weekly...

The post Episode #126: Alex Selby-Boothroyd & Marie Segger appeared first on PolicyViz.

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The Alan Alda Center for Communicating Science, hosted in the School of Journalism at Stony Brook University, empowers scientists and health professionals to communicate complex topics in clear, vivid, and engaging ways; leading to improved understanding by the public, media,...

The post Episode #125: Laura Lindenfeld and Valeri Lantz-Gefroh appeared first on PolicyViz.

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Eileen Webb is a digital strategist and co-founder of webmeadow, a firm that helps progressive organizations develop content and technology strategies to make the world a better place. Her background is in server-side coding and being that odd person who...

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Scott Murray is the Principal Learning Scientist at O’Reilly Media. Prior to that work, he was an Assistant Professor of Design at USF. He works with code, data, and computation to design learning experiences and is deeply interested in learning, teaching,...

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Terri Trespicio is an award-winning writer, speaker, and branding pro, and works with individuals and organizations to help nail their messaging and engage clients, customers, and fans. Her TEDx talk, “Stop Searching for Your Passion” has earned more than 3...

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On this week’s episode of the podcast, I’m joined by two guests to talk about gender discrimination in the field of economics. Erin Hengel is an assistant professor of Economics at the University of Liverpool. She evaluates bankruptcy vis-à-vis recent...

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Alberto Cairo is the Knight Chair in Visual Journalism at the University of Miami. He teaches data visualization and infographics in our Journalism and Interactive Media Masters programs, and he is also the director of the Visualization Program at UM’s Center...

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Emma Bannister is the founder and CEO of Presentation Studio, a presentation-design firm based in in Sydney, Australia. Emma has designed presentations for banks, technology firms, and others to help them communicate better. Emma and her staff of 20 are...

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From chemist to artist to designer, Stephy Lewis used to be a total Adobe snob until she was thrust into the world of presentation design. Ever since, she’s been passionately evangelizing the field in an attempt to elevate our industry as high...

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Steve Haroz is a postdoc researcher at Pierre and Marie Curie University in Paris. His research explores how the brain perceives and understands visually displayed information like charts and infographics. We talk about data visualization research, uncertainty, connected scatterplots, and...

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George Johnston is the founder of Nitrous, a platform to help startup companies collaborate with governments to ensure more sustainable and cost effective innovation. They also help government open and share their data through collaboration with other partners. George is also...

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On last week’s episode, I sat outside Facebook and chatted with Andy Kirk about our experience at the Social Science Foo Camp, a two-and-a-half day conference at Facebook that brought together all sorts of social scientists. One of the first...

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I just returned from a 10-day data visualization trek from DC to New York to San Francisco to Los Angeles where I talk workshops, gave talks, and attended the Social Science Foo Camp at Facebook. SocSciFoo, as it’s called, is...

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Michael Gonchar and Sharon Hessney lead a new project at the New York Times called “What’s Going On in This Graph?” (WGOITG). Every second Tuesday of every month, the NYT publishes a graphic on a topic suitable for subjects across...

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We take a turn towards public policy and communicating research in this week’s episode of the podcast. My guest is my Urban Institute colleague Len Burman, an Institute Fellow and the Paul Volcker Professor and a Professor of Public Administration and...

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Len Kiefer is the Deputy Chief Economist at the Federal Home Loan Mortgage Corporation, better known, as Freddie Mac. Freddie Mac is the government sponsored enterprise that helps keep mortgage money flowing to home buyers and home owners, across America....

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Ben Welsh is the editor of the Data Desk at the LA Times (@LATdatadesk), a team of reporters and computer programmers in the newsroom that works to collect, organize, analyze and present large amounts of information. Projects that he has contributed...

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Happy New Year, everyone! I hope you were able to enjoy some R&R, and spend some time with friends and family. To kick of a great new slate of guests in 2018, I’m very excited to welcome Jenn Schiffer to the...

