Data Radicals: Recent Episodes
Alation
Some people can see things that nobody else can. They seem to be able to peer around corners and into the future. These seemingly super powers come from being able to synthesize the data all around us. They approach problems with a curious and rational mind. They think differently and encourage others to embrace data culture.
We call them “data radicals” because they transform themselves and the world around them
In this podcast, we talk to these Data Radicals to understand what makes their approach so unique and how it can be replicated.
AI can write code faster than ever. But what if code is no longer the hard part?
In the premiere episode of AI Radicals, host Satyen Sangani is joined by Mark Nelson, Venture Partner at Madrona and former CEO of Tableau, to explore what AI is actually changing—and what remains fundamentally the same about building great software and great businesses.
Having led companies through the rise of databases, cloud computing, SaaS, and self-service analytics, Mark offers a rare perspective on today's AI boom. He explains why judgment and customer understanding are becoming the new competitive advantage, why enterprise buyers are shifting from AI experimentation to demanding measurable ROI, and why today's token-based pricing models may be rewarding the wrong behavior.
"Code is easy to generate. Great software isn't. The bottleneck has shifted to understanding what to build."
Listen to this episode to learn:
- Why generating code is no longer the bottleneck – but building great software still is
- Why enterprise AI is entering an ROI-driven phase where customers expect measurable business value
- Why the next generation of AI companies will win by understanding customers, not just building better models
“ We all come with towering strengths and our own weaknesses. Not just being a product person, not just being an engineer, not just being a salesperson, all of those skill sets. One thing I'll always say about any founder that is true is like, Do you understand your customer? Do you understand what you're solving and why? Do you really kind of first personally feel that pain? Understanding who they're building for and what problem they're solving for.” – Mark Nelson
Time Stamps
*(02:21): Why AI is different from every technology wave before it
*(07:48): AI won't replace judgment—and that's what matters most
*(12:17): What venture investors are really looking for in AI founders
*(20:27): AI makes code cheap—but great software is still hard to build
*(30:18): Enterprise AI moves from experimentation to ROI
*(35:15): Why token-based AI pricing is due for a reckoning
*(45:19): The future of enterprise software and the next AI winners
*(54:06): Satyen’s takeaways
Sponsor
This podcast is presented by Alation.
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Subscribe to the newsletter: https://caspianstudios71626.activehosted.com/f/73
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Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/
Links
Connect with Mark Nelson on LinkedIn
Learn more about Madrona
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AI Radicals is back for Season 4 — and trust in AI has never been more contested.
This season, host Satyen Sangani, CEO and co-founder of Alation, sits down with leaders, builders, and operators working at the edge of AI transformation to ask the question everyone's dancing around: can we actually trust the systems we're building? New conversations dig into data quality, governance, and the feedback loops that separate AI that works from AI that just demos well.
If you care about making AI matter inside your company, your team, or your own career — Season 4 starts July 29, 2026.
Sponsor
This podcast is presented by Alation.
Learn more:
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Subscribe to the newsletter: https://www.alation.com/podcast/
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Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/
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Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/
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The future of business intelligence is being rewritten. Have you ever wondered how AI will unlock the power of unstructured data?
In this episode of Data Radicals, host Satyen Sangani is joined by Chris Aberger, newly-minted VP at Alation to discuss building AI-powered data workflows.
A startup pioneer, Chris explores the importance of metadata in enhancing AI applications within organizations, the significance of quick iterations, and the evolving role of AI engineers.
“ That two-step realization is what's causing a lot of this activity that we're seeing in the market, which is, I know I need to plug into databases. I'm now coming to terms with the fact that this is actually a really tough problem to get right.”
Listen to this episode to learn:
- Why metadata curation and feedback loops are crucial for making AI effective
- The necessity of a fast-paced, iterative approach in developing AI solutions
- How to enable end-users to become builders through AI and metadata tools
Listen now: alation.com/podcast/episodes/ai-builders-metadata-chris-aberger
*Satyen’s narration was created using AI
“People have realized that, okay, like structured data is actually like the hard problem to get right. And all these organizations' really valuable data is inside their databases in the structured formats. We have to figure out how to make this ready for the AI era. And then the kind of second level problem that people are discovering is how do I make this structured data actually work? Oh, it's metadata. And I think that realization that that kind of two-step realization is what's causing a lot of this activity that we're seeing in the market, which is, I know I need to plug into databases. I'm now coming to terms with the fact that this is actually a really tough problem to get right. In order to get it right, I need to effectively go build a data catalog or metadata provider, and therefore we're seeing a lot of activity in this space.” – Chris Aberger
Time Stamps
*(02:04): From the Stanford AI Lab to founding Numbers Station
*(12:10): From chat with your data to act with your data: From data users to business builders
*(19:23): The value of metadata to production-ready AI
*(28:46): What are precision agentic workflows?
*(35:35): Empowering enterprise data users to build with AI
*(45:50): Satyen’s takeaways
Sponsor
This podcast is presented by Alation.
Learn more:
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Subscribe to the newsletter: https://www.alation.com/podcast/
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Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/
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Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/
Links
Connect with Chris on LinkedIn
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In the luxury world where artistry is key, how is AI enabling personalization, optimization, and speed?
In this episode of Data Radicals, host Satyen Sangani is joined by Julie De Moyer, Chief Data Officer of LVMH Beauty to break down the role of data and AI in business transformation.
A seasoned strategist and leader of innovation across 15 beauty brands, Julie shares practical examples of AI application in various aspects of LVMH's operations, from product development to supply chain management.
“ The AI is often the cherry on the cake. We're moving towards those new technologies that are helping us dream even bigger.”
Listen to this episode to learn:
- The importance of collaboration, change management, and consumer-centric approaches.
- How to work closely with CEOs to drive meaningful data-driven decisions.
- How to balance AI and human creativity within the luxury beauty industry.
Listen now: https://www.alation.com/podcast/episodes/lvmh-data-ai-strategy-julie-de-moyer
*Satyen’s narration was created using AI
**LVMH is vendor-neutral and this does not constitute an endorsement
**All views and opinions expressed by the speakers are their own
“ If you look at the making of perfumes or the way we actually make the wines, in other industries, we would use the AI in order to help those, I would say, those scientists to go faster, to optimize their trials. It will never replace the final scent or the final product that is decided on, but it can help with the substitutions of products that might need to go out, as a result of regulatory changes. It might also help with making sure that the quality of the products last as long as possible. We really help those researcher scientists do their job better and easier.” – Julie De Moyer
Time Stamps
*(01:34): Julie’s background: From economics student to technology leader
*(07:55): AI in action: How stakeholders collaborate
*(14:42): The role of data in luxury today (and 5 ways to apply AI in retail)
*(22:09): Leading data in a multi-brand environment
*(28:18): How to become a trusted AI leader: Key tips
*(33:40): Satyen’s takeaways
Sponsor
This podcast is presented by Alation.
Learn more:
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Subscribe to the newsletter: https://www.alation.com/podcast/
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Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/
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Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/
Links
Connect with Julie on LinkedIn
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In this special compilation episode, we're bringing together the most insightful conversations from our latest season exploring the rise of AI. You'll hear from thought leaders like Fortune's AI editor Jeremy Kahn on rebuilding the middle class, marketing executive Michael Olaye on creative acceleration, and Tom Davenport on what real AI transformation looks like. Plus insights from numerous other AI experts including CDOs, academics, and tech entrepreneurs who are shaping our AI future today.
We'll explore practical AI use cases, examine how data leaders can leverage these technologies, and discuss how the CDO role is evolving in response to AI's momentum.
Listen to this episode to learn:
- How AI copilots are fundamentally changing the way we work and uplifting workers across industries.
- Why it's important to have data leaders who can translate between technical possibilities and business realities.
- How solid data infrastructure, clear business objectives, and thoughtful human oversight leads to successful AI transformation.
Whether you're a data professional, business leader, or simply curious about AI's impact, this episode offers perspectives from those on the frontlines of the AI revolution.
“I think this is gonna be a tremendously transformative technology, and I think there's some really big positive effects, particularly I think we are gonna see a huge uplift in labor productivity, and I don't think we're gonna see sort of mass joblessness from this technology. I think actually this is a technology that could enable people to sort of be lifted back up into the middle class.” – Jeremy Kahn
“You can do stuff that would take weeks before in days. You can collaborate with people who have no technical knowledge on technical things. We have tools now that you can code by like verbally speaking natural language. We have tools that you can do design without having any design skills. So, I think it's opened up a whole new site for agencies, consultancies, companies, but it's also opened a whole new site for a new like economy of like content creators. When you build anything with AI, having a human in that loop where we are today, having humans in that loop, checking that also, it's good.” – Michael Olaye
“It's not rocket science. It's having senior people who are interested in analytical, decision making, hiring people who can do the work. The day-to-day work of analytics, both the data management and the data analysis. And ultimately having some sort of unique data that is proprietary to you, that will really differentiate you. Because ultimately data is a fuel of analytics and AI. And if you don't have something distinctive, you're gonna have the same models that everybody else has.” – Tom Davenport
Timestamps
*(01:18): AI as copilot
*(04:30): AI: It’s still early days
*(06:13): AI Use Cases
*(11:26): How data leaders can leverage AI
*(14:07): Data leadership and systems thinking
*(18:17): The future (and limits) of the CDO
*(29:15): Predictions
*(35:16): Career advice
Sponsor
This podcast is presented by Alation.
Learn more:
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Subscribe to the newsletter: https://www.alation.com/podcast/
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Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/
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Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/
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David’s LinkedIn Profile: https://www.linkedin.com/in/davidwchao/
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What does it take to turn AI hype into operational value at enterprise scale?
In this episode of Data Radicals, host Satyen Sangani sits down with Todd James, former Chief Data and Technology Officer at 84.51° and Kroger executive, to unpack the realities of leading AI transformation inside one of America’s largest grocery retailers.
Drawing on a career that spans the Coast Guard, consulting, and Fortune 100 leadership, Todd shares how scaling AI isn’t just about building advanced algorithms—it’s about solving real problems, embedding with the business, and building reusable infrastructure that lasts.
“If the people at the consumption end of your sciences aren’t bought in, they don’t get implemented.”
Listen to this episode to learn:
- How effective leaders “delegate to innovate” and create space for team growth
- How AI reduced Kroger’s pick-order travel time by 10% and truck route distance by 8.6%
- Why the CDO role is likely transitional—and what’s next for data leadership
- What it takes to embed data science into the operational core of a business
This conversation is a must-listen for anyone building AI programs, leading data teams, or navigating digital transformation.
Listen now: https://www.alation.com/podcast/episodes/delegate-innovate-todd-james
*Satyen’s narration was created using AI
“ I think we should go into every project, every initiative, saying, ‘I own an outcome around bringing people along and convincing them.’ If you're doing that right, you're probably spending more than half of the project or half the initiative on managing those organizational dynamics. Working with people and how they think and how they feel to be able to drive them to an outcome to listen. I think that's more than half the work that needs to happen in the space. We talk about data and analytics and how hard the math is and how cool the outcomes are, but this is transformation. This is about people.” – Todd James
Time Stamps
*(01:46): Todd’s career journey: From the Coast Guard to Fidelity
*(10:03): Great leaders delegate: Advice for aspiring executives
*(12:53): AI retail use cases: Quicker routes and personalization at scale
*(26:12): Reapplying data-science processes across distinct problems
*(40:21): Is the CDO role transitional?
*(48:27): Satyen’s takeaways
Sponsor
This podcast is presented by Alation.
Learn more:
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Subscribe to the newsletter: https://www.alation.com/podcast/
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Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/
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Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/
Links
Connect with Todd on LinkedIn
Learn more about Aurora Insights
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What if the future of data management isn’t just about governance—but about growth, speed, and strategic advantage? From reshaping data operations to unlocking new levels of productivity, data products and AI agents are redefining what’s possible in the world of data.
In this episode of Data Radicals, Satyen Sangani sits down with Sanjeev Mohan, Principal at SanjMo, to discuss the definition, impact, and lifecycle of data products. They also examine how AI agents are revolutionizing job functions and industries, and practical applications for harnessing this technology’s full potential.
Listen to this episode to learn:
- What data products are and their importance in delivering measurable value, building trust, and improving user experience.
- The challenges in adopting data products, including the need for a cultural shift within organizations and the potential resistance to change.
- How generative AI and autonomous agents can revolutionize data management, business processes, and job functions.
Discover how forward-thinking data leaders are using these tools not just to manage data—but to build trust, accelerate outcomes, and drive measurable business value.
*Satyen’s narration was created using AI
“ The systems that are running really well, in a lot of organizations, why would they rip out and then go down the path of data products? Because, the problem with data products is it's also a cultural issue. It's a mindshift and you have to think from a completely different long-term point of view. We are so used to – in IT – somebody gives me a problem, I'm like, ‘Yes, I got it. I'll solve it for you.’ Then you move on to the next problem. With data products, it's a mindset shift.” – Sanjeev Mohan
Time Stamps
*(02:28): What is a data product?
*(12:30): Why data products demand a mindset shift in IT
*(27:19): What is an AI agent?
*(36:39): How will data management evolve with the advent of AI?
*(47:56): Satyen’s takeaways
Sponsor
This podcast is presented by Alation.
Learn more:
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Subscribe to the newsletter: https://www.alation.com/podcast/
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Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/
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Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/
Links
Connect with Sanjeev on LinkedIn
Learn more about SanjMo
Download Sanjeev’s book Data Products for Dummies
Subscribe to Sanjeev’s podcast It Depends
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Generative AI is reshaping the way go-to-market teams create content, optimize workflows, and drive velocity — but only when it’s powered by the right context and data.
In this episode of Data Radicals, Alation CMO David Chao sits down with Paul Yacoubian, CEO and co-founder of Copy.ai, to explore how large language models (LLMs) are transforming content creation and sales execution at scale. Paul shares lessons from building Copy.ai since 2020 and how his team is helping over 15 million users streamline operations through AI-powered automation.
