Mining Your Own Business Podcast

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In this episode of Mining Your Own Business, John Cook shares his unconventional journey to leading a data science team at one of the world’s largest hospitality companies. As Senior Director of Data Science and Reporting at Marriott International, John guides a talented team supporting U.S. and Canada sales, marketing, and revenue management for Marriott.

Tune in as John shares insights into the intricacies of revenue management, the importance of clear data communication, and how understanding different business aspects helps with problem-solving. You won’t want to miss this engaging conversation with our host Evan Wimpey.

In this episode you will learn:

⛛ Why technical aptitude and business understanding go hand in hand
⛛ The importance of communication and storytelling with data
⛛ Why immediate business needs must be balanced with long-term, scalable solutions
⛛ How understanding different parts of a business can help with problem-solving

Quote
💬 “Where we really get action and where we really get good results from data is when people are able to understand it and then use it to do something.”

Featured in This Episode

John Cook, Senior Director of Data Science & Reporting, U.S. &. Canada SMR | Marriott International

John Cook is the Senior Director of Data Science and Reporting at Marriott International, where he leads a talented team of associates dedicated to supporting the U.S. and Canada sales, marketing, and revenue management teams, along with their sister analytics teams. Over the years, he’s gained experience across different regions and functions, eventually transitioning into his current leadership position.

John has a Master of Data Analytics from the University of Maryland Global Campus. He’s also the founder of Penguin Analytics, where he focuses on bridging the gap between “data people” and “business people” by coaching business leaders in data literacy, and data scientists in business literacy.

LinkedIn: linkedin.com/in/johnalexandercook

Evan Wimpey, Director of Analytics Strategy | Elder Research
Company website: elderresearch.com
LinkedIn: linkedin.com/in/evan-wimpey

Chapters
00:00 Introduction
03:08 John talks about revenue management
05:18 Balancing experience in revenue management with roles in sales and marketing
07:48 Reporting within data science, including standardized and storytelling aspects.
15:23 The importance of hiring team members with both technical skills and enthusiasm for problem-solving
20:12 John shares how his team uses machine learning
22:27 John shares how his background in music helps with communicating data effectively
26:48 John talks about ideas for the future
29:52 Wrapping up the show

Find more show notes, transcripts, & more episodes at:
https://www.elderresearch.com/resource/podcasts/

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In this episode of Mining Your Own Business, Data Scientist Xhoana Laska shares how she and her team are bringing data innovation to the field of industrial paint and coatings, an industry that became prominent in the late 1800s.

With her background in mathematics and statistics, Xhoana’s knack for numbers has come in handy in her role at National Coatings & Supplies | Single Source, Inc. She has spearheaded projects to save thousands of labor hours, optimize inventory processes, improve demand forecasting, and more.

Join us as our podcast host, Evan Wimpey, chats with Xhoana about interesting projects she’s worked on and insight she’s gained along the way!

In this episode you will learn:

⛛ The importance of identifying areas where data analysis can drive significant efficiency improvements
⛛ Why effective communication is crucial for stakeholder buy-in
⛛ The need to prioritize projects based on business-critical needs
⛛ The importance of creating a culture of learning that empowers employees

Quote
💬 “I enjoy working on problems that involve bringing solutions that can potentially save money or optimize and be efficient at the end of the day.”

Featured in This EpisodeXhoana Laska, Data Scientist, National Coatings & Supplies | Single Source, Inc.From process optimization to demand forecasting, Xhoana Laska enjoys creating data-driven solutions for complex business problems. And that’s at the heart of her work as a data scientist for National Coatings & Supplies | Single Source, Inc. (NCS SSI).

With over three years of experience in the field, Xhoana has a knack for numbers and strong background in mathematics. She specializes in business process automation and statistical and machine learning modeling for procurement, sales, and marketing. She’s also a proud member of the Wolfpack at NC State University, where she received her Master of Statistics degree.

LinkedIn:linkedin.com/in/xhoana-laska

Evan Wimpey, Director of Analytics Strategy, Elder Research
Company website: elderresearch.com
LinkedIn: linkedin.com/in/evan-wimpey

Chapters
00:00 Introduction
00:50 Xhoana shares her journey into the field of data science.
01:56 Evan shifts the conversation to discuss Xhoana’s first project at NCS SSI.
06:31 Evan discusses the importance of stakeholder involvement in the project.
07:27 Xhoana explains the support provided by the IT team for data quality and accessibility.
10:08 Evan and Xhoana discuss data quality and tools used at NCS SSI.
13:41 The discussion shifts to the process of prioritizing projects at NCS SSI.
22:17 Evan invites Xhoana to share her ideas for future data science projects.
25:33 Evan wraps up the conversation with Xhoana.

Find more show notes, transcripts, & more episodes at:
https://www.elderresearch.com/resource/podcasts/

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Explore the dynamic world of retail data analytics with Sravan Vadigepalli, Senior Director of Products and Engineering at Lowe’s. In this episode you’ll learn about the unique analytics challenges and opportunities in the home improvement space and how Lowe’s approaches product development.

During the chat Sravan also shares his thoughts on the future of data-driven innovation in retail. Grab some coffee and join host Evan Wimpey as he delves into the intersection of technology and customer-centric solutions. We hope you enjoy the conversation!

In this episode you will learn:⛛ The importance of recognizing and adapting to the diverse needs of customers
⛛ Why data initiatives should be aligned with broader business objectives
⛛ How Sravan’s team uses a unique framework to streamline decision-making processes
⛛ The need for a balance between building internal tools and leveraging external solutions

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💬 “For data folks like us, you need to get into the weeds of the business.”

Featured in This EpisodeSravan Vadigepalli, Senior Director of Products and Engineering, Lowe’s
Sravan Vadigepalli has spent the past decade building and curating data analytics teams in the retail industry. He specializes in translating data into insights and applications of Machine Learning/AI models to the ever-evolving retail space. Sravan did his MS in MIS from Oklahoma State University and an MBA from the University of Illinois Urbana-Champaign. Outside of work, Sravan is a self-proclaimed long-distance endurance runner.
LinkedIn: linkedin.com/in/sravanvadigepalli/

Evan Wimpey, Director of Analytics Strategy, Elder Research
Company website: elderresearch.com
LinkedIn: linkedin.com/in/evan-wimpey

Chapters
00:00 Introduction of the podcast and the guest, Sravan Vadigepalli.
00:45 Sravan Vadigepalli shares his background and career path.
02:53 Discussion on the differences in data challenges between various retail sectors.
8:24 Discussion on the process of developing data products and analytic products.
12:52 Explanation of the prioritization process for projects.
15:49 Discussion on the frameworks used for project prioritization.
19:31 Conversation shifts to evaluating internal versus external solutions for product development.
22:17 Sravan shares some ideas for the future.
25:10 Evan wraps up the show.

Find more show notes, transcripts, & more episodes at:
https://www.elderresearch.com/resource/podcasts/

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When considering evaluation metrics for classification models, is it possible for one metric to rule them all? Join us for a lively debate between Aric LaBarr, Associate Professor of Analytics at NC State's Institute for Advanced Analytics, and Robert Robison, Elder Research Senior Data Scientist.

During the debate Robert champions AUC’s comprehensive measure of model performance, while Aric advocates for a broader perspective, emphasizing the importance of business context in metric selection. Tune in as host Evan Wimpey moderates the discussion, and gain valuable insight on what really matters when it comes to machine learning model evaluation. We hope you enjoy the conversation!

In this episode you will learn:

⛛ The importance of exploring various metrics to evaluate model performance

⛛ Why metrics should align with business objectives

⛛ The need for data science teams to invest time in feature engineering

⛛ Why a model's success relies not only on its performance but also on stakeholders' ability to understand and trust the insights it provides

Quotes
💬 “There's a difference between communicating the value of a model and distinguishing between which models are better.” –Robert Robison

💬 “If you can't explain the model to your stakeholders or business users, then it's not going to get implemented.” –Aric LaBarr

Featured in This EpisodeAric LaBarr | Associate Professor of Analytics, Institute for Advanced Analytics

LinkedIn: https://www.linkedin.com/in/ariclabarr/

Robert Robison | Senior Data Scientist, Elder Research

LinkedIn: https://www.linkedin.com/in/robert-robison/

Evan Wimpey, Director of Analytics Strategy, Elder Research

LinkedIn: linkedin.com/in/evan-wimpey

Chapters
00:00 Evan introduces the debate topic and guests, Aric LaBarr and Robert Robison.
01:37 Robert begins his argument by defining AUC (Area Under the Curve) and its significance as a metric for classification models.
06:11 Aric begins his rebuttal, challenging the notion that AUC is the only metric to consider.
09:26 Robert provides a rebuttal to Aric's points.
11:48 Aric starts his rebuttal, focusing on communicating models to business users.
14:41 Robert responds to Aric’s points.
16:18 Evan asks Robert if certain cases may require metrics other than AUC.
17:03 Robert responds to Evan’s question.
17:53 Aric weighs in on the question.
19:37 Evan asks Aric if focusing solely on AUC may save time and costs.
20:30 Aric responds to Evan’s question.
22:20 Evan gives time for the debaters to ask each other questions.
25:30 The debaters share closing remarks, summarizing their positions.
29:14 Evan wraps up the show.