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For the final episode of 2017, I’m super excited to chat with my old friendAndy Kirk from Visualising Data. You undoubtedly know Andy from his website, books, presentations, Twitter feed, monthly roundups, and overall data viz awesomeness. Andy is also a...

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Jane Pong is a Data Visualization Journalist at the Financial Times. Previously, she produced information graphics at Thomson Reuters in Singapore and South China Morning Post in Hong Kong. She is a graduate of the University of Sydney with a combined Arts/Science...

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On this week’s episode of the podcast, I welcome two special guests, Jimmy Soni and Rob Goodman, authors of the new biography of Claude Shannon, the father of information theory. Jimmy Soni has served as an editor at the New...

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Kenton Powell is the Head of Design and Graphics at Vice News, a somewhat new venture from Vice and now showing on HBO. Prior to Vice, Kenton was a designer at Bloomberg Business Week and then an interactive editor for...

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Matt Abrahams is the author of the book Speaking Up without Freaking Out: 50 Techniques for Confident and Compelling Presenting which can help you address your presentation anxieties. Matt teaches Strategic Communication for Stanford University’s Graduate School of Business and Presentation Skills for Stanford’s...

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Francis Gagnon is founder of the Voilà data analytics and visualization firm. He has worked in reporting and information design at the international level for the last decade. At the International Finance Corporation (IFC), a member of the World Bank Group, he designed...

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You know Severino Ribecca. He’s the one who runsThe Data Visualisation Catalogue, a library of different information visualisation types. Sev and I have also worked together for a few years developing the various Graphic Continuum products. Sev is a graphic...

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Diana Yoo has over ten years of experience as an information graphic designer and as an art director/design director. As the Art and Creative Director at the Pew Research Center, Diana lead a multidisciplinary team to develop digital projects of all...

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Welcome to the 100th episode of the PolicyViz Podcast! 100 episodes! More than 100 guests spanning more than 33 hours to bring you the best of data visualization, open data, storytelling, and presentation skills. I hope you have enjoyed the...

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Cambria Brown is a Performance Management and Data Specialist with the Office of Planning, Partnerships and Improvement at the Colorado Department of Public Health and Environment. She specializes in performance management, data visualization and data dashboard development for divisions and...

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Mark Monmonier is a Distinguished Professor of Geography at the Maxwell School at Syracuse University. In the data visualization field, he is probably best known for his fantastic book, How to Lie with Maps. Professor Monmonier’s research focuses on the history...

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Working together, Tom Weatherburn and William Davis run the mapTO website a project where they make maps of Toronto and the surrounding areas. I found their project on rescaling the Toronto subway system to actual geography really intriguing, so I invited them to come...

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Neil Halloran is one of those people who, in my mind, is pushing the data visualization field in different directions. In his two data-driven documentaries, The Fallen and The Shadow Peace, Neil uses different ways of showing data to convey...

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Hi everyone, welcome back! I’m excited to have Jen Golbeck on this week’s episode. Jen is a world leader in social media research and science communication. She is a pioneer in the field of social data analytics, discovering people’s hidden...

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Welcome back to the show! To get you ready for the September 23rd National Day of Civic Hacking, I’m very happy to have Lilian Coral on this week’s episode. At the time of this interview in late August, Lilian was...

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Welcome back to the PolicyViz Podcast! I’m back! After a few weeks off this summer to enjoy the nice weather and time with the family, I’m excited to be back with a whole new slate of guests. Over the next...

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Hi everyone, welcome back to the show! This will be the last episode for the summer. I’m going to take a break from podcasting for the next two months, do some writing, relaxing, and traveling, and gear up for a...

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Welcome back to this week’s show! This week, I speak with Shomik Sarkar, who is currently a data scientist at the Democratic National Committee and was Director of Reporting for Hillary Clinton’s campaign for president. Shomik and I talk about how...

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I hope you’ve enjoyed the last few episodes of the podcast. Continuing a theme that has been present in the last few episodes, on this week’s show I welcome Javier Zarracina from Vox and Anna Flagg from the Marshall Project...