You’ll learn why content without context falls flat, how siloed systems slow down GTM execution, and why AI isn’t replacing roles — it’s augmenting them to unlock new levels of efficiency and insight.
Listen to this episode to learn:
- How LLMs enable end-to-end automation by taking in data, executing workflows, and generating outputs — without manual bottlenecks.
- Why unifying siloed systems is critical to improving GTM velocity, content relevancy, and business decision-making.
- How AI is transforming — not replacing — roles like SDRs and marketers, and what that means for the future of sales teams.
- What CEOs and business leaders must do to operationalize AI successfully: from standardizing best practices to enabling faster, data-driven decisions.
If you're navigating the challenges of scaling AI in your GTM org — from data sprawl to inefficient workflows — this episode offers practical strategies, fresh perspectives, and a blueprint for AI-powered transformation.
“ The most important problem to solve is how close can you get to customers? How close can you get everyone at the company close to the market and close to customers in every interaction? That's never been possible before. Content is one way that we take action. The other way we take action is how do we deliver the content? If we know who we are trying to reach out to now, we can predict and understand what content is going to be hyper-relevant to that person. Once you have that production process for content, now you can go create the content and distribute it right through your SDR, right to that account.” – Paul Yacoubian
Time Stamps
*(02:18): Why high-quality content needs context
*(09:11): Best practices for leveraging LLM tools
*(11:42): The path to insight: from data silos to shared context – and better content
*(31:00): Which roles will be replaced or augmented by AI?
*(38:29): How should CEOs approach AI?
*(45:45): David’s takeaways
Sponsor
This podcast is presented by Alation.
Learn more:
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Subscribe to the newsletter: https://www.alation.com/podcast/
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Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/
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David’s LinkedIn Profile: https://www.linkedin.com/in/davidwchao/
Links
Connect with Paul on LinkedIn
Learn more about Copy.ai
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As technology rapidly evolves and businesses focus on getting real results, data jobs are shifting. Many data tasks now fall under the CIO or CTO, data leaders are moving into roles that affect bigger business plans, and more companies are using self-service data tools or seeking a path to AI—making CDO-led teams less necessary. How can data leaders adapt?
In this episode of Data Radicals, Satyen Sangani talks with Ryan den Rooijen, Writer and Consultant at Qstar.ai, and Wade Munsie, Interim Director of Data & AI at Heathrow. With years of experience in data leadership, Ryan and Wade explore how the CDO role is changing, the challenges in data and AI, and why the job isn’t always what people expect.
Listen to this episode to learn:
- The future of data leadership, including how AI is changing the way we use data and why it's important to stay flexible and focused on real business results.
- Why data leaders need to go beyond their usual tasks and help improve the whole business.
- How AI and smart computer systems are shaping data management and what these new technologies could mean for the future of the industry.
From capitalizing on generative AI to redefining the CDO role, this episode offers a wealth of knowledge for anyone looking to understand the real-world challenges and opportunities in the data landscape. Tune in to hear practical advice and visionary thoughts from top data leaders.
*Satyen’s narration was created using AI
“ For many of these organizations, there really is an onus on people like ourselves to prove ourselves in the organization. I think the biggest data culture challenge is really how do we make ourselves relevant to the day-to-day of the employee? How do we make sure that if somebody is on an oil rig or in a store or in a call center or on a trading floor or in a lab, they are going to do something different because of us? Because if they're not doing something different because of us, then honestly, we don't deserve to be here.” – Ryan den Rooijen
“ Traditionally, CDOs were in place to wrangle and collate the data and curate the data to a point that it was perfect. That was the ideal for a long time. I think that's probably an impossible task these days with all the different types of unstructured data around there. But also, is it needed? If we keep pushing for that nth degree, you are never going to achieve it. If you keep pushing for that from a quality point of view and a curation point of view, you forget about why you were there in the first place, which is value. If you don't get to that value point quick enough, it's very hard to explain why you were owed that budget in the first place, where all that cost went.” – Wade Munsie
Time Stamps
*(02:23): Why is the chief data officer in trouble?
*(12:00): The CDO as a transformational role
*(23:52): Redefining data culture as value-driven
*(32:50): Data leaders as systems thinkers
*(45:09): How will AI impact data teams?
*(55:48): Satyen’s Takeaways
Sponsor
This podcast is presented by Alation.
Learn more:
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Subscribe to the newsletter: https://www.alation.com/podcast/
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Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/
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Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/
Links
Connect with Ryan on LinkedIn
Connect with Wade on LinkedIn
Read Ryan and Wade’s MIT Sloan article The Chief Data Officer Role: What’s Next
Read Ryan and Wade’s series Chief Data Officers Are In Trouble
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Cloud costs are skyrocketing, and for data teams running AI inference, Spark jobs, and big data workloads, optimization is no easy task. Tuning these workloads for efficiency without disrupting production is a major challenge—but what if there was a better way?
In this episode of Data Radicals, Satyen Sangani sits down with Jeff Chou, CEO and co-founder of Sync Computing, to explore a revolutionary approach to cloud optimization. Sync’s closed-loop tuning engine continuously fine-tunes workloads in real-time—without manual adjustments. The result? 50-60% cost savings on Spark jobs and massive efficiency gains for AI workloads.
Listen to his episode to learn:
- Why declarative computing is the future—letting engineers define their desired outcomes instead of manually configuring infrastructure.
- How Sync Computing slashes cloud costs by dynamically adjusting resources in production, ensuring efficiency without sacrificing reliability.
- The game-changing impact of Sync’s partnership with NVIDIA to optimize GPU workloads, where the stakes—and costs—are even higher.
If you’re managing cloud workloads, this conversation is a must-listen. Discover how cutting-edge AI-powered optimization is reshaping efficiency for Databricks, AI inference, Spark, and beyond.
*Satyen is an investor of Sync Computing
*Satyen’s narration was created using AI
“ We have this high-level thesis we call Declarative Computing. Which the idea is, let's flip the story, instead of a human having to pick the resources and pick all these configurations. That's really hard. Most people don't know any of that stuff, but what people do understand is the outcome. How long did it take? How much did it cost? What was the latency? These are very understandable. These metrics are tied much more to the business, I would say. Our whole thesis is why can't we flip the story? Why can't you declare the outcomes that you want?” – Jeff Chou
Time Stamps
*(04:10): What is analog computing? How can it minimize energy consumption?
*(13:06): What is declarative computing? Scaling compute, minimizing costs
*(21:32): Optimizing cloud compute (and the challenges of vendor lock-in)
*(31:23): The need for high reliability in production
*(39:14): The future of compute: Specialization
*(43:00): Satyen’s takeaways
Sponsor
This podcast is presented by Alation.
Learn more:
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Subscribe to the newsletter: https://www.alation.com/podcast/
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Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/
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Satyen’s LinkedIn Profile:
https://www.linkedin.com/in/ssangani/
Links
Connect with Jeff on LinkedIn
Learn more about Sync Computing
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AI is here—but are businesses truly ready to harness its full potential? In this episode of Data Radicals, host Satyen Sangani sits down with Tom Davenport to explore what it takes for AI to create real business value.
As one of the most respected voices in AI and analytics, Tom brings decades of expertise to the table. From agentic AI to AI-driven leadership, this conversation covers the pressing challenges—and opportunities—that will shape the next era of business transformation.
What You'll Learn in This Episode:🔹 Agentic AI is still in its infancy – AI agents can act autonomously, but most businesses are only using them for simple, low-stakes tasks. Scaling their use requires overcoming reliability challenges.
🔹 AI alone won’t drive value—process redesign is critical – Businesses can’t just add AI to existing workflows and expect results. As Tom puts it, "Economic value requires that we change the way we do our work. And there has to be some intentional design activity. It can't just evolve."
🔹 The C-suite is overcrowded—AI leadership must evolve – With CIOs, CDOs, CTOs, and more, organizations often suffer from fragmented leadership. Tom argues for business-driven executives who can oversee AI, data, and digital strategy holistically.
AI is transforming industries, but the organizations that truly succeed will be those that rethink their leadership, workflows, and data strategies. Whether you're a CDO, CIO, or data professional, this episode offers actionable insights from one of the most influential thinkers in AI and analytics.
*Satyen’s narration was created using AI
“ There is a generative AI component, but it's not just generative AI. It's probably analytical AI as well, it's probably still APIs, it's probably still transaction systems, ERP and CRM and so on. There'll have to be a lot of integration, which means that it's going to be a fair amount of work for companies to pull this off. I think vendors will help and they'll provide lots of tools, but I think companies will have to figure out what they want to accomplish with it and make it happen and that will take some time and effort.” – Tom Davenport
Time Stamps
*(02:27): Agentic AI use cases: Is AI the new software?
*(10:52): The CDO's role in the age of AI
*(20:57): What is AGI? Is it coming soon?
*(26:43): How can organizations transform with data?
*(35:38): The need to redesign processes for AI
*(38:58): Satyen’s takeaways
Sponsor
This podcast is presented by Alation.
Learn more:
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Subscribe to the newsletter: https://www.alation.com/podcast/
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Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/
-
Satyen’s LinkedIn Profile:
https://www.linkedin.com/in/ssangani/
Links
Connect with Tom on LinkedIn
Read Tom’s MIT Sloan article Five Trends in AI and Data Science for 2025
Read Tom’s Harvard Business Review article How Gen AI and Analytical AI Differ — and When to Use Each
Order Tom’s book All Hands on Tech: The AI-Powered Citizen Revolution
Order Tom’s book All in on AI: How Smart Companies Win Big with Artificial Intelligence
Order Tom’s book Competing on Analytics: The New Science of Winning
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As AI becomes integral to every aspect of business, ensuring its accessibility for everyone—not just specialists—is essential. Companies like Humanloop are leading the charge with innovative platforms that empower non-technical users to harness the power of advanced language models through intuitive tools and frameworks.
Democratizing AI access paves the way for transformative business outcomes and a future of collaborative AI systems. However, building a strong AI strategy starts with leveraging powerful models and mastering prompt engineering before considering fine-tuning. Engaging subject matter experts and using robust evaluation and collaboration tools are equally critical to the success of modern AI projects. In this episode, Satyen and Raza examine the evolution of AI models, the practical challenges of model evaluation and prompt engineering, and the role of multidisciplinary teams in AI development.
*Satyen’s narration was created using AI
“ In our experience, fine-tuning is very useful as an optimization step. But, it's not where we recommend people to start. When people are trying to customize these models, we encourage them as much as possible to push the limits of prompt engineering with the most powerful model they can before they consider fine-tuning. The reason that we suggest that is that it's much faster to change a prompt and see what the impact is. It's often sufficient to customize the models and it's less destructive. If you fine-tune a model and you want to update it later, you kind of have to start from scratch. You have to go back to the base model with your label data set and re fine-tune from the beginning. If you're customizing the model via prompts and you want to make a change, you just go change the text and you can see the difference. There's a much faster iteration cycle and you can get most of the benefit.” – Raza Habib
Time Stamps
*(01:26): Raza’s career journey: From academia to industry
*(12:46): What is active learning?
*(17:20): How LLMs diverge from traditional software processes
*(24:53): What is data leakage?
*(35:56): How can software engineers adapt in the age of AI?
*(47:04): Satyen’s takeaways
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There’s a digital revolution happening – and it’s poised to impact data leaders across all industries. During this time of never-ending change, it’s crucial to have data practitioners at the center of holistic AI transformation as regulatory compliance and ethical standards come into the fold.
Businesses of every size will encounter these complex regulations. Learn about these challenges and how connecting practitioners across fields can create more compliant and trusted AI environments.
This episode is packed with practical guidelines and future-focused strategies designed to empower data leaders with the insights they need to build effective, ethical AI.
*Satyen’s narration was created using AI
“ Each state or each country having their own AI policies or privacy policies, frankly, doesn't make any sense. Because, most people, especially if you're on cloud, you may not even know where your data sits. There's basic principles and there's basic practices that you can define that are tech agnostic, that you can still have your own tech stack and your own tools. There's lots of solutions and players that work in those components, but you can give basic guidelines to say, here's the steps and the processes and the pieces that you need to put in place. Here's how they form together to create an encapsulation of trust.” – Wendy Turner-Williams
Time Stamps
*(02:20): Enabling the AI community
*(13:49): How does The Association.AI put regulatory theory into practice?
*(21:01): Why AI practitioners need places to knowledge share
*(34:23): The rise of the CIO: Risk talks
*(39:46): AI predictions: What will change? What won’t?
*(50:30): Satyen’s takeaways
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Any digital marketing leader will tell you that data and marketing strategies go hand-in-hand. In this episode, Michael Olaye, EVP and Managing Director of Hero Digital, shares his journey and practical strategies for success, drawing from his career path that began with door-to-door job hunting and led to spearheading major digital initiatives.
Michael emphasizes the central role of data in digital marketing, from informing internal business decisions to enhancing customer experiences, and discusses the dual focus of AI in driving internal efficiency while offering robust public-facing tools.
He highlights the critical interplay between data governance and AI ethics, stressing the importance of businesses being 'AI ready.' By exploring customer journeys and leveraging data for innovation, Michael demonstrates how insights can shape product development and business strategies.
As a forward-thinker, he shares his enthusiasm for emerging technologies like learning agents and multimodal models, envisioning a transformative future for business operations. Through candid anecdotes and expert advice, Michael delivers actionable insights on harnessing data and AI to drive innovation and customer satisfaction.
“Some clients do not know that they're sitting on gold, they do not know that. They have tons of data that they've never done anything with and then they focus on the most simplistic things: media, SEO, social media content, website content. Then you come in and you're like, ‘Hey, we can help your customer service be more efficient by understanding how the data, how long it takes a call to go through. We can help you process products more better by understanding the transaction from seeing something online to going in store, to buying it, to returning it.’ Looking at those data sets and seeing patterns or bringing them together to see journeys, that's where the secret lies.” – Michael Olaye
Time Stamps
*(03:37): How Michael uses data for customer experience
*(11:39): AI in marketing today: The role of data
*(20:54): The dangers of bad data in AI
*(23:30): How do you find high-value data?