Find more show notes, transcripts, & more episodes at:
https://www.elderresearch.com/resource/podcasts/

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In this episode Evan Wimpey sits down to chat with Nirmal Budhathoki. A seasoned data scientist at Microsoft, Nirmal has extensive experience in both cybersecurity and the tech industry.

During the conversation Nirmal discusses his unique career journey, from unexpected detours, including serving in the Army, to becoming a data scientist. He also talks about applications of machine learning in cloud security, emphasizing the need for proactive defense strategies. Additionally, Nirmal offers advice for aspiring data scientists and highlights the ongoing advancements in the field.

In this episode you will learn:

⛛ How individuals from diverse experiences can excel in data science

⛛ The importance of proactive defense strategies in cybersecurity

⛛ The need for community engagement and mentorship in the field of data science

⛛ How Large Language Models (LLMs) can be used to enhance cybersecurity measures

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💬 “Security is never a compromise.”

Featured in This EpisodeNirmal Budhathoki, Senior Data Scientist, MicrosoftNirmal Budhathoki is a Senior Data Scientist at Microsoft focused on cloud security. He has over 12 years of experience in the IT industry, including more than 5 years in data science. A dedicated learner, Nirmal holds master's degrees in information systems, business administration, and data science. He enjoys sharing his knowledge with others and has provided hundreds of free mentoring sessions to aspiring data scientists. He also conducts mentored learning for MIT’s Data Science and Machine Learning certification program in collaboration with Great Learning.

Nirmal also has experience working with the military—both as a security data analyst and through serving four years in the Army. He’s also passionate about solving data science problems aligned with product strategy and business outcomes. And in his free time, he enjoys applying his skills to sports analytics.

LinkedIn: https://www.linkedin.com/in/nirmal-budhathoki/

Evan Wimpey, Director of Analytics Strategy, Elder Research
Company website: elderresearch.com
LinkedIn: linkedin.com/in/evan-wimpey

Chapters
00:00 Evan introduces the show and Nirmal Budhathoki.
01:01 Nirmal talks about his unconventional path into data science.
04:48 Nirmal shares how data science supports security and mentions specific use cases.
11:00 Discussion on proactive approaches in security and the role of machine learning.
14:40 Nirmal emphasizes the need for a proactive defense strategy in cybersecurity.
15:11 Discussion about the use of large language models (LLMs) in Microsoft's work.
21:05 Nirmal talks about his active involvement in the community.
22:27 Nirmal reflects on the positive impact of mentoring.
24:24 Evan asks Nirmal about his ideas for the future.
28:17 Evan wraps up the show.

Find more show notes, transcripts, & more episodes at:
https://www.elderresearch.com/resource/podcasts/

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Kicking off Season 3 of Mining Your Own Business, Evan Wimpey sits down to chat with Bill Shube. A supply chain analytics expert with over 11 years at the LEGO Group, Bill is also the founder of Supply Chain Watchtower. Through his company Bill’s goal is helping small businesses effectively manage their inventory.

Grab a chair and tune in as he shares his story. Bill talks about how his team has successfully leveraged citizen development to gain actionable insights from data, saving thousands of hours and much more. Plus he shares how citizen development can impact on data tasks, governance, and scalability within an organization.

In this episode you will learn:

⛛ How citizen development can be used to empower business teams to play a more active role in data analytics

⛛ The need for governance initiatives to manage potential risk and ensure citizen developers stick to best practices

⛛ How no-code and low-code tools can complement existing systems, boosting collaboration rather than creating silos

⛛ Why a mindset change is crucial for all teams to be empowered to make the most of data

Quote
💬 “That's what citizen development is about; it's empowering the average user.”

Featured in This EpisodeBill Shube, Founder, Supply Chain WatchtowerBill Shube, founder of Supply Chain Watchtower (SCW), has over 11 years of experience in supply chain management and analytics at The LEGO Group, where he and his team have saved thousands of hours and spotted millions of dollars worth of planning errors through analytics automation.

His goal in founding SCW is to bring these big-business experiences and techniques to small and growing businesses, helping them understand their demand and supply. This empowers businesses to right-size inventories, avoid stock-outs and overages, and ultimately improve cash flow and profitability.

LinkedIn: https://www.linkedin.com/in/bill-shube/

Evan Wimpey, Director of Analytics Strategy, Elder Research
Company website: elderresearch.com
LinkedIn: linkedin.com/in/evan-wimpey

Chapters
00:00 Introduction
00:35 Talking about Bill’s experience with the LEGO Group and his company, Supply Chain Watchtower
01:48 Discussing Bill’s work in the supply chain
02:26 Sharing the team’s evolution to citizen developers
05:10 Talking about the challenges of siloed data
06:31 Breaking down what citizen development is and its benefits
08:52 Discussing how citizen development is implemented
12:28 Sharing the need for data governance
13:22 Talking about putting good practices in place
14:55 Discussing how user communities can foster collaboration
16:17 The importance of being on the same page
22:05 How citizen development can impact multiple areas of a business
25:06 Discussing how Bill’s team is using data tools to support daily operations
27:54 Talking about Bill’s dreams for the future
30:33 Wrapping up the show

Find more show notes, transcripts, & more episodes at:
https://www.elderresearch.com/resource/podcasts/

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In this episode Evan Wimpey chats with Mike Arney, founder of PB Vision, a company helping pickleball players gain game-changing insights. Tune in as he shares how his company got started and how his team is applying AI, machine learning, and computer vision in new ways.

Mike also shares why it’s important to value the thoughts and ideas of end users and how that collaboration makes a difference. Whether your team is embarking on a new analytics project or seeking data-driven inspiration for existing ones, we hope this episode will be insightful!

In this episode you will learn:

🔹 How analytics can be applied in diverse ways and lead to innovative solutions

🔹 The importance of considering end users with any project

🔹 How asking for feedback from end users can help refine an analytic tool

🔹 The need for balancing technical optimization with maintaining a positive user experience

Quotes💬 “I [saw] the amazing things [AI was] doing and said, ‘How can I actually get my toes in the water and understand how this works from a functional perspective?”
💬 “Here's a problem that we can solve. What would a possible solution look like? … Let's do a little bit of research. Let's experiment.”
💬 “Some people are super passionate about certain things and their voice can be pretty loud. It's great to have that passion, but you have to be very careful that does not drown out the silence or the quiet majority.”

Featured in This EpisodeMike Arney, Founder, PB Vision
Mike Arney is the founder of PB Vision, a company combining the power of AI, machine learning, and computer vision to transform video analysis in pickleball. With his background as a UI/UX designer and his love for pickleball, Mike embarked on a journey to transform the game with AI.

Through PB Vision, Mike’s team is providing insights and data-driven coaching to help pickleball players improve their performance. The platform leverages analytics to offer an in-depth level of game analysis, enhancing both training and overall playing experience.

LinkedIn: https://www.linkedin.com/in/mikearney/

Evan Wimpey, Director of Analytics Strategy, Elder Research
Company website: elderresearch.com
LinkedIn: linkedin.com/in/evan-wimpey

Chapters
00:00 Introduction
01:04 Talking about the focus of PB Vision
02:35 Sharing the story behind the launch of PB Vision
04:57 Vetting the solution and delving into computer vision
07:54 Discussing the different ways to approach a challenge
10:54 Talking about the downside of being locked into a solution
12:00 Sharing the importance of building technical skills
12:30 Discussing why UI/UX can’t be an afterthought
15:28 Evaluating user feedback
18:35 Inviting the pickleball community and developers to the table
19:47 Delving into the need for user buy-in
22:46 Looking to the future of PB Vision
30:31 Wrapping up the show

Find more show notes, transcripts, & more episodes at:
https://www.elderresearch.com/resource/podcasts/

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In this episode of Mining Your Own Business, Evan Wimpey chats with Eric Siegel, bestselling author and founder of Machine Learning Week. Tune in as he shares why businesses need to focus on machine learning projects that work in the real world.

Eric also dives into the importance of measuring the impact of machine learning projects, the need for business professionals to understand the technology, and the potential challenges associated with overhyped AI expectations.

In this episode you will learn:

🔹 The importance of deploying machine learning models in real-world business operations to capture value

🔹 Why metrics are key to getting the most out of machine learning projects and impacting business decisions

🔹 The importance of a structured end-to-end practice that enables business stakeholders to collaborate closely with data scientists

🔹 Why tools like generative AI need to be assessed by how they are helping organizations capture real value

Quotes💬 “Measuring the value is just as fundamental as developing the model. And it goes hand in hand.”
💬 “Measuring the value is just as fundamental as developing the model. And it goes hand in hand.”
💬 “We're trying to improve business operations. We're trying to provide actual business value by implementing change.”

Featured in This EpisodeEric Siegel, Bestselling Author & Machine Learning Week Founder
Eric Siegel, Ph.D. is a consultant, former Columbia University professor, and founder of Machine Learning Week. He’s also an instructor for the “Machine Learning Leadership and Practice” course, executive editor of The Machine Learning Times, and a sought-after keynote speaker. Eric authored the bestselling book “Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die” widely used in university courses. His latest book, “The AI Playbook,” is now available at bizml.com.

Eric’s interdisciplinary work bridges the stubborn technology/business gap. At Columbia, he won the Distinguished Faculty award teaching graduate computer science courses in ML and AI. Later, he served as a business school professor at UVA Darden. Eric has appeared on numerous media channels, including Bloomberg, National Geographic, and NPR, and has published in Newsweek, HBR, SciAm blog, WaPo, WSJ, and more.