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In this week’s episode of the PolicyViz Podcast, I’m joined by Priya Krishnakumar, a visual/data journalist at the LA Times. I’m a big fan of Priya’s work and her colleagues at the LA Times. For me in particular, I think...

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Welcome back to the show! There are lots of decisions to be made when it comes to creating a data visualization. And when you pull multiple visualizations together into one view, the decisions compound. That’s why on this week’s show,...

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Welcome back to the PolicyViz Podcast. This week, I’m pleased to welcome Elijah Meeks to the show to talk about his work as a Senior Data Visualization Engineer at Netflix and the state of data visualization jobs. Elijah sparked a...

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Welcome back to the PolicyViz Podcast! On this week’s episode, I’m very pleased to be joined by Mona Chalabi from the Guardian. Mona, if you don’t already know, is involved in a number of exciting projects at the Guardian and...

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Welcome back to this week’s episode of The PolicyViz Podcast. I’m pleased to welcome Anna Flagg from the Marshall Project this week. Anna and I met at the Malofiej Infographic World Summit in March. With 899 online entries to judge,...

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Do you like transportation? Do you like data about transportation? Then you’re going to love this week’s episode of The PolicyViz Podcast! On this week’s show, I’m very happy to be joined by Dan Morgan, the Chief Data Officer at the Department of...

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Many people agreed with my basic argument during last month’s Month of Story. One person, however, had a slightly different take on how we tell stories with data and what that concept means as it is applied to data. Chad Skelton, formerly...

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The “open” movement consists of different product types–we all know about open data and open source code, for example. But more and more, we’re seeing open book writing. Last year, Hadley Wickham wrote his book on R in an open platform....

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This podcast episode wraps up my Month of Story (and the end of my lost voice!). Over the past month, I’ve done a number of presentations and blog writing about stories, and I’ve talked to some incredible journalists and data...

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I’m currently in Pamplona, Spain enjoying what is turning out to be a very rewarding and tiring week at the Malofiej Infographic World Summit (follow #malofiej25 on Twitter, if you like). If I can find the time, I’ll try to conduct...

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My “Month of Story” continues! And I’ve finally started publishing some blog posts on the issue, so check out the PolicyViz blog all this week. On the podcast, the Month of Story continues this week with Jan Willem Tulp. Jan...

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As the “Month of Story” continues, I head out west this week to talk with the Executive Director of the West Big Data Hub, Meredith Lee. Meredith was a Science and Technology Fellow here in DC, worked at the Department...

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I’m calling March the “Month of Story.” Kicking off with the Tapestry Conference in St. Augustine, Florida, the Socrata Connect conference in Washington, DC on March 7, and ending with the Malofiej Infographic World Summit in Pamplona, Spain, I’m focusing on...

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Welcome back to the show! This week, I chat with Sarah Kalicin, Senior Statistician at Intel about how to get an organization to leverage their data to improve their work, analysis, and communication. We talk about all things organizational this...

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Welcome back to the PolicyViz Podcast! As I’m sure you’re aware, data visualization tools are a regular source of discussion: Which tools to use? Which one is best? Which one allows me to do this or that or the other?...

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Welcome back to The PolicyViz Podcast! On this week’s episode, I chat with Feng-yuan Liu and Shangqian Lee from GovTech Singapore. GovTech is responsible for using technology and data to help transform the public sector in Singapore. Their role includes deploying...

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On this week’s episode of the show, I’m excited to chat with Xan Gregg, Data Visualization Development Director for JMP at SAS (see below). Xan regularly creates visualizations and makeovers for all sorts of interesting topics, as well as, of...

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Hi everyone, welcome back to the PolicyViz Podcast! This week, I’m pleased to chat with data journalist, designer, and (new) visualization consultant, Maarten Lambrechts. Maarten has built a ton of great projects and analysis, so we dive into his process, his...

