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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
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“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
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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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This podcast is presented by Alation.
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Links
Connect with Chris on LinkedIn
Order Chris’s book How Data Happened
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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.
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“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
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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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Sponsor
This podcast is presented by Alation.
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Links
Connect with Taylor on LinkedIn
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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.
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“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
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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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Sponsor
This podcast is presented by Alation.
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Links
Connect with Stewart on LinkedIn
Learn more about IDC MarketScape
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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.
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“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
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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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This podcast is presented by Alation.
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Links
Connect with Jeremy on LinkedIn
Order Jeremy’s book, Mastering AI
Learn more about SB-1047
Learn more about Anthropic’s AI Constitution
Learn more about the FASTER Act
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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.
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“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
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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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Sponsor
This podcast is presented by Alation.
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Links
Connect with Geraldine on LinkedIn
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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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This podcast is presented by Alation.
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https://www.linkedin.com/in/ssangani/
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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.
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“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
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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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This podcast is presented by Alation.
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Links
Read Rules for Radicals
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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.
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“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
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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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This podcast is presented by Alation.
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Links
Connect with Edo on LinkedIn
Watch Edo’s TED Talk
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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.
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“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
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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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Sponsor
This podcast is presented by Alation.
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Links
Connect with Sanjeevan on LinkedIn
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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.
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“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
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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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This podcast is presented by Alation.
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https://www.linkedin.com/in/ssangani/
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Links
Learn more about Raoul Wallenberg’s fate
Connect with Ari on LinkedIn
Follow Ari on X
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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.
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“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
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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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Sponsor
This podcast is presented by Alation.
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https://www.linkedin.com/in/ssangani/
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Links
Read In Emergency, Break Glass
Connect with Nate on LinkedIn
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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.
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“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
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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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This podcast is presented by Alation.
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Links
Connect with Guy on LinkedIn
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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.
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“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
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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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Sponsor
This podcast is presented by Alation.
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Links
Read The Fuzzy and the Techie
Visit Scott’s website
Connect with Scott on LinkedIn
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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.
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“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
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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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Sponsor
This podcast is presented by Alation.
Learn more:
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Links
Read Precisely: Working with Precision Systems in a World of Data
Connect with Maddy on LinkedIn
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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.
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“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
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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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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/
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Links
Read Supervised
Connect with Matthew on LinkedIn
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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.
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“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
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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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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/
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Links
Read Bapu’s book Random Acts of Medicine
Random Acts of Medicine Substack
Listen to Freakonomics MD podcast
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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.
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“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
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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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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/
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Links
Connect with Andy on LinkedIn
Connect with Michael on LinkedIn
Learn more about DBOS
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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.
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“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
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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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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/
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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
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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.
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“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
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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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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/
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Links
Follow Michael on LinkedIn
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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.
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“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
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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
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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/
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Links
Follow Dave on LinkedIn
Follow Dave on Twitter
Read Dave’s blog
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