LinkedIn: linkedin.com/in/predictiveanalytics

Evan Wimpey, Director of Analytics Strategy, Elder Research
Company website: elderresearch.com
LinkedIn: linkedin.com/in/evan-wimpey

**Chapters

00:00 Introduction
01:02 Diving into analytics use case examples
05:56 Measuring the effectiveness of analytics efforts
06:49 Discussing Eric’s new book and using metrics to drive decisions
11:37 Talking about best practices around end-to-end processes
14:13 Discussing the need for useful ML models and change management
16:26 Exploring the need for stakeholders and data professionals to understand each other
18:28 Talking about the Machine Learning Week conference
22:58 Delving into Eric’s predictions for future advancements and generative AI
27:24 Chatting about Eric’s dream analytics project and current projects
32:00** Wrapping up the show

Find more show notes, transcripts, & more episodes at:
https://www.elderresearch.com/resource/podcasts/

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Tune in as host Evan Wimpey chats with PayPal Data Science Lead Gulrez Khan about the art of data storytelling. With over two decades of experience, Gulrez has discovered how data storytelling can be an effective tool to bring clarity to the complex.

In this episode he shares lessons he’s learned and creative ways he's helping children and adults build data literacy.

Key Takeaways
🔹 The importance of truly understanding a business problem before starting a data science project
🔹 How data storytelling can help bridge the gap between business stakeholders and data science teams
🔹 The creative approach Gulrez Khan is using to help both children and adults grow in data literacy
🔹 How data scientists can become agents of change by not only fulfilling requests but also identifying alternative solutions based on their domain knowledge

Quotes💬 “So you become a change agent—not just do what has been asked of you but understand the business and say, ‘Hey, you know what, there is another way we can do it.’”
💬 “Data is very powerful, and with a good heart, you can make change.”
💬 “I've got a few principles that I apply in terms of data storytelling … start with the problem formulation. What is the question being asked?”

Featured in This EpisodeGulrez Khan, Data Science Lead, PayPal
With almost two decades of experience under his belt, Gulrez Khan is a data-wrangling extraordinaire, who has a knack for turning boring numbers into captivating stories. When he's not busy working, you can find him digging through open datasets like they're buried treasure or passing on his data visualization skills to the next generation in the hopes of creating a world of data-literate children.

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 and has recently written “Drawing Data with Kids,” a book to cultivate data literacy in children and adults alike.

LinkedIn: linkedin.com/in/gulrezkhan-data

Evan Wimpey, Director of Analytics Strategy, Elder Research
Company website: elderresearch.com
LinkedIn: linkedin.com/in/evan-wimpey

**Chapters

00:00 Introduction
01:09 Working as a data science leader at PayPal
02:17 How and why PayPal started a data science community
03:41 Creating sparks in data science projects
09:08 What it means to become a change agent
09:55 Discussing the principles of effective data storytelling.
11:27 How data storytelling facilitates better communication
15:02 Data storytelling as a marketable job skill
18:09 Gulrez’s book, “Drawing Data with Kids,” and data literacy
24:45 Using data for good
25:59** Wrapping up the show

Find more show notes, transcripts, & more episodes at:
https://www.elderresearch.com/resource/podcasts/

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Join us as we chat with our founder Dr. John Elder. In his decades of experience, he's witnessed the ebb and flow of numerous data analytics projects. And along the way he’s discovered keys to achieving success.

Key Takeaways
🔹 Why successful data analytics projects rely on teamwork, honesty, and perseverance
🔹 How Elder Research has approached challenges and helped organizations move from idea to implementation
🔹 The need to choose the right problems to work on as an organization
🔹 The impact of involving leaders in data-driven initiatives to achieve success

Quotes

💬 “It helps to have teams that are honest in their feedback … You see it as a wonderful thing if they find a flaw and save you time and help you solve the problem.”
💬 “Knowing the degree of accuracy and the cost of a mistake is so important anytime you address a new problem.”
💬 “A lot of times for us it's technology transfer. We'll solve a problem and then we'll teach the client how we did it.”

Featured in This EpisodeJohn Elder, Founder, Elder ResearchDr. John Elder founded Elder Research, America’s largest and most experienced data science consultancy, in 1995. They’ve solved hundreds of challenges for commercial and government clients. Dr. Elder co-authored three books — on practical data mining, ensembles, and text mining — two of which won “book of the year” awards. John has created data mining tools, was a discoverer of ensemble methods, chairs international conferences, and is a popular workshop and keynote speaker.

Dr. Elder earned Engineering degrees from Rice and UVA, where he’s an Adjunct Professor. He was named by President Bush to serve 5 years on a panel to guide technology for national security. Lastly, John is grateful to be a follower of Christ and the father of five.

LinkedIn: linkedin.com/in/johnelder4

Evan Wimpey, Director of Analytics Strategy, Elder Research
Company website: elderresearch.com
LinkedIn: linkedin.com/in/evan-wimpey

**Chapters

00:00 Intro
00:54 Factors that separate successful and unsuccessful analytics projects
02:31 How Elder Research has built project success over time
05:09 The importance of leadership involvement to achieve success in projects
07:14 John’s favorite projects
13:30 The need to follow up after a project is successfully completed
15:19 The growth and impact of generative AI
18:36 Reliability of analytic tools
19:54 John’s transition into a new role at Elder Research
21:02 The vital steps to problem-solving
22:33 How John’s early work in target shuffling was included in Dr. Eric Siegel’s “Predictive Analytics” book
25:05 John’s current interests, including advancement of his global search algorithm and investment modeling
29:43** Wrapping up

Find more show notes, transcripts, & more episodes at:
https://www.elderresearch.com/resource/podcasts/

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Sarah Kalicin from Intel shares insight on how data science can positively impact the way teams work on this episode. As an HR Data Scientist at Intel Corporation, Sarah brings a wealth of experience and knowledge to our discussion. Her journey is a fascinating blend of industrial statistics and diverse industry experience, including roles at Ford Motor Company, Nabisco Craft Foods, and medical device companies.

Her current role in HR data science showcases her ability to bridge the gap between business problems and data solutions. Join us as we explore Sarah's insights into the ever-evolving landscape of data science and how it can transform organizations.

Key Takeaways
🔹The importance of fully understanding a business problem before determining a data-driven solution
🔹How effective training of team members can lead to tangible business results
🔹 Why data literacy and clear communication with stakeholders is crucial for successful adoption and impact of a data-driven solution
🔹The importance of considering the user experience of new solutions that are implemented

Quotes

💬 “It's really about identifying that data that you need and then trying to figure out how do you get that data to your stakeholders in a way that they can consume it, and then start managing success.”

💬 “Understand that business problem and then be able to translate that to a data problem so that we can figure out what the value is for the company.”

💬 “Just because you took a class and you like the class doesn’t necessarily mean it translates into skills or behaviors or business impact … What kind of measurements do we need to do in order to be able to showcase that these classes are having impact …?”

Featured in This Episode

Sarah Kalicin, HR Data Scientist, Intel CorporationSarah is a skilled statistical leader with deep analytical expertise and a strong background in strategic thinking and business management. With almost twenty years at Intel Corporation, she now works as an HR Data Scientist.

Sarah loves helping cross-functional teams find ways to improve commercial profitability for their organizations. And as the founder of Achieve More with Data, she is focused on helping organizations better leverage data assets to achieve greater success.

LinkedIn: linkedin.com/in/sarahkalicin

Evan Wimpey, Director of Analytics Strategy, Elder Research
Company website: elderresearch.com
LinkedIn: linkedin.com/in/evan-wimpey

Chapters

00:00 Introduction
00:26 Sarah’s diverse career background and journey to becoming an HR data scientist
03:23 Sarah’s love for helping organizations get more value from their data
04:52 An example of an HR data scientist project
06:11 The need for effective team training to achieve business goals
10:37 The balance of data analytics, privacy, and ethical considerations
12:19 Getting buy-in from key stakeholders
16:06 Data scientists at Intel and the importance of training teams based on their experience
20:22 The impact of AI at Intel
22:30 The importance of considering the user experience of a new application
24:56 Building a data-driven culture within an organization
29:02 Sarah’s desire to help organizations make strategic decisions using data analytics

Find more show notes, transcripts, & more episodes at:
https://www.elderresearch.com/resource/podcasts/

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Grab some coffee and tune in as Jack Levis, Chief Product Strategy Officer for ESP Logistics Technology, chats with Evan Wimpey on the Mining Your Own Business podcast.

Jack spent over 40 years at UPS, making significant contributions to their use of analytics and technology. In this episode he reflects how UPS evolved as a data-driven organization and how change management was a key part of that process.

You’ll also learn why leaders should look beyond buzzworthy technologies to reach organizational goals and empower their teams.

Key Takeaways
🔹 Why successfully implementing new technologies and strategies requires a focus on changing mindsets, processes, and communication throughout an organization.
🔹 The importance of investing in a team’s skill development to drive change and innovation.
🔹 Why leaders should have a general understanding of analytics to make informed decisions about technology and implementation.
🔹 The importance of starting with the end goal in mind when it comes to choosing effective data-driven tools.