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Hi everyone! Welcome back to the PolicyViz Podcast! On this week’s episode, I’m excited to welcome my Urban Institute colleague Tracy Gordon to the show. Tracy is a senior fellow with the Urban-Brookings Tax Policy Center, where she researches and writes...

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Welcome back to the PolicyViz Podcast! On this week’s episode, I’m excited to welcome Simon Rogers to the show. Simon is a data journalist, writer, and speaker and has worked at the Guardian in the UK, Twitter, and is now a...

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Hi everyone! Happy New Year and welcome back to the PolicyViz Podcast! I hope you had a relaxing holiday season. To kick off 2017, I’m excited to welcome Hadley Wickham to the show to talk about his work with the...

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Welcome to the final episode of The PolicyViz Podcast of 2016! Across 39 episodes this year, we’ve covered open data, data visualization, data workflow issues, presentation skills and design, and more! Thank you for tuning in each week! Your support...

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Thanks for tuning in to this week’s episode of The PolicyViz Podcast! This week, I talk with Wilson Andrews, Graphics Editor at the New York Times. In the wake of Donald Trump’s election win, Wilson and I talk about polling,...

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Welcome back to The PolicyViz Podcast! I’m back after a couple of weeks off–first, taking a break for the Thanksgiving holiday, and then to iron out some bugs with the new PolicyViz website. On this week’s show, I chat with...

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Welcome back to the show! I’m super excited to chat with Nadieh Bremer on this week’s show. Nadieh is an astronomer by training who came to love data visualization. (Does anyone come to data visualization directly??). Her projects include web-based interactive...

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On this week’s episode of The PolicyViz Podcast, I chat with Kim Rees, who is Co-founder and Head of Visualization at the data visualization firm Periscopic. You may know Kim from her great work at Periscopic, her talk at the...

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Welcome back to the PolicyViz Podcast! With my book on presentations about to launch, I’m very excited to talk with one of the authors who inspired my work in this area, Garr Reynolds. If you’ve ever thought carefully about how...

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Welcome back to The PolicyViz Podcast. On this week’s show, I chat with Stephanie Evergreen whose new book on data visualization, Effective Data Visualization: The Right Chart for the Right Data, was released in May. In this week’s episode, Stephanie and I...

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On this week’s episode of The PolicyViz Podcast, I’m pleased to chat with Drew Skau and Robert Kosara, who co-authored a couple of papers on how we perceive the quantities in pie charts. Their work centers on how do we perceive quantities...

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On this week’s episode of The PolicyViz Podcast, I’m pleased to welcome Evan Sinar to the show. Evan is the Chief Scientist and Vice President at Development Dimensions International (DDI) in Pittsburgh. Evan is a Industrial and Organizational Psychologist (I/O Psych)...

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Welcome back to the PolicyViz Podcast! I’m taking a bit of a turn on the show. Instead of talking with people directly creating visualizations or building visualization tools, I talk to two people working in the field of education research...

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Welcome back to the show! This week, I’m very pleased to be joined by John Burn-Murdoch, Alan Smith, and Martin Stabe from the graphics desk at the Financial Times. Established in 1884, the Financial Times is one of the world’s leading...

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On this week’s episode of The PolicyViz Podcast, I chat with the organizers of the DC chapter of the Data+Women Meetup group, Brittany Fong, Julie Kim, Emily Kund, and Erin Simpler. We talk about the current state of the group, challenges...

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Welcome back to The PolicyViz Podcast! I hope you had a great, safe summer. To kick off the fall slate of episodes, I’m very excited to welcome Andy Kirk to the show. Andy–as I’m sure you know–runs the popular website Visualisingdata.com,...

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Welcome back to The PolicyViz Podcast. Number 55 if you’re keeping count. This is the final episode for the summer; I’m going to take some time off from recording, editing, and setting up before coming back in the fall with...

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Welcome back to The PolicyViz Podcast. On this week’s show, I chat with Zan Armstrong (in person, no less!) about her work analyzing and visualizing data. I first met Zan in the spring at the OpenVisConf following her great talk...