*(30:17): Understanding data and the brand-loyalty debate
*(38:16): David’s takeaways
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If you’re a history buff in the data world, you know that there’s a complex interplay between data, statecraft, and machine learning. The history of data visualization is entwined with societal governance and technological advancements, starting from the usage of statistics for statecraft in the 18th century to the transformative innovations during World War II that birthed computation and data science as we know it. And because of the subjective design choices that underpin data gathering and analysis, there’s an inherently political nature of deciding what data to collect and how to utilize it, which is critical in understanding both historical and contemporary data practices.
As we move into the modern applications of data science and the advent of AI technologies, deep reinforcement learning and the integration with generative AI models, these technologies are reshaping the field by enabling computers to process and interact with unstructured data in unprecedented ways. Satyen and Chris discuss his book How Data Happened, the origins of data science and the role of Alan Turing in the creation of digital computing, and the challenges generative AI brings around model interoperability.
*Satyen’s narration was created using AI
“In the last two years, one of the major techniques for advancing the most eye-popping products has been RLHF, Reinforcement Learning from Human Feedback. There's innumerable subjective design choices happening there, which eventually become encoded in a product. But, the presentation of it as though it's somehow unbiased and free from any subjective design choices is illusory.” – Chris Wiggins
Time Stamps
(01:36): How did Chris come to write How Data Happened*?
*(10:33): World War II as the springboard for data science and digital computing
*(18:37): The tension between objectivity and subjectivity in data today
*(25:36): What is Reinforcement Learning from Human Feedback (RLHF)?
*(36:03): How has Gen AI impacted data science?
*(44:53): Satyen’s takeaways
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What does it take for data leaders to deliver real business value? In this episode, Taylor Culver, founder of XenoDATA, shares practical strategies for success, including:
Focus on the right problems: Taylor explains the importance of refining problem statements for actionable, data-driven solutions.
Engage like a salesperson: Actively listening to stakeholders and identifying pain points is key to building impactful use cases.
Adopt a product management mindset: Taylor emphasizes weaving governance and architecture into customer-centric data strategies.
While the path of the data leader is fraught with obstacles, success is possible. Taylor offers time-tested strategies to help data and business leaders alike make a measurable impact.
“What data leaders should just own is the path to me is probably going to be fraught with failure, but I need to be able to pivot and I need to be agile. I can very much serve myself by adhering to a common set of principles, which I'm going to practice consistently and continually adapt and adjust in the way I engage with my stakeholders and identify their problems and lean in or lean out on data management techniques or delivering certain solutions. It comes down to intent. Do you genuinely want to help people in your business solve problems with data? Do you genuinely want to grow? Do you genuinely recognize that there's not a magic bullet to doing this? Those are the data leaders who will be successful despite facing adversity.” – Taylor Culver
Time Stamps
*(07:01): The data-business people problem
*(17:50): How data leaders can tackle business problems in 3 steps
*(26:22): Is data a strategic function or an enablement function?
*(33:50): Strategy: Data offense vs. data defense
*(40:45): Data is a people business: the value of trust
*(46:20): Our takeaways
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About 15 years ago, organizations knew they needed data governance but faced a branding problem. People hated the term. Stewart Bond coined “data intelligence” to describe intelligence about data and shift the governance conversation – and a category was born. Today, data intelligence represents a $9B+ market.
This concept has given rise to the "data intelligence stack," which includes data cataloging, data quality management, and data product hubs, all of which play vital roles in AI model development.
Looking ahead, big changes are coming. IDC predicts that by 2028, the Chief Data Officer’s role will rival the CIO’s in shaping technology investments. In this episode, Satyen and Stewart dive into the components of data intelligence, the growing importance of data products, and key insights from IDC's recent MarketScape evaluation.
“We talk about the modern data environment as being highly distributed. Data is all over the place. It's very diverse. There's so many different kinds of data that we're dealing with today. It's also very dynamic. That data is always moving and it's always changing. I think data intelligence as a category, as a capability, there's always going to be that need to have the intelligence about the data that the organization manages in the modern data environment available. That is visible across all the different places the data lives in that modern data environment.” – Stewart Bond
Time Stamps
*(04:54): What is data intelligence?
*(09:49): The rise of the data marketplace
*(13:01): How will AI impact the data intelligence market?
*(25:54): Is the Chief Data Officer role in trouble? Or is it growing in prominence?
*(38:40): What is the IDC MarketScape?
*(41:14): Satyen’s takeaways
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The transformative potential of AI is going to affect all of us, regardless of what industry you’re in. While AI has the capability to democratize high-demand professions through specialized copilots, it also presents potential positive and negative societal impacts, including misinformation and political discourse manipulation. Today, we’re taking an in-depth look at the evolution of AI copilots tailored for specific professional fields and the need for critical thinking and transparent AI systems to ensure ethical deployment and improved outcomes in sectors like healthcare and finance.
There’s a growing need for federal guidelines to prevent fragmented AI governance (think the EU AI Act). However, differing approaches to regulations across regions can lead to unbalanced directives. Politics are also influencing this new AI landscape. From potential deregulatory pushes under a Trump administration to sustained regulatory efforts under a Harris-led government, AI regulation will look different depending on who wins the US election. And it’s not just politics, total war is a significant worry when it comes to the use of AI. From military strategies to disrupting democracy, AI has the power to impact innovation, ethics, politics, and society. Satyen sits down with Jeremy to discuss his book Mastering AI, the importance of AI regulations, and the impact of AI on the job market.
“In the US, we have this issue where the states are starting to take action because of the lack of action by the federal government and I think that's problematic. I don't think you want a system where you have every state with its own AI act and different laws to comply with in every state. I do think we need to have some action at the federal level. When we're going to see that happen, I don't know, because there has been a lot of lack of will. Even though there was some bipartisan efforts in Congress that looked like they were maybe going to pay off last year. I think there's some agreement on both sides of the aisle that there should be some rules and regulation passed around AI.” – Jeremy Kahn
Time Stamps
*(02:37): The future of AI: A boon for the middle class, a threat to democracy
*(12:18): The case for federal AI regulation in the US
*(20:11): The impact of the US election on AI regulations
*(30:29): What is the Pigouvian or robot tax?
*(36:39): What is total war? How will AI play a role?
*(46:35): Takeaways
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Legacy systems in the financial services industry are notorious for being slower to adapt to big data. But for good reason. Because banks are complicated and intersect across networks, it’s difficult to implement new processes without disturbing the rest of the system. Yet, GXS Bank is proving it doesn’t have to be this way. The digital bank is leveraging data to drive financial inclusion and innovation. Their strategic use of data from parent companies and partnerships, helps create financial products for underserved communities in Singapore.
Not only is GXS Bank driving inclusion, but they’re also exploring the impact of generative AI on customer service, fraud detection, and operational efficiency. The technology is transforming the financial sector by offering valuable insights for improving data quality, governance, and stakeholder engagement. Satyen and Geraldine discuss the importance of creating better products and credit risk profiles, the intricacies of data sharing agreements and operational challenges, and strategies for leveraging AI to enhance customer service and fraud detection.
“If you think about AI being part of a product manager, product creation, so you go to different segments of your consumers, see what their pain points are, do the summarization, and then say, ‘Hey, AI, can you create a new product that would match the needs of 50% of my segments in the consumer business?’ The AI quickly generates some AI product, a banking product for you with such features and you iterate and iterate and iterate. These are some of the tools that a product manager could really leverage on to create a new product from scratch.” – Gerladine Wong
Time Stamps
*(01:24): About GXS Bank
*(10:04): Gen AI at GXS
*(15:35): Supporting financial inclusion with alternative data sources
*(31:34): Playing data offense vs defense at GXS
*(40:51): Takeaways
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The Data Radicals podcast returns on October 23rd!
This season, we're exploring how trusted data drives value, whether that’s building game-changing AI or transforming data into dollars – and business results. You’ll hear from data leaders, journalists, and AI trailblazers like Dr. Geraldine Wong, CDO of GXS Bank, New York Times Chief Data Scientist, Chris Wiggins, and Jeremy Kahn, AI Editor at Fortune Magazine. And so many more! Get ready to discover groundbreaking strategies from some of the most innovative minds in data today.
Welcome to season three of Data Radicals! Powered by the team here at Alation.
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Saul Alinsky's book Rules for Radicals held significant lessons for grassroots movements and political activism when it was published in 1971. It also inspired the title and theme of this podcast. In his book, Saul outlines approaches for unifying people and motivating them to work toward a common goal. Today, these same strategies can be used for data culture change management, triggering transformative action within organizations.
Saul was an American activist and political theorist who lived from 1909 to 1972. His work organized impoverished communities and gave them tools to drive social change, and won him national attention and notability. In the final episode of the season, Satyen sits down with a ChatGPT-infused version of Saul to discuss the role of data in driving social change, the application of community organization tactics in corporate settings, and the key principles from Rules for Radicals.
“On the global stage, data analytics and humanitarian efforts is akin to having a crystal ball. It's not about predicting the future, but about making informed, timely decisions that can save lives. The possibilities are indeed limitless. With data and analytics, we're not just changing the game. We're rewriting the rules and designing a better playbook for humanity.” – Saul GP Talinsky
Episode Timestamps:
*(03:57): Bridging community organizing with data science
*(12:46): Empowering teams: Upskilling organizational agility
*(14:35): Democratizing data and crafting a culture of insight and empowerment
*(17:17): The timeless role of data in driving social change
*(20:54): How to unlock societal transformation with data and analytics
*(26:26): The evolution of radical change
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The rapid progress in AI technology has fueled the evolution of new tools and platforms. One such tool is a vector search. If the function of AI is to reason and think, the key to achieving this is not just in processing data, but also in understanding the relationships among data. Vector databases provide AI systems with the ability to explore these relationships, draw similarities, and make logical conclusions. Understanding and harnessing the power of vector databases will have a transformative impact on the future of AI.
Edo Liberty is optimistic about the future where knowledge can be accessed at any time. Edo is the CEO and Founder of Pinecone, the managed database for large-scale vector search. Previously, he was a Director of Research at AWS and Head of Amazon AI Labs, where he built groundbreaking machine learning algorithms, systems, and services. He also served as Yahoo's Senior Research Director and led the research lab building horizontal ML platforms and improving applications. Satyen and Edo give a crash course on vector databases: what they are, who needs them, how they will evolve, and what role AI plays.
“We as a community need to learn how to reason and think. We need to teach our machines how to reason and think and talk and read. This is the intelligence and we need to teach them how to know and remember and recall relevant stuff. Which is the capacity of knowing and remembering. The question is, what does it mean to know something? To know something is to be able to digest it, somehow to make the connections. When I ask you something about it, to figure out, ‘Oh, what's relevant? And I know how to bring the right information to bear so that I can reason about it.’ This ping pong between reasoning and retrieving the right knowledge is what we need to get good at.” – Edo Liberty
Time Stamps
*(03:13): How vector databases revolutionize AI
*(14:13): Transforming the digital landscape with semantic search and LLM integration
*(28:10): Exploring AI’s black box: The challenge of understanding complex systems
*(37:02): Striking a balance between AI innovation and thoughtful regulation
*(40:01): Satyen’s Takeaways
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Folks in the data space are familiar with the concept of data literacy. However, a new idea is on the rise: business literacy. Whether folks sit in product, marketing, or commercial, there needs to be a productive balance between understanding business context and technical expertise of each department. This shared comprehension means ideas are more likely to be deployed and productionalized because everyone has deeper domain knowledge and business understanding.
Sanjeevan Bala is making business literacy a top priority at his organization. He is the Group Chief Data and AI Officer at ITV, an Alation customer. There, he is responsible for driving the digital data and AI transformation and leading an offensive growth strategy that enhances how they produce, promote, distribute, and monetize content. Sanjeevan is an international thought leader, has won numerous awards for his work, and was named the most influential person in data by DataIQ. Satyen and Sanjeevan discuss the idea of a Data Product Manager, the importance of business literacy, and the power of experimentation.
“I think because we went down the data as a product notion, that leadership role was a Data Product Manager. Incorporate product thinking in the way in which data is developed, designed, and used. I think what's beautiful about product thinking is it's very well adapted and equipped for understanding competing objectives and competing needs. Creating methods by which you're trying to either align or prioritize those needs. But, critically allows you to prioritize around the right things because you're constantly looking at how do you make sure you can productionize and scale and realize the full value? What does it take to do that? That goes way beyond what you're doing in data. That gets into organizational change, that gets into last mile technologies that you may not have thought about.” – Sanjeevan Bala
Time Stamps
*(05:16): How to define your organizational identity
*(07:26): The art of storytelling and data-driven leadership
*(18:20): Harnessing experimentation to drive organizational change
*(25:10): Data literacy versus business literacy
*(42:17): Balancing innovation and regulation in AI
*(44:41): Satyen’s Takeaways
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Whether you work in retail, healthcare, or CPG, data analytics is key to making your business stand out. You’re able to find new sources of data, synthesize them, and then work with business folks to get better and better insights. Even with all of the advantages analytics offers us, sometimes there’s hesitancy to invest in data. In sports, it’s the exact opposite. The use of data is felt immediately in game wins, player selection, and gate revenue.
Known as “The Real Moneyball guy,” Ari Kaplan has revolutionized sports through analytics and is a leading influencer in the area, as well as in AI and data. He helped create analytics departments for the Chicago Cubs, Los Angeles Dodgers, and Baltimore Orioles. Ari is now Head of Evangelism at Databricks where his team helped the Texas Rangers clinch their first World Series title. Satyen and Ari discuss data analytics in sports, how data intelligence platforms are shifting the landscape, and the concept of generation AI.
