Data Radicals
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Top 10 Data Radicals Episodes
Goodpods has curated a list of the 10 best Data Radicals episodes, ranked by the number of listens and likes each episode have garnered from our listeners. If you are listening to Data Radicals for the first time, there's no better place to start than with one of these standout episodes. If you are a fan of the show, vote for your favorite Data Radicals episode by adding your comments to the episode page.
02/19/25 • 40 min
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
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“ 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
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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
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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 Tom’s MIT Sloan article Five Trends in AI and Data Science for 2025
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
02/21/24 • 41 min
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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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
Gen AI at Work: Inside the Digital Bank Revolution with Dr. Geraldine Wong, CDO at GXS Bank
Data Radicals
10/23/24 • 41 min
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.
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
Using AI to Revolutionize CX with Michael Olaye, EVP & Managing Director at Hero Digital
Data Radicals
01/08/25 • 39 min
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.
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“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
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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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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/
David’s LinkedIn Profile:
https://www.linkedin.com/in/davidwchao/
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Links
10/30/24 • 47 min
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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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 Jeremy on LinkedIn
Order Jeremy’s book, Mastering AI
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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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
11/13/24 • 42 min
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.
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
08/17/22 • 36 min
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.
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“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
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Time Stamps
(0:00) Is your data full of lies?
(2:45) Seth’s book Everybody Lies
(6:25) Google trends is a data treasure trove
(11:36) How data can help you online date
(16:25) Taking a data-driven approach to your personal life
(19:33) The data behind happiness
(24:13) Seth’s book Don’t Trust Your Gut and how it can change your approach to business
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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/
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Links
Seth’s book : Don’t Trust Your Gut: Using Data to Get What You Really Want in Life
Emerging from the Data Dark Ages with Jennifer Belissent, Principal Data Strategist, Snowflake
Data Radicals
01/19/22 • 34 min
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.
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"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
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Time Stamps
(0:00) What’s happening with data literacy today
(3:28) The inevitability of smart cities
(5:34) Data serves as a lens into people’s behavior
(9:05) How to approach data literacy
(17:04) The CDO’s role in data literacy
(22:55) Creating a data culture
(27:44) Teaching your employees data literacy
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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/
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Links
02/05/25 • 48 min
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
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“ 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
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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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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
Order Information Theory, Inference and Learning Algorithms by David MacKay
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FAQ
How many episodes does Data Radicals have?
Data Radicals currently has 67 episodes available.
What topics does Data Radicals cover?
The podcast is about Leadership, Data, Podcasts, Technology, Business and Data Science.
What is the most popular episode on Data Radicals?
The episode title 'Multiple Sources of Truth: Decentralization and the Data Mesh with Zhamak Dehghani, Creator of the Data Mesh' is the most popular.
What is the average episode length on Data Radicals?
The average episode length on Data Radicals is 39 minutes.
How often are episodes of Data Radicals released?
Episodes of Data Radicals are typically released every 14 days.
When was the first episode of Data Radicals?
The first episode of Data Radicals was released on Jan 12, 2022.
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