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Welcome back to The PolicyViz Podcast. Episode #53, if you’re keeping count. On this week’s show, I’m happy to chat with Jen Christiansen, Senior Graphics Editor at Scientific American. We talk about a whole range of issues this week, folks:...

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Welcome back to the show! As you may know, I’m a big fan of data and research that shows how we do or don’t acquire information. But so much of that research–or purported research, I should say–sits on dubious methods...

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I’m very excited to welcome Kennedy Elliott to this week’s episode of The PolicyViz Podcast. Kennedy is a member of the graphics department at the Washington Post and part of the team that recently won a Pulitzer Prize for their...

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Welcome back to the show! This week, I’m pleased to have Max Galka join me on the show. Max is a data enthusiast and has created a lot of great visualizations, ranging from such topics as immigration, traffic, and climate...

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This week’s episode was recorded live at NPR in Northeast Washington, DC with Supervising Senior Editor of the NPR Visuals team, Brian Boyer. No, I didn’t get to sit in one of the fancy chairs, but I did get to...

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Welcome back to the show! On this week’s episode, I’m pleased to welcome Rebecca Williams and Eric Reese from the Center for Government Excellence at the Johns Hopkins University. We talk about all things open data, open technology, and how...

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Welcome back to the PolicyViz Podcast. Thanks for tuning in each week to hear about people doing great work in data, open data, data visualization, and other related fields. In this week’s show, I’m happy to have Isabel Meirelles join...

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Welcome back to the PolicyViz Podcast! I’m pleased to welcome Andy Cotgreave and Andy Kriebel to the show this week. Each week, The Andys, as it were, are the primary authors of the Makeover Monday project in which they–and dozens...

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I’m pleased to be joined by Seth Blanchard this week on the show. Seth is a Senior Developer on the engineering team of the graphics department at the Washington Post. He helps develop new story forms and smooths development process...

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Recorded along the lovely Boston Harbor (enjoy the outdoor sounds), in this week’s episode of The PolicyViz Podcast, I chat with Lane Harrison, Assistant Professor in the Computer Science Department at Worcester Polytechnic Institute. We not only talk about Lane’s interesting...

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In this week’s episode of The PolicyViz Podcast, I chat with Marie Whittaker from the Office of the Deputy Mayor for Planning and Economic Development (DMPED) in Washington, DC. With a small team responsible for providing economic insight to DC’s...

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Welcome back to The PolicyViz Podcast. I’m happy to have Ben Jones on the show this week. Ben writes about data visualization on his popular blog Data Remixed and also helps run Tableau Public. Ben is a prolific writer, blogger, speaker,...

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As everyone knows, Microsoft creates a lot of stuff: software, hardware, services, cloud services, and more. But there is also a group at Microsoft that gets to play around and create cool stuff. In this week’s episode of The PolicyViz...

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Welcome back to The PolicyViz Podcast. This week, I’m pleased to chat with Zach Gemignani, CEO and Founder of Juice Analytics. Juice, a sponsor of the show, helps their customers analyze, visualize, and deliver their data. Their experiences with different clients demonstrates...

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Tomorrow (April 6) marks the first birthday of The PolicyViz Podcast! Thanks so much for continuing to tune in every week; your support makes the show worth doing. And a special thanks to Juice Analytics for sponsoring this and many other...

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I’m pleased to welcome Steven Drucker to the show, who is a principal researcher at Microsoft Research. Steven and I talk about MSR’s new project, SandDance, a browser-based information visualization system that scales to hundreds of thousands of items. You...

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On this week’s episode of The PolicyViz Podcast, I chat with Sarah Kliff from Vox. Sarah is a Senior Editor at Vox.com and oversees reporting on health and medicine. She previously worked at the Washington Post and Politico. Sarah and I...