“Even if we change nothing else, to be able to make better predictions of player development, finding what skills are better in the draft, predicting injuries and so on, that's part of the competitive advantages. How can we ingest this data? It's a ton of data. Terabytes of data every game, multiply that by dozens and dozens of teams at all levels around the world. Right now, teams are struggling to store it, process it on a daily basis. Teams that could do that faster will be an advantage. For listeners, if you're not in the baseball world, same idea. If you're in retail, CPG, healthcare, it's finding new sources of data, proprietary, nonproprietary. How could you synthesize it? Then, how can you start working with the business people to get better and better and better insights?” – Ari Kaplan
Time Stamps:
(03:00): The birth of Moneyball*
*(10:11): How the Texas Rangers hit a data home run
*(15:49): The next evolution: data intelligence
*(27:17): Partnering for success in the ecosystem
*(38:54): The role of AI in building the future
*(41:29): Satyen’s Takeaways
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When it comes to our relationship with technology, be like philosopher Friedrich Nietzsche and practice mindfulness. We usually think mindfulness means setting boundaries like screen time limits. However, we should think about the goals and values we want from technology, like greater human connection, improving efficiency, or driving knowledge. This introspective thinking enables us to be intentional about how and why we’re using technology. Without mindfulness, instead of you driving the tech, the tech may be driving you.
Nate Anderson lives by and continues to share Nietzsche’s philosophies today. Nate is the Deputy Editor at Ars Technica, where he covers technology law, politics, and culture. He combined his high-tech background with a love of writing to freelance at publications like The Economist and Foreign Policy. Nate is also the author of In Emergency, Break Glass: What Nietzsche Can Teach Us About Joyful Living in a Tech-Saturated World. Satyen and Nate discuss forming positive connections with technology, saying “yes” to life, and what Nietzsche would have to say about tech.
“Connection to other people is important. We use technology to create that connection. That might mean a Friday night game group over Zoom or Twitch or multiplayer with your friends. As long as you have the goal in mind, that's where it requires your creativity. That's where you're using the tools creatively to produce outcomes that you want in life. The problem with not thinking in a goal-directed way is that technology itself is not completely neutral. Technology has no goals of its own. It was created by people and companies who have plenty of goals and some of those don't necessarily take you to places where you would choose to go. That's why if you don't have a goal-driven approach to technology, you may find technology is actually driving you.” – Nate Anderson
Time Stamps:
*(04:25): Why Nietzsche? Why now?
*(15:07): Offer agency, not just prescriptive rules
*(24:17): The loneliness of technology
*(27:51): Seeking that goal-driven place
*(35:44): Producing actual value
*(38:35): Satyen’s Takeaways
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Thanks to GenAI, we have an overabundance of tools, models, and capabilities. However, the use and impact of these advancements is yet to be known. That’s why in the age of technological innovation, traditional skills like fact-checking are more important than ever to ensure that the technology and predictions are correct.
Guy Scriven, U.S. Technology Editor at The Economist, is on the frontlines of the AI explosion. In his tenure at the publication, he has served as a researcher and climate risk correspondent, and has grown his affinity for telling data-driven stories. Satyen and Guy discuss the role of data in journalism, instilling a culture of debate, and the unsexy – but critical – side of AI.
“We've had this long period of experimentation and excitement. That's been basically marked by the supply side of AI just really ramping up. You've had loads of model makers releasing new models. You've had the cloud players buying enormous amounts of specialized AI chips. You've had thousands of AI application startups who are going to build on top of the model makers, who then use the AI chips from the cloud providers. You've had this boom in the supply side of AI. Now, the big question is whether the enterprise demand meets that and what shape it takes. I think we don't really have a good sense of that until at least the first couple of quarters of next year.” – Guy Scriven
Time Stamps:
*(02:22): Less reporting, more commentary
*(13:32): Dataset discovery
*(22:34): ChatGPT’s hallucination problem
*(34:38): AI headlines on the rise
*(41:48): What’s the next big AI story?
*(46:10): Satyen’s Takeaways
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The best kind of data radical is one who knows how to balance their technical expertise with their fuzzy side. Skills like storytelling, empathy, and ethics are becoming invaluable in the tech space. The ability to balance both enables data folks to recognize patterns where others might miss them. This type of integrative thinking can guide people on their next investment, whether they’re investing time, money, or resources.
Scott Hartley is a global early-stage investor and author of The Fuzzy and the Techie: Why the Liberal Arts Will Rule the Digital World. His passion lies in emerging markets and big ideas that improve lives, particularly in financial services, health, supply chain, and logistics. Scott has served as a Presidential Innovation Fellow at the White House and has co-founded two venture capital firms: Everywhere Ventures and Two Culture Capital. Satyen and Scott discuss the techie and fuzzy sides of Silicon Valley, the advancement of tech, and how Scott chooses his next investment.
“I love this thought around data collection and big data is one thing, it's collecting information. But, then turning that information into knowledge and into wisdom. In one part, can be done through unstructured to structured data, through things like LLMs that are enabling us to move out of the information noise into a bit more knowledge noise, and then maybe into wisdom specificity. I still think that there's a leap there that's going to be human-driven. Whether it's a person sitting there interpreting or it's a team of engineers thinking about the sensitivities, the data tagging. There are human decisions in the mix somewhere along that chain, as we're taking on structured data and turning it into structured knowledge and wisdom. All these things to say, that even these deeply technical infrastructure-level technologies, have elements of humanity in them.” – Scott Hartley
Time Stamps:
(10:55): The genesis behind The Fuzzy and the Techie*
*(18:11): Subjectivity, structure, and bias
*(20:17): Scott’s investment focus
*(30:09): The “tables-stakes economy”
*(38:11): AI and public policy
*(47:43): Satyen’s Takeaways
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Precision in technology is powerful. When it comes to services like Uber, people know the exact location of the driver and how much the trip will cost. Precision helps banks lend money to folks with bad credit, but who took the initiative of telling a bank when they would miss a payment. Precision can even help deliver urgent medical supplies via drones in countries that need it most. Precision in technology means users have total visibility on location, price, and competitors, and they’re able to achieve better outcomes.
Maddy Want is the VP of Data for Betting and Gaming at Fanatics. Maddy has over a decade of data product experience spanning diverse web and app services, and has served companies like Audible, upday, and Index Exchange. When Maddy joined Fanatics, she was responsible for creating the data strategy, hiring the data team, and partnering with tech. Satyen and Maddy discuss her new book, Precisely, data governance, and why precision matters.
“We've gone to total visibility on location, total visibility on price, and ability to shop across competitors. To me, the big theme out of all of those things is it's not about the technology itself, it's not about drones, or it's not about auction mechanics like that power Uber. Those things are cool, but it's about the capability that it's given to the customers, or the patients, or whoever. The theme there is that they have more precision. They can be more precise about what kind of change they're requesting or they're affecting, and they can have an outcome that's much more tailored to them.” – Maddy Want
Time Stamps:
*(05:45): The disconnect between public policy and tech
*(13:09): The focus on precision
(20:18): Writing Precisely*
*(29:50): Maddy’s role at Fanatics
*(39:27): Structuring the team
*(47:19): Satyen’s Takeaways
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This podcast is presented by Alation.
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Links
Read Precisely: Working with Precision Systems in a World of Data
Connect with Maddy on LinkedIn
With the rise of GenAI, LLMs are now accessible to everyone. They start with a very easy learning curve that grows more complicated the deeper you go. But, not all models are created equal. It’s critical to design effective prompts so users stay focused and have context that will drive how productive the model is.
In this episode, Matthew Lynley, Founding Writer of Supervised, delivers a crash course on LLMs. From the basics of what they are, to vector databases, to trends in the market, you’ll learn everything about LLMs that you’ve always wanted to know. Matthew has spent the last decade reporting on the tech industry at publications like Business Insider, The Wall Street Journal, BuzzFeed News, and TechCrunch. He founded the AI newsletter, Supervised, with the goal of helping readers understand the implications of new technologies and the team building it. Satyen and Matt discuss the inspiration behind Supervised, LLMs, and the rivalry between Databricks and Snowflake.
“This idea of, ‘How does an LLM work?’ I think, the second you touch one for the first time, you get it right away. Now, there's an enormous level of intricacy and complication once you go a single step deeper, which is the differences between the LLMs. How do you think about crafting the right prompt? Knowing that they can go off the rails really fast if you're not careful, and the whole network of tools that are associated on top of it. But, when you think from an education perspective, the education really only starts when you are talking to people that are like, ‘This is really cool. I've tried it, it's awesome. It’s cool as hell. But how can I use it to improve my business?’ Then it starts to get complicated. Then you have to start understanding how expensive is OpenAI? How do you integrate it? Do I go closed source or open source? The learning curve starts off very, very, very easy because you can get it right away. Then, it quickly becomes one of the hardest possible products to understand once you start trying to dig into it.” – Matthew Lynley
Time Stamps:
(04:21): The genesis of Supervised*
*(11:34): The LLM learning curve
*(21:35): Time to build a vector database?
*(31:55): Open source vs. proprietary LLMs
*(41:35): Snowflake/Databricks overlap
*(47:47): Satyen’s Takeaways
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This podcast is presented by Alation.
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Links
Read Supervised
Connect with Matthew on LinkedIn
The art of medicine happens when physicians combine data and knowledge to deliver better patient outcomes. A physician that relies both on guidelines and their learned experience is creating a culture of data and insights and improving the lives of patients. Whether you’re a doctor or a data leader, knowing how to balance data and intuition will always drive better results.
Dr. Bapu Jena is an economist, physician, and Joseph P. Newhouse Professor of Health Care Policy at Harvard Medical School. He bridges his professions to explore the economics of healthcare productivity and medical innovation. Satyen and Bapu discuss leveraging data in healthcare, applying AI in medicine, and measuring the innovation of doctors.
“We have put a premium on the innovativeness of the technology. There could be a new molecule that attacks a pathway that has never been attacked before. If that molecule doesn't improve life expectancy or improve quality of life, then there's not a lot of value to me in that innovation, even though it's certainly innovative. I care more about whether or not it impacts patients' lives. The correlator to that is that you could have a medication which does not appear to be that quote, unquote, ‘innovative,’ at all because it's just a reboot, in some respect, of other medications. But, it's taken in a way that people are more likely to be adherent to. Those types of technologies are sometimes pooh-poohed on, but they could be very valuable because what ultimately matters is the outcome of whether or not a person gets better when they're on that medication, not how innovative it is. This is also a problem when it comes to data-driven interventions, as well. Because, there's a lot of interest in AI and non-medical technologies, or non-life science technologies. The key there is you've got to demonstrate that there's some outcome benefit.” – Bapu Jena
Time Stamps:
*(03:23): Predictable randomness
*(12:13): Data points tracking intensity of care
*(25:48): AI in medicine
*(31:29): The politics of standards of care
*(38:41): The challenges of influencing change
*(51:18): Satyen’s Takeaways
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This podcast is presented by Alation.
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Links
Read Bapu’s book Random Acts of Medicine
Random Acts of Medicine Substack
Listen to Freakonomics MD podcast
Starting a revolution is no easy task. Just ask Dr. Michael Stonebraker and Andy Palmer, co-founders of Tamr, the enterprise data mastering company. Their path to innovation begins with a universal problem. They also collaborate with other data radicals who challenge them to think differently and help them grow.
Michael is a database pioneer, MIT professor, and entrepreneur. He has founded nine database startups over 40 years and won the A.M. Turing Award in 2014. Andy is a serial entrepreneur and founder, board member, and advisor for over 50 start-ups. Satyen, Michael, and Andy discuss Tamr’s tech evolution, third normal form, and probabilistic methods.
“There's a lot of work to be done in these big enterprises of getting all the data cataloged, getting it all mastered and curated, and then delivering it out for lots of people to consume. Early on at Tamr, we did a lot of stuff on-premise and those projects just took so much longer and you ended up doing a whole bunch of infrastructure stuff that's just not required. We’re really encouraging all of our customers to think cloud native, multi-tenant infrastructure as the de facto starting point because that'll let them get to better outcomes much faster.” – Andy Palmer
“Data products and data mastering are basically a cloud problem. And so you want to be cloud native, you want to run software as a service, you want to be friendly to the cloud vendors. Tamr spent a lot of time over the last two or three years doing exactly that. There's a big difference between running on the cloud and being cloud native and running software as a service. That's what we're focused on big time right now. After that, I think there's a lot of research directions we're paying attention to. Trying to build more semantics into tables to be able to leverage. You can think of this as leveraging more exhaustive catalogs to do our stuff better. I think that's something we're thinking about a bunch.” – Dr. Michael Stonebraker
Timestamps:
*(04:47): The procurement proliferation
*(15:51): Solving data chaos
*(24:49): Probabilistically solving data problems
*(37:34): The future of Tamr
*(43:16): A great technologist versus a great entrepreneur
*(44:51): Satyen’s Takeaways
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This podcast is presented by Alation.
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Links
Connect with Andy on LinkedIn
Connect with Michael on LinkedIn
Learn more about DBOS
Over the last two seasons of Data Radicals, we’ve seen that data experts have been promoted to leadership roles. It’s proof that organizations are seeing the value of data and the significance of establishing a data culture.
In this episode, you’ll hear from past guests like Stan McChrystal, Tricia Wang, and Paul Leonardi as they discuss traits of a successful data leader, adapting your data strategies, and the importance of soft skills.