Quotes
💬 “In today's world, a data-driven organization is one that can turn on a dime ... Everything isn’t just making decisions from data; the data is driving those decisions.”
💬 “If you're really data-driven, can you with a straight face say, ‘My data is as important to me as my product.’”
💬 “Leaders need to understand analytics. That doesn't mean that you need to be able to do it yourself … But you need to understand the different types of analytics that exist.”

Featured in This EpisodeJack Levis, Chief Product Strategy Officer, ESP Logistics Technology During his 40-plus years at UPS, Jack was responsible for the development of operational technology solutions. These solutions required advanced analytics to reengineer processes, streamline the business, and maximize productivity. He currently serves as Chief Product Strategy Officer for ESP Logistics Technology.

Jack is a frequently requested speaker for business executives and organizations. He has been featured in many publications and media productions, including a TED Talk on Innovation.

LinkedIn: linkedin.com/in/jacklevis

Evan Wimpey, Director of Analytics Strategy, Elder Research
Company website: elderresearch.com
LinkedIn: linkedin.com/in/evan-wimpey

Chapters
00:00 Introduction
01:22 Jack's background and discussing what it means to be data-driven
04:20 UPS's journey to becoming a data-driven organization
09:02 The significance of innovation in execution rather than just the idea
11:20 Focus on change management as a crucial aspect of successful technology adoption
12:50 Four pillars to data-driven innovation at an organization
16:57 The importance of understanding analytics and the value of focusing on the decision, not just the tools
22:29 Why listening and communicating effectively is a crucial part of implementing change
24:28 How outcomes improve as a team takes ownership of data-driven change
25:50 Advice for how to approach implementing a new data or analytics initiative
27:02 Why leaders should understand analytics for better decision making
29:08 The importance of keeping the end goal in mind
31:05 Jack’s thoughts on ways to apply data and analytics to solve other challenges
35:40 Discussing ESP Logistics Technology and wrapping up the show

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In the ever-evolving world of data science, creativity is key to problem-solving. In this episode Mary Stimson, Big Data Analyst at Southwest Airlines, shares her journey into data science and how creativity has come into play in her role. Tune in as host Evan Wimpey chats with Mary about the importance of adaptability and innovation in data science, and how these elements play a role in creating solutions.

Key Takeaways
🔹The role of big data at Southwest Airlines and how it’s being used to effectively evaluate constantly emerging data points
🔹How AI and ML are helping Southwest Airlines optimize operations and address complex challenges
🔹The importance of understanding the needs of key stakeholders to create data-driven solutions
🔹How creativity in data science plays a significant role in solving problems

Quotes
💬 “Data science is solving new problems. And when it comes to solving new problems, there has to be a willingness to create new solutions, new ideas.”
💬 “Big data means that there's always a frontier of unknowns—a frontier of that infinite growth that can be discovered. I think that’s also what’s fun about big data.”
💬 “I always say that data science should provide information and not answers to people. So I love what I get to do because it allows that intuition to interact with what the computers are telling us.”

Featured in This EpisodeMary Stimson, Big Data Analyst, Southwest AirlinesMary Stimson is a Big Data Analyst and UI/UX developer at Southwest Airlines. She graduated from Texas A&M in 2019 as a Business Honors and Accounting major. Today Mary works on the Enterprise Data Science Center at Southwest Airlines, where she problem-solves in an internal business consulting role, performing analytics for the financial planning and analytics department and building interactive web tools to bring data insight to human decisions. Mary works with such tools as SQL, Python, ReactJS, HTML, Tableau, Alteryx, and Collibra. As a lifelong learner, Mary is also a part-time student at Dallas Theological Seminary, pursuing her masters in Christian Studies. Mary currently holds one pending utility patent regarding an animal computing interface.
LinkedIn: linkedin.com/in/mary-stimson-693912145/

Evan Wimpey, Director of Analytics Strategy, Elder Research
Company website: elderresearch.com
LinkedIn: linkedin.com/in/evan-wimpey

Chapters:
00:00 Introduction
00:36 Mary’s background and her journey to the role of big data analyst and UI/UX developer at Southwest Airlines
02:13 Developing models using human intuition - Mary's transition from business analyst to data science role and involvement in metadata management.
03:30 Discussion on the types of users and departments Mary's team collaborates with at Southwest.
05:59 The diverse knowledge sets within Mary's team and the value of different backgrounds.
09:46 Creativity and its role in data science
13:56 How Mary’s team at Southwest Airlines invests time on project work and personal growth
16:37 Further defining what big data means and Mary’s role as an analyst
19:14 AI and ML implementation at Southwest
22:06 Discussing data-driven ideas for the future
25:03 Sharing ways to continue the conversation on data science

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In this episode you’ll meet Kathryn Walter, a senior operations research analyst at Avista Corporation. Kathryn shares an inside look at how operations research is making an impact at this utilities company based in Spokane, Washington.

Tune in as our host Evan Wimpey chats with Kathryn about how she got into the field and how her team has used operations research to make informed and cost-saving decisions. With a dash of humor and tips to drive organizational change, you won’t want to miss this episode! Key Takeaways:
🔔 How operations research is being used to guide decisions at Avista Corporation
🔔 How the field of operations research has evolved over time
🔔 The applications of operations research in different industries
🔔 Ways to approach moving toward data-driven change at an organization

Quotes:
💬 “I feel like one of the beauties of operations research is that it’s so industry neutral. You can apply it anywhere.”
💬 “People are eager to make things better … So if it’s easier and it's also helping, people are going to embrace it.”

Featured in this episode:
Kathryn Walter, Senior Operations Research Analyst, Avista Corporation Kathryn Walter is a senior operations research analyst who works on mathematical models supporting better decision making at Avista Corporation, a utilities company in Spokane, Washington. She is a former optimization consultant, a former military officer, and a holder of Master of Science and Bachelor of Science degrees in operations research. She is a Certified Analytics Professional (CAP) and currently serves on the Analytics Certification Board and on the INFORMS Board of Directors.
LinkedIn: linkedin.com/in/kathrynwalter

Evan Wimpey , Director of Analytics Strategy at Elder Research
Company website: elderresearch.com
LinkedIn: linkedin.com/in/evan-wimpey

Chapters:
00:00 Introduction
00:37 Kathryn’s background and how she got into operations research
02:30 Discovering the impact of optimization and building an effective model
04:06 Investigating and refining the model
05:31 The evolution of the field of operations research
08:38 The benefits and flexibility of operationsresearch skills across industries
10:57 A willingness to change the way things are done
12:56 Approaching challenges in change management
15:47 The growth of data science at Avista
18:17 Factors to think about when building an operations research team
21:46 Looking to the future of operations research at Avista
24:56 Sharing some math humor

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Kicking off the second season, Evan Wimpey chats with Nechama Katan, director of data science for a large pharmaceutical company.

In the episode Nechama shares how her journey in the world of analytics and pharmacy began and what she’s learned along the way. From using risk-based monitoring in clinical trials to building effective teams, Nechama’s experience has led to data-driven solutions and better communication.

Come along as Nechama shares her experience in pharmalytics and gain insight on the importance of data literacy in an organization. We hope this episode sparks fresh ideas and perspective!

Key Takeaways:
🔔 How data science comes into play in pharmacy
🔔 The benefits of different perspectives on a team
🔔 How effective data evaluation can impact real people
🔔 The importance of building data literacy to reach a common goal

Quotes:
💬 “Use everything as a learning opportunity. People learn with analogies. Always have an analogy or two or three.”
💬 “In data science in general, you can either think and reason about data or you can’t, and then you can either learn the business or you can't. And if you can learn the business, you can learn any business. If you’re motivated and curious enough about the business, you can learn the business.”
💬 “Programming doesn’t scale; thinking does. And so anything that cuts out and lets you think and leverage kind of what we do well then is worth it.”

Featured in this episode:
Nechama Katan, Director of Data Science, Pharmaceuticals Nechama Katan is a highly skilled problem solver who uses innovative techniques to help organizations overcome complex technical and process-related issues. With experience working in various industries, including high and low tech, finance, and pharmaceuticals, Nechama has honed her expertise in developing code, creating prototypes, and driving business conversations which solve technical challenges for the leaders she supports. She is the director of data science for a large pharmaceutical company and founder of Wicked Problem Wizards.
LinkedIn: linkedin.com/in/nechama

Evan Wimpey , Director of Analytics Strategy at Elder Research
Company website: elderresearch.com
LinkedIn: linkedin.com/in/evan-wimpey

Chapters:
00:00 Introduction
00:36 Nechama’s background and how she got into analytics and pharmacy
01:44 Analyzing high and low risks in the clinical trial space
04:24 Using risk-based monitoring as a method to identify systemic problems
06:04 Defining the team roles and skills needed to be effective
08:17 The importance of having different perspectives represented on a team
11:58 Building data literacy across an organization
15:40 Learning to speak each other’s language to build buy-in
17:27 The impact of AI tools on work productivity
19:55 Approaching changes to processes in pharma analytics
21:24 Dreaming of new horizons in pharma analytics

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On the show today, Evan sits down with Isaac Collazo, Vice President of Analytics at STR. Isaac shares how he got started in the travel and hotel industry, and how he’s seen data analytics in the hotel space take off and advance over time. He discusses what type of data STR works with and how they strategize delivering key insights to their clients. Isaac and Evan also discuss what it takes to be a successful data analyst on these types of projects, highlighting crucial characteristics such as creativity, play, and drive. Finally, Isaac speaks on opportunities he sees in the hospitality industry and in his current role at STR.