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Welcome back to the PolicyViz Podcast! On this week’s episode, I chat with Xaquín González Veira who is the Editor of Visuals at the Guardian. Recorded live in Germany at the Dagstuhl Visual Storytelling conference, we talk about all the things...

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If you haven’t heard, OpenVisConf 2016 is right around the corner (April 25-26 in Bostom). In its 4th year, OpenVis Conf is a two-day, single track conference centered around the practice of visualizing data on the web. The conference features...

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This week, I’m pleased to welcome Dominikus Baur to the program. Mobile interaction designer and developer, Dominikus creates usable, aesthetic, and responsive visualizations for all platforms. His latest project, Subspotting, logged the cell phoe reception of New York City’s four...

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In this week’s episode of the PolicyViz Podcast, I speak to Chris Parmer, Chief Product Officer and co-founder of Plotly, the online data visualization and analytics firm. Chris and I talk about Plotly’s tool, their business model, open source data...

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Welcome back to the show. In this week’s episode, we’re talking about teaching data visualization. What are best practices? Are there great exercises and assignments to give? What should students walk away with at the end of the day, week,...

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A few weeks ago, I attended an interesting one-day workshop on the responsible and ethical use of data in the data visualization field in New York City. The Responsible Data Forum brought together about 35 people to address issues and topics...

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We turn back to tools this week on the PolicyViz Podcast. I’m pleased to have with me Jon Acampora who runs the very popular Excel Campus website. Jon provides tutorials on VBA, formulas, and many other tools. As an Excel...

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Welcome back to the PolicyViz Podcast! A special episode this week–a panel discussion, as it were, about open data. This conversation was sparked by a presentation I gave at the 2015 Socrata Customer Summit about the need to make open...

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Happy New Year! I hope you had a terrific holiday season and kickoff to the new year. I also hope you’re ready for an exciting year of The PolicyViz Podcast because I’ve got some unbelievable guests lined up for the...

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For the final episode in 2015, I’m excited to welcome Giorgia Lupi and Stefanie Posavec back to the show to talk about their year of Dear Data, their analog data visualization project. Our conversation is a bit broader than just...

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Very excited to welcome Jim Vallandingham to this week’s episode of the podcast. Jim is a Data Visualization and Open Web Engineer at Bocoup. He writes regularly about everything open: code, projects, collaborative tools, building great stuff. In this episode,...

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I’m happy to invite my good friend Andy Cotgreave from Tableau Software to the 25th episode of The PolicyViz Podcast. Andy is the Technical Evangelist at Tableau software and writes and speaks regularly and widely about all things data: how to analyze...

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Welcome back to The PolicyViz Podcast! This week, I speak with Cole Nussbaumer Knaflic, who runs the Storytelling with Data blog and whose new book–Storytelling with Data–was just released. I was fortunate enough to get a copy of the book...

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Welcome back to The PolicyViz Podcast! In this week’s episode, I chat with Arvind Satyanarayan, Computer Science PhD candidate at Stanford University, working with Jeff Heer and the Interactive Data Lab. Arvind’s research seeks to lower the threshold for design, with a...

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Welcome back to The PolicyViz Podcast! In this week’s episode, I chat with Rebecca Williams, open data and civic tech guru extraordinaire. Rebecca is on detail at the Office of Management and Budget from her usual job at Data.gov. We...

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On this, the 21st episode of The PolicyViz Podcast, I am very excited to welcome Edward Tufte to the show. As you might expect, I was excited to talk with Professor Tufte, so this episode is quite a bit longer than...

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This is a special bonus episode of the PolicyViz Podcast. I was in New Orleans last week for the Presentation Summit and Nigel Holmes kicked things off with his presentation about the role of humor in presentations. Formerly of Time...

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In this week’s episode, I’m happy to welcome to the show Chris Ingraham from Wonkblog at the Washington Post. We talk about good and bad data, interesting and unique data, and his process for writing and pulling together great stories....

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Welcome back to the PolicyViz Podcast! I hope you had a great summer! I’m excited to kick off the fall slate of episodes with Tiago Veloso from Visual Loop. Tiago has been curating some of the best data visualization and...