“I found that if I told somebody to do a task, they might try to do that task. But if I say, ‘Create this effect,’ they owned it because they felt a level of responsibility for what approach that they chose, and it made it much stickier.” – Stan McChrystal, Retired US Army General
“I think having gone through the valley of suffering myself, I have a massive amount of respect for founders because they carry a weight that most people will never realize. So it's hard for me not to like them.” – Jepson Taylor, Chief AI Strategist at Dataiku
“Those CDOs that are most successful quickly establish trust within business, with business sponsors. They work with the business sponsors to identify what are the one or two or three most important things to them and see if they can solve those questions, even if it’s with a very small subset of data, to begin to develop that relationship, that trust.” – Randy Bean, Author of Fail Fast, Learn Faster
“You have to be able to have a learner’s mindset. You have to understand what different teams and functions do and how they play into a bigger picture so that you can get into cause and effect. And then when you start to do that, you have a lot more ability to actually have impact.” – Wendy Turner-Williams, CDO at Tableau
Time Stamps:
*(00:48): Randy Bean: Alignment with expectations
*(02:39): Jennifer Belissent: The diplomatic CDO
*(05:01): Taylor Brown: Lead by example
*(05:44): Ashish Thusoo: The DNA of a CDO
*(07:48): Stan McChrystal: The strength of humility
*(15:40): Paul Leonardi: Collaboration, computation, and change
*(17:50): Mike Capone: Tapping your network
*(18:39): Tricia Wang: The other vital “C’s”
*(19:41): Bernard Liautaud: Setting your North
*(21:03): Jepson Taylor: Heroism and the human touch
*(22:45): Wendy Turner-Williams: Leading future leaders
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This podcast is presented by Alation.
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Links
Listen to Randy Bean’s episode
Listen to Jennifer Belissent’s episode
Listen to Taylor Brown’s episode
Listen to Ashish Thusoo’s episode
Listen to Stan McChrystal’s episode
Listen to Paul Leonardi’s episode
Listen to Mike Capone’s episode
Listen to Tricia Wang’s episode
Listen to Bernard Liautaud’s episode
Listen to Jepson Taylor’s episode
Listen to Wendy Turner-Williams’s episode
How does the NBA use data to compete and improve? When it comes to driving business growth with data, transparent communication makes success a slam dunk. By sharing innovative ideas and best practices across the business, one all-star team elevates the success of others across the entire organization.
Michael James, SVP and Head of Data Strategy and Analytics at the NBA, is committed to creating a better fan experience and making better business decisions through collaboration. In this role, he bridges executive leadership and technical expertise to create a data-driven culture in constant pursuit of innovation. Satyen and Mike discuss the NBA’s digital transformation, the future of GenAI in the league, and attracting more people to sports business analytics.
“We have very active communication with our teams. You build up a relationship over time and you start to realize, ‘If this person is sharing this thing that worked, we have a good sense of who else might be able to benefit from it.’ We'll make sure to package that up in a way that is not only informative, ‘Here's what the team did,’ but also has the tangible next steps. ‘Here's what you can actually do with this to drive the business.’ And it's no different on the data side. We've built a ton of data products through the years at the league level for our teams, also for different departments within our league office as well. But, the goal of all of those products is to make sure that we are driving better business decisions, we're driving a better fan experience, and, ultimately, that's going to lead to more revenue.” – Michael James
Time Stamps:
*(13:16): Sharing the (data) ball among the league
*(17:34): Establishing best practices across an enterprise
*(37:08): Measuring performance to measure culture
*(41:16): Improving DEI in data
*(46:57): Satyen’s Takeaways
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This podcast is presented by Alation.
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Links
Follow Michael on LinkedIn
As data radicals, we want to deliver insights that enable our organizations to know more, which we often do by providing an answer. Instead, we should be thinking about frameworks we can implement to communicate our ideas in simple and consumable ways.
Dave Kellogg knows the importance of a framework. Dave is one of the leading enterprise executives in software today and currently serves as the Executive in Residence at Balderton Capital. His blog, Kellblog, is a highly regarded content hub for software leaders, drawing on his experience as an angel investor, board member, advisor, and thought leader. Satyen and Dave discuss the evolution of the data industry, problem solving with frameworks, and mapping your business in a complicated world.
“People need the world simplified for them, and if you don't do it, somebody else will. A confused buyer is just going to buy from the market leader. If you're running a startup, you're definitionally not that. The burden of simplicity is on you. If you want to be successful, you need to have a very simple explanation of why someone should buy your stuff.” – Dave Kellogg
Time Stamps:
*(03:58): The evolution of BI and the BI customer
*(22:25): Solving complex problems with simplifying frameworks
*(32:41): Defining “data intelligence”
*(43:16): The DNA of a D&A career
*(47:44): Satyen’s Takeaways
Sponsor
This podcast is presented by Alation.
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Links
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Read Dave’s blog
If you’re on a journey of fitness, you know that perfectionism is your enemy. The same goes for data. There will always be another achievement that you wish to reach. Instead, focus on creating habits that will lead you to better data decisions and long term health.
Ameen Kazerouni knows this journey well. Ameen has spent his career at the intersection of science, data, and technology to create intuitive, data-driven experiences. In his role as CTO of Orangetheory Fitness, he is driving consumer wellness journeys by turning workout data into feedback and personalized recommendations. In this episode, Satyen and Ameen discuss data-driven exercise, keeping humans in the feedback loop, and AI data governance.
“We make a lot of investment in demystifying the Orangetheory workout. And there's a lot of parallels to data, and I love that because when you think about data in an organization, ‘Well, it's going to be a multimillion dollar investment.’ And it can get so overwhelming that instead of being like, ‘Let's start piece by piece,’ the instinct becomes, ‘Let's just keep guessing instead.’ Which is never a good idea. You should never, ‘Let's just revert to not using data at all because it's going to be really difficult to use data perfectly.’ It's the same thing with fitness. You don't revert to doing nothing at all because meeting all the requirements will be hard. Showing up and getting started is what gets you going.” – Ameen Kazerouni
Time Stamps:
*(04:42): AI in the workout
*(13:56): Data as a habit
*(25:45): AI data governance
*(38:36): The future of connected health
*(44:08): Satyen’s Takeaways
Sponsor
This podcast is presented by Alation.
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Links
Follow Ameen on LinkedIn
We’ve all heard the term “data literacy” by now. Although there is general consensus regarding the importance of knowing how to read, write, and communicate with data, some folks may take issue with the term itself. Wendy Batchelder, CDO at Salesforce, wants to reframe the conversation and focus on how people can leverage data to do their jobs better.
Wendy is a technology executive who has spent her career tackling business problems with technical solutions and transforming diverse team members into leaders. In her current role at Salesforce, Wendy is helping the right people access the right data at the right time — with the right controls. In this episode, Satyen and Wendy discuss the problems with data literacy training, the power of answering “so what?” questions, and the value of advocating for DEI in tech.
“You have to drop the jargon and get down to what are you trying to explain? If you're trying to help people to use more data for decision making, then just introduce the data. Don't sit down and say, ‘We're gonna talk about data literacy,’ because everyone's eyes gloss over and you lose their interest and their attention. It just doesn't give you a lot of respect. Part of our job as data experts is to help people to use data better and that's the conversation that should be had. But, the second you say things like data literacy, the tone of the conversation totally shifts.” – Wendy Batchelder
Time Stamps:
*(06:41): Data strategy at Salesforce
*(26:37): Keeping up with connectivity
*(28:40): Data literacy denial
*(35:22): DE&I in data
*(47:18): Satyen’s Takeaways
Sponsor
This podcast is presented by Alation.
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Links
Follow Wendy on LinkedIn
Read Wendy’s book Data Governance Handbook
Before search engines, we had to rely on memory and investigation to answer our questions. Then, search engines made answers instantly available. Now, in the age of AI, we have to engineer our questions to get the best results.
Frank Farrall, Deloitte's Strategy & Analytics Ecosystems and Alliances Leader, knows that asking the right questions is just as critical as knowing the answers. Frank is a global business builder with 20 years of experience helping clients and startups become billion-dollar businesses through AI and digital transformation.
In this episode, Satyen and Frank discuss identifying worthy investments, the sexiness of prompt engineering, and efficient engagement with AI.
“I think in a lot of cases, prompt engineering will at least become a skill that knowledge workers, creative workers use to get an outcome from the technology. I think some people will be highly, highly specialized. I actually think prompts are going to have value in organizations and I think prompt libraries and how you manage prompts will become a set of IP and something that's highly valuable inside organizations. I think prompt engineering has a very significant future ahead of it. I think all of us are going to have to learn some level of prompt engineering to be effective in the future.” – Frank Farrall
Time Stamps:
*(02:42): Defining the AI ecosystem
*(07:55): How to identify a worthy investment
*(23:23): How “sexy” is prompt engineering?
*(41:44): The future of generative AI
*(44:43): Satyen’s Takeaways
Sponsor
This podcast is presented by Alation.
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Follow Frank on LinkedIn
As folks in the data space, we’re introverts by nature. But, getting out of our comfort zone can open you up to endless possibilities. As one person who’s gone from CIO to CEO can tell you, the key to growth is getting comfortable with feeling uncomfortable.
That person is Mike Capone, CEO of Qlik, where he’s revolutionizing the business intelligence landscape through data. In this episode, Mike shares with Satyen how his decades of experience in product development, data science, and go-to-market operations influence his role as CEO today. Satyen and Mike discuss transitioning from CIO to CEO, navigating economic downturns, and stepping out of your comfort zone.
“Now is the time to get closer to your best customers. They're the ones who sustain you through these periods of economic ups and downs. The reality is for both of us and both of our companies, companies need data and analytics now more than ever. How are you going to navigate this uncertainty? You're going to navigate it through data. The conversation like, ‘Hey, we don't want to spend any more money on data and analytics because the environment is tough right now,’ is actually counterintuitive. The reality is you need data and you need real-time data to get through it because your old data models are useless.” – Mike Capone
Time Stamps:
*(02:51): The growth of Qlik
*(08:37): The relationship between private equity and software
*(20:00): From CIO to CEO
*(27:29): Navigating rough economic times with data and analytics
*(33:10): Maintaining long-term landscape leadership
*(40:24): Satyen’s Takeaways
Sponsor
This podcast is presented by Alation.
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Links
Follow Mike on LinkedIn
Follow Mike on Twitter
As human beings, we’re not accustomed to talking about data. In order to learn about new subjects, we traditionally use stories. However, bridging the gap between data and stories allows us to cross that barrier and create data-driven organizations.
In this episode, Satyen interviews Ashish Thusoo, GM of AI and ML at AWS. Previously, Ashish was the Founder and CEO of Qubole, a pioneering cloud data lake platform. He also served Facebook as the Engineering Manager of Data Infrastructure where he co-created Apache Hive with the aim to democratize data access and analytics. Satyen and Ashish discuss the accelerated push to the cloud, building a data culture, and how the economic climate is impacting customers.
“You have to remember, human beings are trained from the get-go to talk about stories, not data. That's how we learn. It takes special discipline to bring the conversation back to data, saying that, ‘You have this anecdote somewhere. Get me the data that proves or disproves it.’ That specific mindset has got to be inserted in the organization, and that's how it becomes data-driven. It's a very fine line, but if you cross that line, essentially you become a data-driven organization. But, if you stay on the side of anecdotes and stories, then you can't bridge that.” – Ashish Thusoo
Time Stamps:
*(02:33): The SQL excitement that powered Hive
*(13:42): The evolution of Qubole’s founder hypothesis
*(22:48): Navigating Amazon with AI/ML
*(31:41): The future of AI/ML investment
*(42:01): People are the foundation of the data culture
*(45:57): Satyen’s Takeaways
Sponsor
This podcast is presented by Alation.
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Learn more about AI/ML services on AWS
When building a data platform, it’s important to stay true to your vision. Whether that's through creating a definitive user experience or an open platform that allows people to build upon it, you’re constructing a cathedral. This cathedral is sophisticated and dependable, and allows for a bazaar of business intelligence, machine learning, and AI use cases.
In this episode, Satyen interviews Matei Zaharia, Chief Technologist and Co-founder of Databricks. Matei is an open source trailblazer and the creator of Apache Spark, a widely used framework for distributed data processing. He is also an Associate Professor of Computer Science at Stanford University where he leads various data management and machine learning projects. Matei and Satyen discuss the Databricks and Alation partnership, exploring how platforms can help companies own their data, and consider the value of democratizing open source large language models.
“One of the early stories about open source has been this thing about the cathedral and the bazaar. The cathedral is the thing that's all designed by one person, maybe. It's extremely coherent and so on, but also takes forever to build. And when you go there, there's one message you're hearing. And then the bazaar is the open thing. You don't know who's going to show up each day, but there'll be some really interesting goods and things that you just wouldn't see anywhere else. If you just want to get started and get stuff done, follow the defaults in the product and it'll work. But, we want to be open to some of that innovation and let people bring that in.” – Matei Zaharia
Time Stamps:
*(01:33): The story behind Spark
*(11:56): Solving for user problems versus product vision
*(20:12): The cathedral and the bazaar of open source
*(24:04): Matei explains the Databricks Unity Catalog
*(31:04): The Databricks and Alation partnership
*(43:36): The data culture at Databricks
*(48:21): Satyen’s Takeaways
Sponsor
This podcast is presented by Alation.
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Learn more about Databricks’s Unity Catalog
Learn more about Alation + Databricks
Once considered the outcast of Silicon Valley, data has metamorphosed as a cool kid that everyone wants to be friends with. In the last decade, data has solidified itself as the key to success in business. The same shift can be said for India who primarily operated behind the scenes, has emerged as a leader in innovation.
In this episode, Satyen sits down with Jitendra Putcha, EVP and Global Head of Data, Analytics, and AI at LTIMindtree. As an industry leader for over 20 years, he has solved data and analytics challenges for global companies by creating innovative next-generation solutions. Satyen and Jitendra discuss data platform modernization, data quality initiatives, and the future of AI and data science.