Key Takeaways: 
🛎️ How STR provides insights from data to inform hotel progress and strategy
🛎️ Opportunities for increased consumer understanding via hotel data
🛎️ The importance of creativity and perseverance as a data analyst
🛎️ How increased time and resources allow for long-term high-level analysis

Quotes: 
💬 People who are on the data side have to be creative and have to just love to play with data because if you don't, then it's a pretty boring job.
— Isaac Collazo

💬 I think there's a lot more space – not ability, but opportunity to do more with that data [in the hotel space].
— Isaac Collazo

💬 I'm looking for is someone who is very creative and who has more of a strategy mindset. Not only can they use data, but they understand that we've got to get to a decision or an outcome.
— Isaac Collazo

Featured in this episode: 
Evan Wimpey , Director of Analytics Strategy at Elder Research.
Company website: www.elderresearch.com 
LinkedIn: https://www.linkedin.com/in/evan-wimpey/ 

Isaac Collazo, VP of Analytics at STR

As a seasoned analytics professional, Isaac Collazo believes that data is not everything and understands the value of relationships when immersing himself in an industry to truly understand its people and experiences.

He is the VP of Analytics at STR where he solidifies the company's analytical methodology, assists with integration into CoStar's digital platform, and unifies products and services to provide a single, reliable source of knowledge for the Hotel Industry. He is an expert in delivering meticulously curated insights based on industry knowledge and encourages others to make a personal investment in the insights they uncover. His easy-to-digest knowledge ensures recipients can apply it to their own business performance for an informed and successful future.

LinkedIn: https://www.linkedin.com/in/isaaccollazo/

Chapters: 
00:00 Introduction 
00:57 Isaac’s background in the travel & hotel industries
02:52 How Isaac got started in data collection for hotel decision-making
05:35 The type of data and benchmarking that STR provides
11:55 What it takes to be successful on an analytics team
16:54 Insights from consumer data: seeking to learn more
20:05 Working with data in the hotel industry: the importance of time & resources
21:18 A new mindset

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Today we welcome to the show Eric Sims, Senior Analyst of Strategy and Analytics at LendingTree, an online lending marketplace. Eric and host Evan Wimpey discuss the process of getting projects off the ground, ways of making analytics accessible to business units and stakeholders, and gaining alignment on business endeavors. Eric also shares how he has been enriched by personal investigative data projects, and tips for others endeavoring on a side project. Finally, Eric speaks about the importance of understanding one’s clients and truly putting oneself in their shoes. We hope you enjoy this insightful episode!

Key Takeaways:
🌳 How LendingTree communicates and coordinates its projects amongst various teams
🌳 How to make analytics accessible to non-technical stakeholders
🌳 How M&Ms helped team members feel more comfortable with data concepts
🌳 Ways to truly understand and immerse oneself in a customer’s experience

Quotes:
💬 The most valuable thing to me, and I think to any company... is knowing your customer – and knowing not just your customers’ data, but knowing your customers’ voices and their problems.

— Eric Sims

💬 I really like teaching and showing and sharing. Here's how you can do it. Ask me when you forget later. And we'll just keep practicing through it.

— Eric Sims

💬 The most important thing when it comes to you have a list of projects you want to do: pick one you like, just pick one that you're interested in. Because even if you are working on something you like, sometimes it's a slog. And so you gotta want it.

— Eric Sims

Featured in this episode:
Evan Wimpey
Director of Analytics Strategy at Elder Research.
Company website: www.elderresearch.com
LinkedIn: https://www.linkedin.com/in/evan-wimpey/

Eric Sims, Sr. Analyst, Strategy & Analytics

Eric’s personal brand can be summed up in one word: originality. He’s always in search of new and interesting ways to approach learning and problem solving. He currently works as a Sr. Data Analyst at LendingTree, supporting the Small Business vertical, and in his free time he builds machine learning applications. He’s also interested in how recommenders can be used to connect social networks, and his latest project is a tea recommender system.

Besides working and building side projects, Eric contributes to the LinkedIn data science community, plays paintball in VR, travels with his partner, and does normal home stuff (#RealLife).

https://www.linkedin.com/in/ericsims2/

LendingTree is hiring! Check out current opportunities here:

https://www.lendingtree.com/careers/jobs/

Chapters:
00:00 Introduction
01:00 Eric’s background and how he got into analytics
03:12 Eric’s role and projects at LendingTree
06:47 How do new projects start?
09:52 Biggest challenges to getting projects from ideation to final product
13:54 Communicating data terminology: making analytics accessible
17:41 Lightbulb moments: making data make sense with M&M’s
21:33 Eric’s personal projects: learning new skills
28:33 Truly understanding the customer

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On today’s episode, Francisco Rius gives us an exclusive look into the data world of the ever-popular video game Minecraft. Francisco is the Head of Data Science and Data Engineering with a background in business economics and marketing. He and host Evan Wimpey discuss his journey to his current role at Minecraft and how data insights are used to improve each player’s experience. Francisco shares how a relationship-oriented approach that prioritizes social listening is crucial to the game’s long-term success. They discuss what it takes to have a strong data team in a creative field. Finally, Francisco wraps up by sharing his vision for the intersection of AI in video games. Tune in for an enlightening and inspiring conversation.

Key Takeaways:⛏️ How data have provided insights to improve Minecraft over the past twelve years
⛏️ How Minecraft practices social listening to provide the best game experience possible for their players
⛏️ The different skills and expertise needed for a strong, creative data science team
⛏️ How AI opens new worlds of possibilities for the video game industry

Quotes:💬 A lot of times the relationship aspect of working in big corporations is multiplied times 10 when you're working in creative industries as well. So that relationship aspect is really important...[a new hire’s] ability to make friends and the emotional intelligence aspect of working shoulder-to-shoulder with folks that see the world in a little bit of a different light.
— Francisco Rius

💬 I feel like data science and analytics teams are maturing to be subject matter experts alongside decision-makers. And we're seeing a little bit of a reduction in the number of layers of translation that happens between the data insight and the decision.
— Francisco Rius

💬 The strategy for Minecraft really is: Hey, we want to create a relationship with our players that will last for the next 10, 20, 50 years.
— Francisco Rius

Featured in this episode:Evan Wimpey
Director of Analytics Strategy at Elder Research.
Company website: www.elderresearch.com
LinkedIn: https://www.linkedin.com/in/evan-wimpey/

Francisco Rius
Head of Data Science and Data Engineering, Minecraft at Microsoft
Francisco leads the data science and data engineering efforts for one of the biggest video game franchises in the world. He is passionate about the intersection of art and science, where video game makers can use data-driven insights to improve player experiences and outcomes. Minecraft is one of the best-selling video games of all time, with over 120 million active players per month. Before joining the Minecraft franchise at Microsoft, Francisco led the analytics group for Electronic Arts’ FIFA, NHL, and UFC franchises.
LinkedIn: https://www.linkedin.com/in/franciscorius/

Chapters:00:00 Introduction
02:03 Francisco’s background and how he got to his role at Minecraft
06:01 How data has provided insights over Minecraft’s lifetime
09:44 Social listening at Minecraft
12:54 Partnerships in academia and Microsoft
15:26 Art & Science: fostering relationships between creatives and data scientists
19:46 A well-rounded data science team: working in tandem with subject matter experts
24:35 Balancing short-term and long-term change
28:41 Francisco’s vision for the future: AI in video games

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On today’s episode, we welcome Rob Horrobin to the show. Rob is Pacific Life’s AVP of Data Science where he leads the Advanced Analytics Center of Enablement. You’ll hear Rob and Evan discuss the Center of Enablement and how it supports data science efforts across the organization. Rob describes their "Do-Partner-Support" strategy, which governs how they support internal clients while also creating possibilities for true transformation. We’ll also hear about the intersection between actuarial science, data science, and engineering, as well as the importance of varied skill sets and backgrounds in data science professions. Rob and Evan end by exploring the future paths of analytics capabilities.

Key Takeaways:

📈How the Center of Enablement supports learning and transformation
📈How experience in multiple fields can build complimentary skillsets for successful data science projects
📈 How to successfully partner with clients to implement a data project
📈Tips for those currently in or seeking careers in data science

Quotes:

💬 I was a career-switcher, I was in pre-business school where I actually did mechanical engineering in the shipbuilding industry.
— Rob Horrobin

💬 Starting with the end in mind — it's great to build a model, but you have to make sure that it's fit for its purpose. In the end, how are the folks gonna consume it?
— Rob Horrobin

💬 If an actuary's a quarterback, a data scientist is a running back and an engineer could be a tight end — very complimentary skills, but not one-to-one. Where I get really excited is when an actuary and a data scientist and a data engineer rally around a problem and say, “how do we kind of make this thing work better?”
— Rob Horrobin

Featured in this episode:

Evan Wimpey
Director of Analytics Strategy at Elder Research.
Company website: www.elderresearch.com
LinkedIn: https://www.linkedin.com/in/evan-wimpey/

Rob Horrobin
AVP of Data Science at Pacific Life
LinkedIn: https://www.linkedin.com/in/robertmhorrobin/

Chapters:
00:00 Introduction
01:04 Rob’s background and how he got into data science
02:53 Rob’s role in the Center of Enablement: The Do-Partner-Support Model
05:47 Scope of the team’s work and how teams are structured
11:03 Actuaries, engineers, and data scientists: kindred spirits
15:04 Advice for those considering launching a data science effort
19:04 Advice for those interested in a career in data science
21:08 Standard certifications for data scientists in the future?
27:58 Rob’s vision: How can analytics create entirely different business models?