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Episode Number 17! This is the last episode for the summer, folks. Time to take a little break and recharge the batteries. I have a whole new slate of guests scheduled for the fall and I’ll also have a sponsor...

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Justin Grimes is a statistician, open data proselytizer, Code for America Brigade leader and all-around fun guy to be around. In this, the 16th episode of The PolicyViz Podcast, Justin and I talk about effective open data strategies, civic technology,...

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The 15th episode of the PolicyViz Podcast features Aron Pilhofer from the Guardian newspaper in the UK. Aron and I talk about how the Guardian is working to deepen reader engagement, measure how readers interact with their content, and how...

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In this, the 14th episode of the PolicyViz Podcast, I speak with Ken Melero, Director of Federal at Socrata. Socrata is a cloud-based open data software company that helps local, state, and federal governments open their data and make those data...

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In this episode of the PolicyViz Podcast, I speak with Adam Coyne and Jennifer de Vallance from Mathematica Policy Research. MPR conducts policy and economic research to improve public well-being by bringing the highest standards of quality, objectivity, and excellence...

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Welcome to Episode #11 of the PolicyVIz Podcast! This week, I’m excited to welcome Scott Klein, Assistant Managing Editor at ProPublica. Scott and his team do really exciting work with data, by presenting large datasets with journalism. That is, they...

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In this week’s episode, I speak with author, speaker, and dashboard expert Mico Yuk. Mico is co-founder and partner at BI Brainz, author of the book Data Visualization for Dummies, and leads business intelligence dashboard trainings around the world. In this...

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When it comes to creating visualizations with your data, it’s not the tool that “makes” things, it’s the user. I’ve heard lots of people say that Excel makes terrible data visualizations. It’s true that 3D cones and 3D exploding pie...

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What’s the best way to teach data visualization? How do you mix theory and practical applications? What tool or tools should you teach: Tableau, Excel, d3, Processing, something else? Scott Murray, Assistant Professor of Design at the University of San...

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In this week’s episode, I chat with Nick Diakopoulos from the University of Maryland at College Park. Nick recently co-hosted an event on algorithmic transparency at the Tow Center for Digital Journalism at Columbia University. Nick and I chat about algorithms...

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In this week’s episode, I speak with color-guru Rob Simmon from Planet Labs. If you don’t know Rob, you should absolutely read is famous 6-part series on color hosted at the Visual.ly blog. Nowadays, Rob is busy launching satellites and...

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In the previous episode of the PolicyViz Podcast, I spoke with Maral Pourkazemi about the DeepLab project. That discussion was so interesting, we pulled in instigator Addie Wagenknecht to talk more about the project, its future, and what it means...

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There are important and consequential issues to discuss when it comes to technological differences and disparities across different groups such as gender, race, ethnicities, or differences across countries. In this, the fifth episode of The PolicyViz Podcast, I speak with Maral...

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We’ve seen some big changes in data driven journalism over the past year or so with new media companies like 538, Vox, and the Upshot launching. In this fourth episode of the PolicyViz Podcast, I speak with Ben Casselman, Chief...

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Thanks so much for tuning into the first two episodes of the PolicyViz Podcast. On this week’s episode, I welcome Eric Klotz, founder of Visualized, to discuss the challenges and excitement around data visualization conferences. Eric and I have been...

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On this week’s episode of The PolicyViz Podcast, I speak with Giorgia Lupi and Stefanie Posavec about their current project, Dear Data. If you haven’t seen it, Dear Data is a year-long analog project in which Giorgia and Stephanie collect their...

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Welcome to the PolicyViz Podcast! That’s right, I’m moving my random audio blob posts to a more structured podcast. In these podcasts, I will chat with my guests primarily about data visualization, presentation skills, and presentation design. I’m also hoping...

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The PolicyViz Podcast: Recent Episodes

The PolicyViz Podcast