“Gone are the days of looking at India as the back office and factory models, to looking at this is an opportunity. There are two, three reasons for it. One reason is about the startup ecosystem and the unicorns we started building, created aspirations for people and created curiosity for individuals even during school itself. Which wasn't the case a couple of decades back. That's one. The second is the promotion during school itself to encourage people to be driving incubation startups and throw their ideas has dramatically increased, there are many forums today. So, that's the second one. The third one, even the large SIs like us, today we have what we call entrepreneurs. Within our own ecosystem, people can come out with an idea, put an entire canvas and business plan, go to the board, get the funding, create that as an incubator, and go and test the market. If it is working, create that as an entire product line. I think when the young generation is able to get exposure, already been educated, and also they see in their workplace this kind of opportunity, I think that's the biggest benefit the younger generation is able to do, which probably wasn't the case before.” – Jitendra Putcha
Time Stamps:
*(09:57): The evolution of service providers
*(13:22): The transformation of the Indian talent base
*(30:52): The data marketplace and “data as a service”
*(46:35): Defining the ROI of data tools
*(53:17): Satyen’s Takeaways
Sponsor
This podcast is presented by Alation.
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Links
Follow Jitendra on LinkedIn
Follow Jitendra on Twitter
Learn more about Snowflake Summit
There are some things that just can’t be quantified by data; imagine trying to portray your childhood in a spreadsheet! Yet these experiences are valuable. How can data teams capture qualitative information – and use it to steer the business? It starts with getting your data team out of the building. Only then can they gain insights about customer pain points and what the data is failing to tell us.
In this episode, Satyen interviews Tricia Wang, a “tech ethnographer” and co-founder of Sudden Compass, a consulting firm helping companies improve their business through thick data. She also co-founded CRADL (Crypto Research and Design Lab) with the mission to create inclusive and sustainable growth of the crypto ecosystem. Satyen and Tricia discuss the power of thick data, the value of digital personhood, and the dangers of quantification bias.
“Your job as a Chief Data Officer or a data leader in the company is, data is only part of your job generating the quantification to reflect back to the company. The other half is the bleeding edges around communication and helping the rest of your business, your business counterparts, to understand the value of this in a way that isn't scary and where they can see that it actually is going to improve their business. [...] But that takes a really brave kind of leader to work that way because it's not just about having the light shine on you, but it's about you making others and your company successful.” – Tricia Wang
Time Stamps:
*(01:35): The role of a tech ethnographer
*(15:29): Tricia gives a rundown of thick data
*(23:06): Understanding customers by getting out of the building
*(32:36): Why quantification bias is dangerous to growth
*(44:48): The importance of digital personhood
*(57:22): Satyen’s Takeaways
Sponsor
This podcast is presented by Alation.
Learn more:
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Subscribe to the newsletter: https://www.alation.com/podcast/
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Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/
-
Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/
Links
Follow Tricia on LinkedIn
Follow Tricia on Twitter
Watch Tricia’s TED Talk
Visit Tricia’s website
Learn more about Sudden Compass
In a world where technology is constantly evolving and AI is everywhere, it’s all too easy for content to be deceitful, including scientific papers. Capturing source metadata, incentivizing reproducibility, and protecting whistleblowers are steps we can take to ensure science remains honest.
In this episode, Satyen interviews Dr. Elisabeth Bik. Elisabeth is an experienced microbiologist whose groundbreaking work in scientific integrity has led to more than four thousand potential cases of improper research conduct. She also founded the blogs Microbiome Digest and Science Integrity Digest, and was awarded the John Maddox Prize for "outstanding work exposing widespread threats to research integrity in scientific papers" in 2021. Satyen and Elisabeth discuss image manipulation in scientific papers, the impact of AI on scientific integrity, and why paper mills must be stopped.
“There's people looking at statistical problems or DNA sequences that don't make any sense, and appear to have been made up, or plagiarism. [...] We have a community of people doing this, data detectives or image detectives. And I think what we have in common is a desire to make science better and to flag these papers so that other people can see that there's a potential problem with that paper.” – Dr. Elisabeth Bik
Time Stamps:
*(01:34): Image manipulation in the context of scientific papers
*(17:41): Elisabeth explains scientific paper mills
*(22:52): Why biomedical research needs to slow down
*(34:20): How Elisabeth manages backlash from scientists
*(46:45): How prevalent fraud is in science today
*(50:32): Satyen’s Takeaways
Sponsor
This podcast is presented by Alation.
Learn more:
-
Subscribe to the newsletter: https://www.alation.com/podcast/
-
Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/
-
Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/
Links
Follow Elisabeth on LinkedIn
Follow Elisabeth on Twitter
Visit Microbiome Digest
Visit Science Integrity Digest
Data governance is often seen as a confusing topic but everyone, even dummies, are capable of applying it to their organization. By starting with the “why” and acting on the most critical pieces, you can build a successful data governance initiative.
In this episode, Satyen interviews Dr. Jonathan Reichental, author of Data Governance for Dummies and Founder of Human Future. He is an Adjunct Professor at several universities, including the University of San Francisco, Pepperdine University, and Menlo College. Dr. Reichental also served as the Chief Information Officer at both O’Reilly Media and the city of Palo Alto, California. Satyen and Dr. Reichental discuss implementing data governance step-by-step, avoiding common governance pitfalls, and the future of smart cities.
“I do think in the long run though, data governance is not about a narrow target. You will build a better business if you hire all the right people, if you build the right products, and deliver the right services, not by doing just one thing and doing it really well. It's a comprehensive approach to running a successful business, as you know well. And I think data governance should be thought of in the short term as targeting some very specific things, but long term as a cultural shift in how you actually think about data and how you use data on the backend and in the front end of your business.” – Dr. Jonathan Reichental
Time Stamps:
(01:34): Dr. Reichental dives into his book Data Governance for Dummies*
*(08:51): How to convince people to invest in data
*(13:27): Dr. Reichental defines data governance and how it relates to data management
*(24:11): The signs a data culture is ready for governance
*(42:42): Dr. Reichental’s opinion on cryptocurrency and blockchain
*(47:20): Satyen’s Takeaways
Sponsor
This podcast is presented by Alation.
Learn more:
-
Subscribe to the newsletter: https://www.alation.com/podcast/
-
Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/
-
Satyen’s LinkedIn Profile:
https://www.linkedin.com/in/ssangani/
Links
Follow Jonathan on LinkedIn
Follow Jonathan on Twitter
Read Jonathan’s book Data Governance for Dummies
Visit Jonathan’s website
Vulnerability is an important quality often overlooked in the world of tech. Yet being vulnerable and authentic helps you to set realistic expectations, speak with executives on a human level, and connect with your audience.
In this episode, Satyen interviews Jepson (Ben) Taylor, Chief AI Strategist at Dataiku. Jepson is a visionary in the advancements of AI, ML, and data science. Prior to joining Dataiku, he served as the Chief AI Evangelist at DataRobot and co-founded the deep learning startup Zeff.ai, which was acquired by DataRobot in 2020. Jepson is a frequent industry speaker and collaborates with the data science community to improve AI and deep learning. Satyen and Jepson discuss the power of failure, the lie of job security, and proving data’s worth through storytelling.
"If nothing is failing, then it's not a very innovative company, not a very innovative culture. So there is a fraction of failure that for a mature organization you should celebrate. But with failure, you have the time urgency. How can we fail faster? I'd rather fail this week than four months from now." – Jepson Taylor
Time Stamps:
*(03:25): The special ingredient for speaking with executives
*(11:52): Learning to embrace the expectation of failure and failing fast
*(18:13): The two rules of good storytelling: authenticity and knowing your audience
*(30:47): Jepson’s advice for joining a startup and the lie of job security
*(44:08): The importance of celebrating the soul of your user
*(50:43): Satyen’s Takeaways
Sponsor
This podcast is presented by Alation.
Learn more:
-
Subscribe to the newsletter: https://www.alation.com/podcast/
-
Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/
-
Satyen’s LinkedIn Profile:
https://www.linkedin.com/in/ssangani/
Links
Follow Jepson on LinkedIn
Follow Jepson on Twitter
It’s time to launch your digital transformation – but first you’ll need new tools. Buyer, beware: Technology selection is tough because it’s impossible to predict the future. How can leaders take this journey with clarity and confidence? In this episode, Satyen interviews professor Paul Leonardi, author of The Digital Mindset, to learn the answer.
Paul is an expert in digital transformation and organizational change, Duca Family Professor of Technology Management at UC Santa Barbara, author of 4 books on technological innovation, and consults major tech companies like Google and Microsoft. Satyen sits down with Paul to discuss the skills leaders need to thrive in a digital landscape, how to demonstrate ROI with data, and why beta testing new software is critical for successful tech selection.
“Many of our organizations today are not used to thinking about data as a byproduct of actions that we take on the suite of tools that we use to do our work. And a data culture, for me, is one that recognizes that almost everything we do produces data in some way, shape, or form. And we can use those data, maybe to our advantage, but at least we can use those data to explore, ‘Can they tell us things that we might not know or otherwise have access to, if we aren't perceptive and thinking about where those data exist and how we might use them?’” – Paul Leonardi
Time Stamps
*(01:47) How an organizational change expert views behavior and data
*(15:04) The importance of beta testing technology’s functionality
*(18:04) Skills digital leaders need to thrive in the world of digital algorithms and AI
(22:17) Paul’s inspiration behind authoring The Digital Mindset*
*(27:42) The 30% knowledge rule of surviving in a digital environment
*(38:52) Paul breaks down what a digital mindset is
*(46:11) How to successfully convince executives to invest in technology
*(54:12) Satyen’s Takeaways
Sponsor
This podcast is presented by Alation.
Learn more:
-
Subscribe to the newsletter: https://www.alation.com/podcast/
-
Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/
-
Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/
Links
Follow Paul on LinkedIn
Check out Paul’s Website
Read Paul’s book The Digital Mindset
Data has never been more readily available, yet the world seems more confusing than ever. In this episode, Satyen interviews Tim Harford, who’s on a mission to help everyone become a Data Radical.
As a Senior Columnist for The Financial Times, author of The Data Detective, and host of the Cautionary Tales podcast, Tim joins us ready to change how we view numbers—and in turn, the world. Him and Satyen discuss everything from how he develops ideas for stories driven by data to fostering a spirit of curiosity in how we approach the world (and try to change it).
“Approach the world and numbers with a spirit of curiosity. Very often we don't behave like we like curiosity. We often like simple answers or we often like to just win an argument. But the world is a confusing place. The world is fascinating—I have questions and maybe the data can help me answer those questions. If that's the spirit with which you approach the data, then obstacles become intriguing. Mysteries become satisfying puzzles. Arguments turn into constructive, exploratory questions and it's just a lot more fun.” - Tim Harford
Time Stamps
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(2:26) How an award-winning storyteller approaches his work
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(8:13) Data brings people comfort and knowledge in times of crisis (like the pandemic)
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(16:50) A glimpse into Tim’s book The Data Detective
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(19:49) The importance of “the stuff around numbers” in increasing data literacy
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(25:03) Approaching the world with humility and curiosity
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(37:15) How you can be an undercover economist
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(39:10) Satyen’s Takeaways
Sponsor
This podcast episode is presented by Experian.
Learn more:
-
Subscribe to the newsletter: https://www.alation.com/podcast/
-
Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/
-
Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/
Links
Follow Tim on LinkedIn
Check out Tim's Podcast Cautionary Tales
Read Tim’s Book “The Data Detective”
The second season of the Data Radicals podcast launches on February 15th! I’m your host, Satyen Sangani.
In TV, some second seasons can be disappointing. After a triumphant season one, there’s no more story to tell.
Fortunately, that’s not the case in tech. Innovations, discoveries, and market movement — plus a new round of guest stars — guarantee plenty of plots and intrigue, all with one common theme: data.
Last season, we heard from industry leaders, journalists, analysts, and founders. They gave expert advice for using data to navigate any struggle: from overhauling your company’s governance strategy… to even getting a date!
These data radicals see things that nobody else can. And this next season is no different. We have incredible guests lined up who will give you the tools you need to bring your business – and career – to the next level.
We’re welcoming guests like: Tim Harford, of the BBC’s More or Less; Jonathan Reichental, Author of Data Governance for Dummies; Mike Capone, CEO of Qlik (and Talend), Ameen Kazerouni, CTO of OrangeTheory, and so many more.
So tap the follow button and get ready to hear radical strategies from the most innovative minds in the world.
Welcome to season two of Data Radicals!
Powered by the team at Alation.
Data governance isn’t easy. It demands a vision, strategy, and clear-cut plan that ultimately supports the business. Many have tried, and failed, to lead governance initiatives. What can we learn from the rare few who have led these tricky projects to success?
Learn top tips in this supercut episode, which compiles the best data governance advice from season 1. Featuring Bob Seiner of KIK Consulting, Jennifer Belissent, Principal Data Strategist at Snowflake, Francesco Marzoni, CDO of Inkga Group, Michelle Hoiseth, former CDO of Parexel, and more!
“We were, very much, very careful about using language around data enablement, not data governance. Yes, there’s a certain amount of this, of course, that’s about control. But it’s control and service to the aims of the business. It’s not a gate control for the sake of pure defensive posture. As a business, we needed to be able to do more with our data. It’s been an educational journey and it had to be timed with the advanced analytics initiatives.” - Michelle Hoiseth, former CDO of Parexel
Time Stamps
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(1:12) What is Data Governance?
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(4:38) How to get stakeholders excited about governance
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(7:10) Making your implementation a success
Sponsor
This podcast is presented by Alation.
Learn more:
-
Data Radicals: https://www.alation.com/podcast/
-
Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/
-
Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/
Links
-
Connect with guest Bob Seiner on LinkedIn
-
Connect with guest Paola Saibene on LinkedIn
-
Follow guest Francesco Marzoni on LinkedIn
-
Follow guest Jennifer Belissent on LinkedIn
-
Follow guest Wendy Turner-Williams on LinkedIn
The modern data stack, sparked by the Redshift revolution and furthered by cloud-platform players like Snowflake and Databricks, forever changed the data integration landscape. Realizing that ETL tools badly underperformed in the cloud, Fivetran strengthened the stack with an ELT pipeline to perform transformations within the data warehouse environment.