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We’re kicking off the new year with Rick Hinton, founder and CEO of Valerius Consulting. Valerius provides adaptive change management services, especially for data analytics organizations. Rick and Evan discuss what change management looks like in the analytics world and why it’s necessary for growth. We learn about the importance of the words “trust”, “deliberate”, and “gradual” as they relate to implementing effective change. Rick wraps up by teaching us the CALM process for change management (Communication, Alignment, Learning, & Measurement) and providing practical advice for businesses.

Key Takeaways:

📈How change management is synonymous with continuous learning

📈Common barriers to change and how to foster an environment of trust

📈The pacing of effective change within an organization

📈The CALM process for change management and how to apply it to your business (Communication, Alignment, Learning, Measurement)

Quotes:

💬 Trust is not an initiative that you plug in. It's the way you operate every day.

— Rick Hinton

💬 The business folks, the technical folks, and the data science and analytics folks - the challenge has always been them speaking the same language…The change challenge is they don't understand each other…That language barrier is a big impediment to change.

— Rick Hinton

💬 We’re not just changing for change’s sake. We're trying to improve, we're trying to learn, and that ties in really well with what people in the data science realm and analytics realm do. That's what they do every day, to try to interpret output and make decisions and learn from it.

— Rick Hinton

Featured in this episode:

Evan Wimpey
Director of Analytics Strategy at Elder Research.
Company website: www.elderresearch.com
LinkedIn: https://www.linkedin.com/in/evan-wimpey/

Rick Hinton

Founder and CEO at Valerius Consulting

Valerius Consulting: http://www.valerius.us/

LinkedIn: https://www.linkedin.com/in/rfhinton/

Chapters:

00:00 Introduction
01:16 Rick’s background and why he founded Valerius Consulting
03:05 What is change management in analytics?
05:19 Change in data analytics, past and present
10:41 Analytics team structures and communication barriers
14:18 Building trust
17:54 Too much too fast? Pacing of change
21:42 The CALM process
29:12 Conclusion

Transcript & Show Notes: https://bit.ly/myob14

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Today’s very special guest joins us all the way from the North Pole. Elf is the Head of Data and Analytics at Santa’s Workshop. Elf and Evan discuss how the elves got into data analytics to solve complex problems, from toy manufacturing to efficient delivery routes. Elf describes how data analytics has changed over the centuries that he’s worked at Santa’s workshop. He also provides insight on increasing buy-in from key stakeholders. Evan and Elf wrap up by discussing Elf’s vision for the future of analytics to increase the amount of joy and cheer spread throughout the world. Tune in for an exclusive look into the magic of Santa’s Workshop!

Key Takeaways:

🎁 How to ensure buy-in from stakeholders by centering them in the process

🎁 How Santa’s workshop tackles route optimization

🎁 Why data is needed to adapt to evolving business challenges, from chimney shortages to toy demands

🎁 Current challenges that Santa’s workshop faces and how they hope to solve them

Quotes:

💬 “If we're not able to measure some improvement in what the data and analytics are able to do, then it's really hard to get that buy-in from the stakeholders.”

💬 “And it's really the data that we collect year over year, and we also do some third-party data acquisitions to understand some of these changing trends, but the data is really helping to drive those delivery decisions.”

💬 “I would really like to focus on that last mile, the most difficult children to deliver to, to make sure that every child that is hopeful of a Christmas gets something under their Christmas tree, something that they can open to have a little bit of magic there on Christmas morning.”

Featured in this episode:

Evan Wimpey

Director of Analytics Strategy at Elder Research.
Company website: www.elderresearch.com
LinkedIn: https://www.linkedin.com/in/evan-wimpey-40469b47

Elf

Head of Data and Analytics at Santa’s Workshop

Elf is the Head of Data and Analytics at Santa's Workshop and is passionate about using data to spread joy and cheer. He has extensive experience leading his team at the North Pole location for the past 1,145 years. He has a background in retail management and carpentry. In his free time, he enjoys collecting vintage candy canes and watching Lord of the Rings.

00:00 Introduction
01:02 Elf’s background in data analytics
03:37 Scope of data analytics in the business model
05:54 How have data analytics problems evolved over centuries of work?
08:03 Ensuring buy-in from stakeholders: creating analytics champions
12:07 Do freezing temperatures affect computing?
14:08 If Elf could tackle one analytics question, what would it be?

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Joining us today is Selma Dogic, Senior Manager of Analytics at Carter’s.
Selma discusses strategies for the implementation and adoption of data solutions in a large multi-brand business.
Selma and Evan also talk about how data teams coordinate with the business side of Carter’s to deliver solutions.
Finally, Selma discusses future directions for data analytics at Carter’s, which includes the importance of building a healthy data culture and data literacy at organizations.
Tune in to hear more of this informative and thoughtful episode!

Key Takeaways:
🍼How teams are structured and projects are prioritized for optimal results

🍼How data science effectively operates in the retail space to provide crucial business insights

🍼Who Carter’s markets to and how they build customer trust

🍼The importance of a healthy data culture and data literacy for all

Quotes:
💬“Not only are we doing the data engineering and feature engineering to help train better models... but we're also learning ourselves.” - Selma Dogic

💬“When you identify a champion, somebody who gets it just as much as you do, but they're on the other side of it, that can transcend your analytic strategy and really drive adoption.” - Selma Dogic

💬“I'm really interested in building out well-structured and well-maintained data domains for the organization and having the business users equipped to leverage those data domains to make their business decisions.” - Selma Dogic

Featured in this episode:
Evan Wimpey
Director of Analytics Strategy at Elder Research
Company website: www.elderresearch.com
LinkedIn: https://www.linkedin.com/in/evan-wimp...

Selma Dogic
Senior Manager of Analytics at Carter’s
LinkedIn: https://www.linkedin.com/in/selmadogic

Chapters:
01:12 Selma’s background and how she got started at Carter’s
03:33 What is Carter’s?
04:45 Selma’s role at Carter’s
06:43 Business team structure and strategic decision-making
11:37 How are data results delivered to businesses?
14:59 How to implement data strategies – identifying champions
19:18 Carter’s data sources
22:47 How Carter’s gets creative with marketing
24:40 Promoting data literacy and data culture

For more info and podcast transcript:
bit.ly/myob12c

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In this episode, we are joined by Zach Wasielewski, Senior Data Scientist at General Mills. Evan and Zach discuss Zach’s role in strategic revenue management and how data science teams operate cohesively in a large multinational company like General Mills. They discuss change management strategies and how General Mills has grown as an established company. Zach also shares about his background in financial services and discusses his transition to consumer product goods.
We recommend enjoying this episode over a bowl of Cinnamon Toast Crunch. Tune in for an entertaining and informative conversation!

Key Takeaways:

🥣How various business units across the company strategize together through knowledge-sharing
🥣How an established company like General Mills has incorporated data science strategies and grown over time
🥣What strategic revenue management looks like and what challenges they work to solve
🥣How data teams interact with the supply chain and future directions for data science projects in supply chain shortages

Quotes:

💬“We're learning as we go and building on top of what we've got in place to make sure that anything that they could be using and testing and looking at historically is what we're also incorporating into our models.” - Zach Wasielewski
💬“The data science group, especially in the SRM space, is all very interconnected... we are all very much one team, even if we work in different things, different products, different stores.” - Zach Wasielewski
💬“We are one giant cohesive organization...What I learn in my day-to-day job is something that is important for them and what they learn is equally as important for us.” - Zach Wasielewski

Featured in this episode:
Evan WimpeyDirector of Analytics Strategy at Elder Research
Company website: www.elderresearch.com
LinkedIn: https://www.linkedin.com/in/evan-wimpey-40469b47

Zach Wasielewski
Senior Data Scientist at General Mills
LinkedIn: https://www.linkedin.com/in/zachary-wasielewski/
Zach’s athletic accomplishments: https://datacolumn.iaa.ncsu.edu/blog/2019/04/25/intramural-softball-winners-2019/
Join General Mills’ team: https://careers.generalmills.com/careers

Chapters:
01:51 Zach’s background and how he got started at General Mills
04:00 Zach’s role at General Mills
08:55 Data flows at General Mills
10:41 How are data teams organized at General Mills?
15:48 How change is implemented at General Mills
18:36 From finance to consumer goods – how did Zach get up to speed?
24:34 What data science project would Zach like to tackle in the future?

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Joining Evan on the data science slopes is Gus Kaeding, Senior Manager of Data & Analytics at U.S. Ski & Snowboard. Gus shares how his background in professional skiing, coaching, and the U.S. Olympic Committee informs the work he does today. He discusses the types of challenges athletes use data to solve and the tech infrastructure for sharing insights with coaches. Gus also explains some of the challenges with data collection and how he envisions data collection in the future. Gus and Evan wrap up by discussing the future of the U.S. team. Tune in for an invigorating episode on sports analytics!