George Fraser, Fivetran Co-Founder and CEO, and Taylor Brown, Fivetran Co-Founder and COO, join us in this episode to discuss their founding story — from Y Combinator to $5.6 billion valuation — and the hard reality that data’s role is often to prove your assumptions are wrong.
“Taylor was the one who really locked onto this term years ago. I remember him saying ‘modern data stack, that's what we're doing.’ And it was not a commonly used term at the time. He's like, ‘that's the term we need to lock onto.’ Over the years it has really grown, but he has a decent amount to do with the present day popularity of that term.” — George Fraser
Time Stamps
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(0:00) A deep dive into Fivetran
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(10:10) Navigating the product journey
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(15:02) Normalization vs denormalization
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(17:00) The power of saying “no”
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(20:15) Inventing the Modern Data Stack
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(27:26) The future of Fivetran
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(29:47) Making data-driven decisions
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(33:20) Advice for building your own data culture
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
Connect with George on LinkedIn
Connect with Taylor on LinkedIn
Check out Fivetran
DataOps is having a moment. Where does it sit in the data lifecycle? And how is this emerging trend changing data management today? To find out, Satyen sits down with guest speaker Michele Goetz, author of The Forrester Wave: Enterprise Data Catalogs for DataOps.
“DataOps is really the engineering and practices of designing and developing data capabilities, launching them out to production and ensuring that they're providing value and delivering on the outcomes that businesses expect in being able to use that data.” — Guest Speaker Michele Goetz
Time Stamps
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(0:00) The birth of DataOps
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(2:43) What is DataOps?
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(11:18) DataOps and the Data Mesh
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(18:41) Diving into data prep
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(22:09) Tackling data governance for your data catalog
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(31:17) The future of the data cataloging landscape
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
Connect with guest speaker Michele on LinkedIn
Check out Forrester
Librarians have played a crucial role in the management of data throughout history. From the halls of the Library of Alexandria to the Library of Congress all the way to your elementary school: librarians have been the guardians of our knowledge for thousands of years. But now that we’re in the digital age…has that role shifted? That’s what we’re exploring today.
Deb Seys, Senior Director of Learning and Communities at Alation, is a passionate librarian who has made the jump from the dewey decimal system to the data catalogue. She shares with us how data is the most powerful tool in a librarian’s arsenal, how data can better your user experience, the steps to finding the best answers for any question, and much more.
"One of the most misunderstood things about data is that it’s this objective hard thing that sits there and reveals something. It’s just not true at all. A lot of the work of documenting metrics is about what's important to a group of people, and then using that to tell a story or make a decision." - Deb Seys
Time Stamps
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(0:00) How librarians have championed data for millennia
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(4:25) The modern-day librarian’s biggest ally is the data catalogue
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(8:24) Understanding the basics of data
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(14:45) How to be a data-driven librarian
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(17:09) The relationship between data and user experience at eBay
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(22:04) The secret to finding the right answers
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(26:52) Creating the right data mindset
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
Connect with Deb on LinkedIn
Check out Alation
In this episode, CDO Steve Pimblett of The Very Group describes the four pillars of extracting value from data and shares how he’s built a successful B2C data team. Steve shares his advice for CDOs just starting off or maybe years into the role. He discusses optimizing your data strategy, making your business more data-driven, leveraging account managers, and much more.
“Set up some sort of an internal agency to see how you can make business areas more data-driven.” - Steve Pimblett
Time Stamps
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(2:28) What CDOs should know on day one
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(8:44) A deep dive into The Very Group
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(14:26) How to approach your company’s data strategy
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(20:56) Using data to better approach tough conversations
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(22:59) Account managers are your secret weapon
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
Connect with Steve on LinkedIn
Check out The Very Group
With the data space more complex than ever, many people throw their hands in the air, and use solutions that fix many (but not all) of their problems. Today’s guest saw that complexity, and leaned into it.
Zhamak Dehghani is the creator of the data mesh. Where others saw chaos, she saw an opportunity. Zhamak dives into decentralization, all things data mesh, how data architecture can empower your workers, and much more.
“Seeing the real world problems made me curious about the data space—scratching the surface and seeing the discord between the reality of the complex world we're living in with data. The solutions weren't up to the task for dealing with that complexity, so it got me to look further for a solution.” — Zhamak Dehghani
Time Stamps
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(0:00) A brief history of data architecture
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(2:45) An intro to the data mesh
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(13:08) How analytics impact data engineers
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(18:54) The socio-technical approach
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(23:26) Getting started in the data mesh
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(28:59) Data mesh vs. Data fabric
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
Connect with Zhamak on LinkedIn
Check out Zhamak's book Data Mesh
What if you could combine data across industries and use cases? FAIR data is Findable, Accessible, Interoperable, and Reusable. These are the tenants that Francesco Marzoni, Chief Data and Analytics Officer of Ingka Group, has made central to his data-driven career.
In today’s episode, Francesco discusses the FAIR principles, data silos, the importance of data governance, and much more.
“The biggest lesson I got during the pandemic was the importance of having usability in data assets you can tap into and how much it can hurt you if you don't have usable data assets in times of crisis.” — Francesco Marzoni
Time Stamps
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(0:00) How Aristotle impacts data today
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(2:45) Exploring FAIR
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(14:07) Francesco used FAIR to navigate Nestlé through the pandemic
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(20:52) The reusability of data
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(27:00) How Francesco creates data programs
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(29:54) Future opportunities in data
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
Connect with Francesco on LinkedIn
Check out Ingka Group
While analysts in other fields face an ocean of data, those in social justice often face a desert. After all, those who abuse their power take care to conceal their crimes. How can data scientists hold the guilty accountable?
Today's guest has used data to uncover hidden human rights violations and uphold social justice. Tarak Shah is a Data Scientist at the Human Rights Data Analysis Group (HRDAG), a RAFTO award-winning non-profit. In this episode, he reveals how his work has exposed the crimes of violent dictators and police – delivering justice to the violated and a chance to heal the communities affected.
“In human rights, the question when we're talking about accountability is about what actually happened, not just what was documented.” — Tarak Shah
Time Stamps
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(0:00) Navigating a lack of data
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(2:46) Analyzing human rights violations in Guatemala
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(11:45) How data solved a murder mystery
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(14:02) Data’s insights into gender-based violence in Chicago
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(34:33) How you can help preserve human rights with data
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
Connect with Tarak on LinkedIn
Check Out HRDAG
In the era of “fake news” how do you know what to trust? Today’s guest, Seth Stephens-Davidowitz, has found the last dependable source of truth: Google. Armed with Google trends data, Seth illuminates hidden truths about racism, politics, and sexuality in our world today.
In today’s interview, Seth dives into his latest book Don’t Trust Your Gut: Using Data to Get What You Really Want in Life. He shares interesting insights he gathered along the way and how unfortunately, lying and data sometimes go hand-in-hand.
“Google trends is so powerful because it gives you an incentive to get the information you need. And PornHub, similarly, gives you an incentive to get the information you need. So even in an anonymous survey, there's no incentive for people to tell the truth.” — Seth Stephens-Davidowitz
Time Stamps
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(0:00) Is your data full of lies?
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(2:45) Seth’s book Everybody Lies
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(6:25) Google trends is a data treasure trove
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(11:36) How data can help you online date
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(16:25) Taking a data-driven approach to your personal life
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(19:33) The data behind happiness
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(24:13) Seth’s book Don’t Trust Your Gut and how it can change your approach to business
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
Check out Seth's Work
Seth’s book : Don’t Trust Your Gut: Using Data to Get What You Really Want in Life
Business intelligence has dominated corporate conversations for years. It’s an integral component of making data-backed decisions and as well as an important tool for any Data Radical. Today, we’re lucky enough to speak with a business intelligence pioneer, Bernard Liautaud.
Bernard is the Managing Partner of Balderton Capital and was the founder of BusinessObjects. In this interview, he and Satyen discuss the origins of business intelligence, what it’s like to found a company, creating healthy company cultures, and so much more.
“The No. 1 element of creating a culture is by setting an example, because you're going to be modeled after — unconsciously or consciously.” — Bernard Liautaud
Time Stamps
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(0:00) Bernard’s impressive history of making data digestible
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(2:45) The history of BusinessObjects
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(5:10) Inventing a new way to look at data
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(11:39) A deep dive into spearheading business intelligence
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(30:40) Creating a unique corporate culture
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
Connect with Bernard on LinkedIn
Check out Balderton Capital
Knowledge graphs are a critical tool in a Data Radical’s toolbox. But how can we make sure we’re leveraging them to the best of our ability? Today’s guest helps us out.
Kendall Clark, is the Founder and CEO of Stardog. His non-typical data journey has given him a wealth of knowledge that we’re lucky enough to hear today. He goes deep into knowledge graphs, philosophy’s impact on management, and much more.
“It's a little bit like asking the first person you ever date to marry you. It's not that that's definitely a mistake and we'll never work out, but it's risky. This is really what happens when you commit to doing integrations in the storage layer.” - Kendall Clark
Time Stamps
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(0:38) The human impact on data
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(3:06) Kendall’s unconventional journey into data
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(6:27) What is Stardog?
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(8:52) All you need to know about Knowledge Graphs
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(27:20) How to build culture at your organization
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
Connect with Kendall on LinkedIn
Check out Stardog
Information is power, and data is rarely as subjective as it seems. Savvy data leaders know one thing: data is political. And for many data professionals — that fact can feel discouraging. But it doesn’t have to be.
Today, Wendy Turner-Williams, CDO of Tableau, brings us a fresh perspective on how to win at the game of data politics. Shifting corporate culture can be difficult. But with Wendy’s tips and tricks, you’ll be able to transform your org into a data-driven machine. You won’t want to miss it.
Quote:
“When it comes to culture the thing that data professionals never love to talk about is that data's very political.” - Wendy Turner-Williams
Time Stamps
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(00:24) Who is Wendy Turner-Williams
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(2:37) Ins and outs of working on data in a software company
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(7:17) CDOs as supporting actors
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(16:26) The evolution of data governance
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(22:13) Wendy’s approach to data culture
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(24:45) The politics of data
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
Connect with Wendy on LinkedIn
Check out Tableau
Data is one of our most underutilized weapons in our arsenal. In today’s battles, the ability to gather, disseminate, consume and act on information has become the fundamental capability in the war on terror.
In this episode, Satyen speaks with Stan McChrystal, retired US Army General and Managing Partner of the McChrystal Group. They discuss Stan's experiences building a data culture in the US military, evaluating leaders, the concept of empowered command, and much more.
“Often you have data you don't know you have. ” - Stan McChrystal
Time Stamps
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(0:00) Using data as a weapon
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(3:16) Insight into changing massive organizations
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(11:33) How to find the best leaders
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(18:17) Running military operations backed by data
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(29:37) What success looks like for a US Army general
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
Connect with Stan on LinkedIn
Check out the McChrystal Group
The role of CDO is constantly evolving, and Data Radicals need to stay ahead of the curve. Today's guest gives us the inside scoop on how to do just that. Randy Bean is the founder and CEO of NewVantage Partners and author of Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI. In this interview, he and Satyen discuss the adoption of the chief data officer role, the challenges of creating a data culture, and the importance of soft skills for CDOs.
“Data-driven organizations never rest. They're never comfortable. Because they know that unless they continue to do things to stay data-driven—they won't be the most data-driven going forward.” - Randy Bean
Time Stamps
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(0:00) How the Dunning-Kruger effect impacts your organization
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(3:10) Randy’s beginnings in data
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(6:31) The endless data journey
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(10:05) The evolution of the CDO
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(19:51) How to know you’re a successful CDO
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(23:54) Barriers to building a data culture
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
Connect with Randy on LinkedIn
Check out NewVantage Partners
Read Randy’s Book: Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI
The early founders of the internet argued that privacy was dead. But today’s guest was motivated to revolutionize data privacy instead. Michelle Finneran Dennedy, CEO of PrivacyCode and co-author of The Privacy Engineer’s Manifesto, is one of the world’s first Chief Privacy Officer. She and Satyen talk about how the role of CPO has changed throughout her tenure, the tough work of designing privacy policies, the future of data privacy, and much more.
“CPOs today are business leaders and risk takers.” — Michelle Finneran Dennedy
Time Stamps
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(0:00) Privacy is dead. Get over it.
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(5:54) What made Michelle passionate about privacy
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(10:05) The evolution of the Chief Privacy Officer
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(12:29) What is data policy?
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(14:26) The roles morals and ethics play in data policy
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(19:23) Debunking privacy engineering
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(29:11) The future of privacy
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
Connect with Michelle on LinkedIn
Check out The Privacy Engineer’s Manifesto
No risk, no reward. Kyle Kirwan, co-founder and CEO of BigEye, helps companies manage risk more intelligently through the power of data quality. In this interview, Kyle describes his experience as one of the first data scientists and analysts at Uber, weighs the pros and cons of build versus buy, and defines the modern data stack – with an eye to what’s next.
“We're living in this Renaissance era when it comes to data tools.” - Kyle Kirwan
Time Stamps
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(0:00) What Monopoly teaches us about data
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(3:00) Data’s role at Uber
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(7:29) Building vs. Buying
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(12:14) The current and future landscape of data tools
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(17:58) Getting the right people involved
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(20:20) Creating a data culture
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(28:48) Insights into the modern data stack
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
Connect with Kyle on LinkedIn
Check out Bigeye
Seven Principles for Reliable Data Pipelines by Kyle Kirwan
Register for the Data Reliability Engineering Conference
Data Discovery in 2020 by Kyle Kirwan
Science and data are powerful tools of discovery, but our expectations are often misaligned with what science can actually do. Interpreting and understanding science involves nuance, humility, and an open mind – muscles that we don’t always flex as often as we should.
Today’s guest is Christie Aschwanden, journalist and NYT bestselling author. Christie is an expert in understanding the power – and limitations – of science. Christie explains why science isn’t broken, how to get reliable answers from the scientific method, and the science behind why quitting can sometimes be the best decision possible.
“I think it's really important to always recognize that there's a possibility that whatever you're thinking now is wrong…We need to understand that one of the most fundamental aspects of science is that it's provisional. We always have to be open to new evidence and recognize that there's always the chance that we will gain new evidence that will overturn our current ways of thinking. And that's okay.” -Christie Aschwanden
Time Stamps
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(3:12) Is Science Broken?
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(7:07) What is Motivational Reasoning?
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(11:27) The Importance of Nuance in Science
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(12:50) One Trait Christie Highly Recommends
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(14:40) The Misaligned Incentive Structure in Science Today
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(16:38) Stories Are the Way We Make Sense of Data
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(19:44) The Creative Process and the Value of Quitting
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(23:35) The #1 Thing that Any High-Performance Person Can Do
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(25:40) The Importance of Not Being Productive
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(27:21) The Benefits of Sleep and How to Get Better Sleep
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(30:30) Christie’s #1 Piece of Advice
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
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Christie’s Website
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Good To Go: What the Athlete in All of Us Can Learn from the Strange Science of Recovery
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Follow Christie on Twitter
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Christie’s Podcast “Emerging Form”
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Christie’s FiveThirtyEight Archive
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Psychology’s Replication Crisis Has Made The Field Better from FiveThirtyEight
The average CDO only lasts 18 months in their position. 18 months! With odds that dire, data leaders need all the inside scoop they can get to climb the massive mountain ahead of them. Luckily, today we’re giving you a roadmap.
Today’s guest is Peter Jackson, Co-Founder and Director of Caruthers and Jackson. Peter shares his advice on how to up your odds of passing that 18-month mark with flying colors. He discusses why CDOs are key players in a company’s future, and offers tips for navigating vendor relationships, building up data literacy in your organization, and much more.
"Wherever you find a spreadsheet—that's an opportunity for a quick win, because you might be able to automate that process by using some technologies and by teaching people new skills, and that will reduce data errors.” - Peter Jackson
Time Stamps
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(0:00) The high stakes for CDOs
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(2:29) How CDOs are change agents
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(6:02) How to know if being a CDO is right for you
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(8:40) Advice for new CDOs
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(11:45) The secrets of spreadsheets
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(17:06) How to make the most of vendor relationships
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(23:48) Building data literacy at your organization
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
Connect with Peter on LinkedIn
Check out Carruthers and Jackson
Whether you’re a whistleblower exposing fraud or a new CDO with uncomfortable insights at an organization, “just the facts” alone won’t convince the crowd. You need buy-in, conviction, and the ability to tell a compelling story.
In today’s episode, Dr. Margaret Heffernan, author, professor, and seasoned CEO, shares what she’s learned about leading teams to light. She shares insights on willful blindness, how to make people care about data, and how to build the most productive teams.
"What's really interesting about whistleblowers is that they're deeply ordinary." - Dr. Margaret Heffernan
Time Stamps
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(0:03) A history of whistleblowers
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(2:43) Alice Stewart and a pattern of willful blindness to important data
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(9:38) Data alone doesn’t win arguments
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(19:59) The psychology of whistleblowers
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(27:31) How to be a whistleblower
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(30:00) The downside of being a super chicken
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
Connect with Margaret on LinkedIn
Check out Margaret's book
Uncharted: How to Map the Future
What can the tale of the blind men and the elephant teach us about our own blindspots in the world of data?
Today’s guest helps us identify some of our data blindspots and learn how to attack them head on. Cindi Howson, Chief Data Strategy Officer at ThoughtSpot, shares how BI has transformed in recent years, what you can do to increase data literacy at your org, how your company can stay ahead of the hiring curve, and much more.
"CDOs have to associate with others outside the organization so they do not get demotivated or burned out when there's too many setbacks or naysayers." - Cindi Howson
Time Stamps
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(0:00) What elephants teach us about data
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(4:00) BI: then and now
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(12:05) The urgency of increasing digital literacy
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(17:07) How to start your data transformation
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(19:42) Competing in the labor market
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(26:49) The Data Chief Podcast
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
Connect with Cindi on LinkedIn
Check out ThoughtSpot
Corporate malfeasance is a cold fact. And when companies lie to the public, real people get hurt.
Amir Efrati, Co-Founder and Executive Editor of the Information, exposes the truths big firms conceal. His reporting has brought corporations to justice, won numerous awards, and been cited by federal judges.
In this episode, Amir discusses the Facebook Papers, controversies around self-driving cars, and the relationship between data-driven decisions and transparency. This one isn't for the faint of heart, and you won’t want to miss it.
"There was this dissonance between what was privately being shared and what was publicly being announced. People are making life decisions based on completely false assumptions. That's not good for anyone. Investors are investing pension money and other people's money in companies based on what's publicly announced and not what's privately known. That's not good either." - Amir Efrati
Time Stamps
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(0:00) Corporate responsibility vs public good
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(3:18) What’s The Information?
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(6:31) The inside scoop on the Facebook Papers
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(14:51) How to hack humans
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(17:12) The secrets of self-driving cars
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(23:57) Why the cloud is a trillion dollar industry
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
Connect with Amir on LinkedIn
Check out The Information
The world of data is often elusive and nebulous. And as time goes on, different myths circulate the zeitgeist. Today’s guest walks us through some common myths, and serves as our own personal mythbuster. Michelle Hoiseth, Chief Data Officer of Parexel, uncovers the biggest data challenges facing the pharmaceutical industry, and shares how you too can be a data mythbuster, how you can lead a data journey at your organization, and much more.
“I use a cartoon still where the top frame is ‘who wants clean data’ and everybody in the crowd has their hand up. Then the next frame is ‘who wants to clean their data’ and everyone's hand is down and they're looking at their shoes—and that was Parexel." - Michelle Hoiseth
Time Stamps
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(0:00) Being a data mythbuster
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(2:45) The data problems facing the pharmaceutical industry
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(10:42) Embarking on a data journey
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(24:10) Lessons learned from a data overhaul
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(25:23) A data cultural evolution
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
Connect with Michelle on LinkedIn
Check out Parexel
Anxiety about math. For most Americans, it’s so deeply ingrained it’s practically a personality trait. Unfortunately, this fear can linger in our psyches as we age. But, thankfully, Data Radicals like Caroline Carruthers are here to confront that fear and educate our data-anxious peers.
Caroline is the co-author of “The Chief Data Officer's Playbook,” and today she’s explaining why CDOs play a crucial role in building a data-based organization. She shares advice on how to be an effective CDO, and promote data literacy so people embrace data-driven decision making at your company, and much more.
"Spend time with your stakeholders and talk to them about data. Relax them, take them out for coffee and cake, sit there and just get to know them as a person and ask them about what they're trying to do." - Caroline Carruthers
Time Stamps
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(0:04) Being a data cheerleader
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(2:39) How to be an effective CDO
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(8:45) Finding your data skillset
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(11:59) Turning your stakeholders into Data Radicals
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(14:40) Teaching data literacy to your organization
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(20:18) Raising the next generation of Data Radicals
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(26:23) Meeting people where they are
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
Connect with Caroline on LinkedIn
Check out Carruthers and Jackson
With stories of different space explorations constantly in the news—it’s an exciting time to be a space enthusiast. But with the habitation of Mars seemingly on the horizon it begs the question: is there going to be an HOA on Mars?
Figuring out how governance will work on Mars is a logistical problem that just stems more and more questions, not unlike data governance today. Similarly, we’re in the exciting times of data governance, but are still relatively unsure about any of the logistics. Today, Paola Saibene, Principal of Teknion Data Solutions, shares her insights on how to confront data governance head on, transform your organization with data, the future of data, and much more.
"Protect the people inside of the data. And I don't mean in a cyber sense only, but protect the people by having that care put into how it is being treated all the way post capture." - Paola Saibene
Time Stamps
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(0:00) Data Governance and the Wild Wild West
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(3:46) How to help your clients face data challenges
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(7:49) What is data governance?
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(8:44) The key to certified data
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(14:13) How to advocate for data at your organization
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(20:21) The future of data work
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
Connect with Paola on LinkedIn
Check out Teknion Data Solutions
We spend our lives surrounded by stories—the ones we hear, read, and watch. Stories are how we learn how to navigate life. Some have played a major role in pivoting how entire industries approach storytelling like The Sixth Sense movie and maybe, if you listen to today’s episode, your next work presentation.
Today, we’re hearing from Cole Nussbaumer Knaflic, Founder and CEO of storytelling with data. She shares her tips and tricks for data radicals venturing into the world of data storytelling. We’ll learn how to better understand your audience, what makes a data story captivating, how to improve your public speaking skills and much more.
"There is so much potential for the person who has strong technical skills and can speak in ways that make other people feel engaged and attentive. People will pay more attention to your data if you can talk to them in ways that make them want to keep participating and become part of that discussion." - Cole Nussbaumer Knaflic
Time Stamps
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(0:00) What The Sixth Sense teaches us about data
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(3:13) Understanding your audience
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(4:45) Building blocks of a compelling data story
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(12:16) Why data enthusiasts need strong public speaking skills
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(15:28) The secret to being a great data storyteller
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(20:50) The importance of feedback
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
Connect with Cole on LinkedIn
Check out storytelling with data
Communication—or lack thereof—tends to be at the core of most of life’s issues. Although most of us know this, few of us set out to create systems to address the problem. Today, we’re learning from two people who did: L.L. Zamenhof and Bob Seiner.
L.L. Zamenhof saw the crucial role words played in dividing people, and wanted to create a language that would unify the world. His language, Esperanto, was born, but unfortunately didn’t catch on. Bob Seiner, President & Principal of KIK Consulting, on the other hand, has had more success. He has seen how miscommunication has negatively impacted how organizations handle data and data governance, and has worked to improve their processes and revolutionize their workflows. Today, he shares his insights with us. You won’t want to miss it.
"Most organizations think that governance has to be top down, but there is an alternative approach. You can look to leverage things that already exist within your environment first and then apply the heavy-handedness wherever you need to." - Bob Seiner
Time Stamps
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(0:00) What’s Esperanto, and why aren’t we speaking it?
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(4:05) How to communicate about data
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(8:16) Why your organization is struggling to share data
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(11:17) Understanding data governance
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(14:27) How to improve data governance at your company
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(17:56) The three O’s of data
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(20:01) Creating a data culture
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(22:03) The future of data
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
Connect with Bob on LinkedIn
Check out KIK Consulting
You may not realize it, but data literacy is an issue that affects everyone. From the mom-and-pop shop around the corner to the Fortune 500 C-suite, everyone has skin in the game. Yet, how to teach data literacy is still not widely understood or prioritized by data leaders. That needs to change, and the first step is listening to today’s episode.
We’re talking with Jennifer Belissent, Principal Data Strategist at Snowflake, about tackling data literacy. She shares how data literacy impacts everything from smart cities, to your local grocery store, to your corner office. Tune in to learn how you can use data literacy to build your own data culture, have a lens into your customer's behavior, increase productivity, and much more.
"Many people are not comfortable with data. We've created the haves and have-nots within an organization. We need to make sure that data literacy programs are inclusive. " - Jennifer Belissent
Time Stamps
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(0:00) What’s happening with data literacy today
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(3:28) The inevitability of smart cities
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(5:34) Data serves as a lens into people’s behavior
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(9:05) How to approach data literacy
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(17:04) The CDO’s role in data literacy
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(22:55) Creating a data culture
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(27:44) Teaching your employees data literacy
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
Connect with Jennifer on LinkedIn
Check out Snowflake
Growing up, we’re all asked what we want to be when we’re older. Our answer is normally met with an explanation of the years of education and dedication we’ll have to go through to get there. Whether it’s trade school, medical school, a PhD, or apprenticeship, we start to understand that we’ll need years of specialized training to get to where we want to be. But what if that whole way of thinking is wrong?
Today, we’re learning to completely shift our approach to the understanding of expertise. Our guest is David Epstein, bestselling author of Range: Why Generalists Triumph in a Specialized World and The Sports Gene: Inside the Science of Extraordinary Athletic Performance. He shares why you should shift your focus from being a specialist to a generalist, and how that can exponentially increase your odds for success. You won’t want to miss it.
"The people who are good forecasters sometimes have an area of specialty, sometimes they don't, but more important than what they think, is how they think." - David Epstein
Time Stamps
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(0:00) How Satyen became a generalist
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(2:52) Why IQ tests aren’t as helpful as you think
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(5:53) What makes an environment kind or wicked
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(13:23) Getting comfortable with sporadic success
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(18:31) The perks of generalization
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(22:57) The shortcomings of specialization
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(26:36) What we can learn from Vincent Van Gogh
Sponsor
This podcast is presented by Alation.
Hear more radical perspectives on leading data culture at Alation.com/podcast
Links
Connect with David on LinkedIn
Check out David's website
Data confers immense power. With it, we can transform the world as we know it. But, despite all that potential—too few people know how to wield data. 2.5 quintillion bytes of data are created every day. Saul Alinsky famously said “If people don’t think they have the power to solve their problems, they won’t even think about how to solve them.” Translating that information into meaning is one of the most significant challenges of our time. We believe it’s the key to addressing our most pressing issues, from climate change to global inequality. Many of us see this as an opportunity: How do we teach more people to leverage data? This show is called Data Radicals, hosted by Satyen Sangani, CEO of Alation. Join us on this journey as we engage data leaders, academics and analysts from all walks of life. We’ll be featuring the stories of the culture builders and truth-seekers who are doing big things with data. Whether you’re a Chief Data Officer, a young data analyst on the path to the C-suite, or a data fanatic who wants to learn from today’s change makers - this podcast is for you. So tap the follow button, and get ready to hear from some of the most innovative minds in the world of data. Welcome to Data Radicals. Powered by the team at Alation.