Key Takeaways

How data benefits athletes and team development and creates champions
How data insights are delivered to coaches and athletes
Challenges in data collection and how new innovations are addressing them
Insights into the future of the U.S. Ski & Snowboard team

Quotes

“It's about growing those sample sets - whether they are in the gym or at home, during recovery, or on the slopes - as much as we can. And solving for “how do you create those results?” whether good or bad, and incrementally improving those.” - Gus Kaeding

“Anytime you're talking about an analytics kind of project, whether it's in business or whether it's in an MBA program, buy-in's always tough and there's a number of different stakeholders you have to address. So, most of the projects that I work on are sourced from a question that comes from the coaches” - Gus Kaeding

“Now we really want to make sure we're scaling. And when we identify some of those [young] athletes, to support them in a way where they're bringing their friends, their community, and the sport along with them as well.” - Gus Kaeding

Featured in this episode

Evan Wimpey
Director of Analytics Strategy at Elder Research.
Company website: www.elderresearch.com
LinkedIn: https://www.linkedin.com/in/evan-wimpey-40469b47

Gus KaedingSenior Manager of Data & Analytics at U.S. Ski & Snowboard
LinkedIn: https://www.linkedin.com/in/kaedinggus/

Chapters
01:40 – Gus’s background and how he got into ski & snowboard data
03:49 – What types of data is collected for ski and snowboard?
06:23 – Video tracking for data collection: challenges and benefits
09:10 – How are analytics efforts prioritized?
13:38 – How are data insights delivered to coaches and athletes?
16:22 – Challenging conventional norms
20:03 – Does ski data change the way you ski?
22:47 – What’s one data question you aspire to answer?
27:12 – For the fans of U.S. Ski & Snowboard: what to look out for

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Description

In this episode, we hear from April Wilson, Head of Career Services at the Institute for Advanced Analytics. April’s ten years of career expertise inform the insights she provides on hiring in the analytics world. She shares the most desirable skills companies look for when hiring, the importance of having equal expectations, and the benefits of having an open mind when evaluating candidates – the skills they have may be beneficial in unexpected ways. Evan and April wrap up by discussing three tips for teams looking to hire.

Key Takeaways

The primary skills that employers are looking for when hiring
How companies and prospective hires can manage their expectations
The surprising truth about industry experience
The importance of an open mind when hiring

Quotes

“I want you to know the technology, I want you to understand it, but I need for you to be able to talk about it and explain it as well.” - April Wilson

“[The skills employers are looking for are] like a three-prong: it’s that technology, that communication, and that ability to present to stakeholders. And be able to present the right information to your stakeholders.” - April Wilson

“Remember, there's no such thing as unicorns. It takes a team, a data science team to do well.” - April Wilson

“[Employers] don't care if you're straight outta undergraduate or if you've been a college professor for 15 years, as long as you have the skills that they need, you fit the organization, and you feel like you have something to contribute.” - April Wilson

Featured in this episode

Evan Wimpey

Director of Analytics Strategy at Elder Research.

Company website: www.elderresearch.com

LinkedIn: https://www.linkedin.com/in/evan-wimpey-40469b47

April Wilson

Head of Career Services at Institute for Advanced Analytics

If you are a company interested in learning more about recruiting analytical talent you can email April at april_wilson@ncsu.edu

If you are an individual that wants to hone the skills you need to get the job you want, email April at clearpathcs@gmail.com

LinkedIn: https://www.linkedin.com/in/april-wilson-8125a55/

Chapters

01:09 April’s background and the work she does in career services

02:27 The skills sets individuals need when entering and exiting a master’s program

05:10 How the demand for certain skills has changed over time

09:46 Commonly overlooked skills and company expectations when hiring

15:12 Importance of accurate job descriptions and equal expectations

16:44 Prospective hires with much vs. little experience – should they take different approaches?

21:09 Does industry experience make a difference?

26:13 Three tips for teams looking for candidates

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Description

Welcome to Mining Your Own Business Podcast. Each episode will bring in data and analytics gurus from around the world as they regale us with their data analytics stories and enlighten us with their secrets for how to turn data into actionable insights.

Today’s episode features Nick Wan, Director of Analytics for the Cincinnati Reds Baseball Team.

Evan and Nick’s conversation covers the value sports analytics provides to everyone involved, from hiring personnel to coaches to players.

You’ll hear about how specific baseball rule changes affect analytics and the accessibility of sports data to analysts and fans alike.

You’ll also learn how Nick’s work landed him on the front page of the New York Times and what he envisions for the future of sports analytics.

Key Takeaways

The breadth of analytics usage in the sports world
The demand for data that is applicable and actionable
How public research can add value to the analytics world
The importance of critical thinking to ensure sound methodology

Quotes

“The goal is really to produce work that is not just strong statistically, but also applicable.” - Nick Wan

“Everyone's reading FanGraphs in the industry. It truly is a bastion of the analytics community, not just in baseball, but in sports in general.” - Nick Wan

“You do have to be a pretty sharp, critical analyst or analytics contributor to recognize when the methods are off a bit.” - Nick Wan

Featured in this episode

Evan Wimpey
Director of Analytics Strategy at Elder Research.
Company website: www.elderresearch.com
LinkedIn: https://www.linkedin.com/in/evan-wimpey-40469b47

Nick Wan

Director of Analytics, Cincinnati Reds Baseball Team
Twitch: https://www.twitch.tv/nickwan_datasci
YouTube: https://www.youtube.com/c/NickWan
Twitter: https://twitter.com/nickwan
LinkedIn:https://www.linkedin.com/in/realnickwan/
Check out Nick’s front page NYT article feature: https://www.nytimes.com/2015/02/14/upshot/how-arizona-state-reinvented-free-throw-distraction.html

Chapters

01:17 How Nick got into sports analytics from a neuroscience background
05:49 How sports data analytics teams are organized functionally
08:28 Who are the end users of sports analytics?
12:00 Demand for applicable data
13:56 Accessibility of sports data
18:21 The value of the public research field of sports analytics
24:18 How do rule changes in the game affect analytics?
29:33 Neuroscience: the future of sports analytics?

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In this episode, we are joined by not one but two data science and analytics leaders from Home Depot: John Carroll and Jon Weininger.

You'll hear them discuss their past and present roles at Home Depot as experts on analytics on different teams.

They offer insight into coordinating with different teams across the organization and how their respective backgrounds inform the way that they communicate with both technical and non-technical individuals about data.

You’ll also hear them talk about prioritization when it comes to project management and the ways they’ve learned from and adapted to challenges over their careers.

Finally, they share the “why” behind their work and their role in creating value and impact for the company as a whole.

Key Takeaways

How the data science and analytics teams have evolved over time, responding to company needs
The dynamics of the data science team and how they communicate their ideas with other data teams
Strategies and approaches to prioritizing projects in a complex environment
Common roadblocks and how to prioritize the problems that need to be solved

Quotes
My role is more strategic and supporting the good works and making sure the roadblocks are gone and our leadership understands the value of what our data science teams are working on - John Carroll

People are asking for analytic support. Therefore, more people want analytic support - Evan

Market timing is like organizational interests and kind of the likelihood of getting a strong adoption from somebody who can help to focus attention and resources - John Weininger

Featured in this episode
Evan Wimpey
Director of Analytics Strategy at Elder Research.
Company website: www.elderresearch.com
Linkedin: https://www.linkedin.com/in/evan-wimpey-40469b47

Jon Weininger
Data Science Manager, Home Depot
Linkedin: https://www.linkedin.com/in/jonweininger
https://careers.homedepot.com

John Carroll
Senior Manager MET analytics and Insights, Home Depot
Linkedin: https://www.linkedin.com/in/johncarroll652
https://careers.homedepot.com

Chapters
01:34 Jon Weininger and his work background
02:31 John Carroll and his work background
05:03 How does Home Depot structure their data analytic capability?
09:12 Communicating technical work to other teams
15:09 What are the roadblocks?
21:17 How to set priorities with stakeholders
25:59 Given an ideal scenario, where do you want to focus your analytics effort?

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In this episode, we are joined by Olga Sazonova, Director of Data Science at NutriSense. Through her work as a data scientist and her own personal experience, Olga offers insight on how having access to one’s personal health data can empower individuals to take charge of their wellbeing.

You will hear about how their data analytics team is structured to work efficiently with their clients’ data, and how data analysts of different backgrounds complement each other. Olga also discusses the importance NutriSense placed on developing meaningful data since being founded in 2019 and how insights from the data work alongside professional insights from subject matter experts.

Evan and Olga wrap up by discussing future possibilities Olga sees for this type of work, especially in accurately predicting health outcomes.

Key Takeaways

What NutriSense does and how they use data analytics to help personal health
The value in building a team with complementary skill sets
How to create text data that enable meaningful insights
How personalized data can be a powerful motivator for lifestyle change

Quotes
Within our company, we're very aligned on the idea that you want to practice a holistic approach to someone's well-being. - Olga

The hope was that if we teach people about their genetic risk and tell them what they can do to mitigate that risk, they will be motivated by that information to do it. - Olga

I think for every individual there are so many other factors that will impact the results for a given meal, so it's wrong to expect the same results every time. - Olga

Featured in this episode
Evan Wimpey
Director of Analytics Strategy at Elder Research.
Company website: www.elderresearch.com
Linkedin: https://www.linkedin.com/in/evan-wimpey-40469b47

Olga Sazonova
Director of Data Science at NutriSense
Linkedin: https://www.linkedin.com/in/olga-sazonova-0328124
Company Website: https://www.nutrisense.io

Chapters
00:00 Intro
01:26 Olga’s background

05:00 What NutriSense does

09:31 Potential challenges faced by breadth of data collection

11:44 Organization of a data analytics team

13:44 How personalized data can lead to change

16:27 What to do when professional advice clashes with the data

19:49 Data results are unique to each person

23:01 One burning question that Olga would like to solve

25:25 Conclusion

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In this episode, we invited Jay Lanterman, Senior Manager, Operations Performance Analytics & Insights at InterContinental Hotels Group. In this podcast, he shares his life background and how he ended up being a data analyst in IHG and gives some points why data analytics is very useful in businesses such as the Hotel and Restaurant industry.

Key Takeaways
Jay’s background and what projects he worked on
How does Data analytics help the hotel and restaurant business industry
Establishing a data analytics team and addressing problems within the H&R Industry
The Challenges and other ways of getting data and conveying them to clients

Quotes
That's what analytics is for, it is to guide the business and help them focus rather than tackling everything all at once. - Jay

if I had a magic wand, it would be great if we all use the same system and we could get the cleanest data and everyone would operate on it and set it perfectly - Jay

Featured in this episode
Evan Wimpey
Director of Analytics Strategy at Elder Research.
Company website: www.elderresearch.com
Linkedin: https://www.linkedin.com/in/evan-wimpey-40469b47

Jay Lanterman
3rd degree connection
Senior Manager, Operations Performance Analytics & Insights at InterContinental Hotels Group
Linkedin: https://www.linkedin.com/in/Jay-lanterman-36641b86
Company Website: https://www.ihgplc.com/en/

Chapters
00:00 Intro
01:16 Jay’s background
03:06 Projects that Jay worked on
06:00 Getting data in each hotels
08:30 Dedicated data architecture team
10:04 Working with each data architecture team
11:58 putting the recommendations into action
15:01 The challenge to convey without technical terms
20:06 Getting data in a physical way
22:38 Predicting new scenarios
25:57 Things our guest to work on in an ideal scenario

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In this episode, we invite Liping Wu, a senior manager and a data science team member from Advance Auto Parts. You will learn the background of her company Advance Auto Parts, the uses of analytics in the daily business functions, the pros and cons of having an enterprise data science team, and lastly, the impact of the pandemic on the company.

Key Takeaways
What is Advance Auto Parts company
Uses of Analytics in the business functions
Advantages and disadvantage of having an enterprise data science team
The impact of the pandemic to the company

Quotes
Folks like us who have similar skill sets find it easier to cross-pollinate the technologies as it evolves because the design is changing and we need to keep learning every day. - Liping

The auto parts industry had record high DIY sales after the dip in March and April In 2020 - Liping
we did find that simplicity is the virtue in these changing times - Liping

Featured in this episode
Evan Wimpey
Director of Analytics Strategy at Elder Research.
Company website: www.elderresearch.com
Linkedin: https://www.linkedin.com/in/evan-wimpey-40469b47

Liping Wu
Senior Manager, Data Science at Advance Auto Parts
Linkedin: https://www.linkedin.com/in/liping-wu
Company Website: https://shop.advanceautoparts.com/

Chapters
00:00 Intro
01:50 Liping's background
03:34 About Advance Auto Parts
06:12 Analytics in business functions
09:33 The team structure
13:27 Pros and cons of enterprise data science team
19:31 The impact of the pandemic
26:42 Final thoughts

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In this podcast, we are joined by Korri Jones, A Senior Lead Machine Learning Engineer and Innovation Coach at Chick-Fil-A. He discusses his background and his career journey that lead him to be a Machine Learning Engineer working at Chick-Fil-A.he also talks about what it is to be a Machine Learning Engineer and what are the usual works and tasks every day. He also shares how he manages his teams such as the Data Science Team and the Data Engineering Team to be more productive and dynamic with each other. He also answered the big question in this show, given an ideal scenario, what would you focus on? Lastly, Korri describes what it looks like working with Chick-Fil-A.

Key Takeaways

What is a machine learning engineer and when they get involved in a project
The value that an innovation coach brings to a team
Where project ideas originate & how they are decided upon
How they operationalize projects at Chick-fil-A
The unique culture of the Chick-fil-A team

Quotes

I want people to do what they are hired to do and really just crush it. I don't need a data scientist doing all of the engineering work. I don't need the engineer doing all the data science work. - Korri

Some people talk about thinking outside the box. We help to make sure that there is no box in the first place. And so you don't have to try to think inside or outside of a box. - Korri

Featured in this Episode

Evan Wimpey
Director of Analytics Strategy at Elder Research
Linkedin: https://www.linkedin.com/in/evan-wimpey-40469b47
Website: www.elderresearch.com

Korri Jones
Senior Lead Machine Learning Engineer
Innovation Coach at Chick-Fil-A
Linkedin: https://www.linkedin.com/in/korri-jones-mba-780ba56

Chapters

00:00 Introduction
01:39 Korri's Background
04:00 Korri's Career as a Senior Lead Machine Learning Engineer
07:01 Balancing the technical side and business side
09:01 What is an innovation coach?
11:36 Getting the ideas from the data science team or others
14:02 When does a machine learning engineer get involved
16:44 How are projects decided upon
19:11 If there was a magic "analytic success" button, where would Korri use it?
22:51 Working With Chick-Fil-A
25:10 Outro

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In this episode, we are joined by Alex Cunningham, Manager of Advanced Analytics at Church & Dwight. Alex and Evan have a thought-provoking discussion about how to drive analytics successes, build momentum, and develop partners. He also discussed their strategy to develop a good product and the team's secret to integrating that process. Alex also touches on what makes the analytics team at Church & Dwight special and the most important traits they look for when growing their team.

Key TakeawaysWhat the analytics center of excellence group of Church and Dwight is.
How to build momentum and develop partners early on.
How to prioritize projects and implement them into production.
What traits they value in their analytics team.
Where Alex would use a "analytic success" button if he had one.

Quote TakeawaysWe went out to folks and invited them to join the circle as partners. Then, once we've established ourselves, it gets better. We don't need to do much to attract people; they're drawn in because they've heard about our successes and achievements and want to be a part of it. - Alex

Empathy and humility. Two closely related traits are what allow us to become those good partners to folks in the business in that internal consulting type capacity. - Alex

Being a lifelong learner is one of the qualities I believe is essential for a successful data scientist. The learning component is something that strong data science groups truly value. - Alex

Speakers of this Episode
Evan Wimpey | Director of Analytics Strategy at Elder Research
www.elderresearch.com
Linkedin: https://www.linkedin.com/in/evan-wimpey-40469b47

Alex Cunningham | Manager, Advanced Analytics at Church & Dwight Co., Inc.
Churchdwight.com
Linkedin: https://www.linkedin.com/in/alex-cunningham-297a8a39

Timestamp00:00 - Intro
01:43 - Alex's background and career history
04:50 - How Church & Dwight structures their analytics capability
06:06 - How the analytics group has integrated in the business & built momentum
11:33 - How they prioritize & implement projects
19:05 - The important aspects of their team
23:43 - If there was a magic "analytic success" button, where would Alex use it?

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In this episode, we are joined by Gerhard Pilcher, the CEO of Elder Research. He is also the author of the book titled Mining your Own Business. In this episode, he talks about his book and the possibility of a second edition. He also discusses the challenges of the new analytics approach, as well as how it compares to previous models. Gerhard underscores the key components needed to drive change and adoption. He also discusses the similarities and differences between successful and failed projects that he has seen in the past. He then ended the conversation by answering the question, “If there was a magic button, where would you use Analytics?”

Key Takeaways-Discovering what the book "Mining Your Own Business" is all about
-The possibility of a second edition
-What are the challenges of the modern model of analytics
-The commonalities of successful and failed projects
-What Gerhard hopes to learn from future episodes of this podcast
-If there was a magic "analytics success" button, where would Gerhard use it?

**Quotes From The Episode

"**Success is when the team is willing to be have their norms challenged." - Gerhard

"Exploring why the analysis is counterintuitive can lead to some of the most significant breakthroughs. However, I believe that people who are unwilling to be questioned may miss out on a tremendous chance." - Gerhard

Books Mentioned in The EpisodeMining Your Own Business

https://www.elderresearch.com/resource/books/mining-your-own-business-how-to-use-analytics-in-your-business/
Amazon ->https://www.amazon.com/Mining-Your-Own-Business-Understanding/dp/0996712100

📌Speakers of this Episode

Evan Wimpey | HostDirector of Analytics Strategy at Elder Research
https://www.linkedin.com/in/evan-wimpey-40469b47

Gerhard Pilcher | Guest
Chief Executive Officer at Elder Research
https://www.linkedin.com/in/gerhard-pilcher-a3583122/

Timestamp[00:00] Intro
[01:24] Gerhard’s Background
[06:32] Gerhard’s Book, Mining Your Own Business
[06:15] Making the 2nd edition of Book and What is the Goal of the Book
[10:47] Challenges for the New Model of Analytics
[18:02] Commonalities on Successful and Failed Projects
[20:03] Topics to Expect in Future Episodes
[24:51] If there was a magic button, where would you use Analytics?
[29:03] Conclusion

Mining Your Own Business Podcast: Recent Episodes

Elder Research