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Data Radicals

Data Radicals

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.
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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.

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

Connect with Geraldine on LinkedIn

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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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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

Learn more about SB-1047

Learn more about Anthropic’s AI Constitution

Learn more about the FASTER Act

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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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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 Chris on LinkedIn

Order Chris’s book How Data Happened

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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.

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 Stewart on LinkedIn

Learn more about IDC MarketScape

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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

Check out Seth's Work

Seth’s book : Don’t Trust Your Gut: Using Data to Get What You Really Want in Life

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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

Connect with Jennifer on LinkedIn

Check out Snowflake

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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.

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"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

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Time Stamps

(0:00) How Satyen became a generalist

(2:52) Why IQ tests aren’t as helpful as you think

(5:53) What makes an environment kind or wicked

(13:23) Getting comfortable with sporadic success

(18:31) The perks of generalization

(22:57) The shortcomings of specialization

(26:36) What we can learn from Vincent Van Gogh

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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

Connect with David on LinkedIn

Check out David's website

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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.

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“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

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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

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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 Paul on LinkedIn

Check out Paul’s Website

Read Paul’s book The Digital Mindset

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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.

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“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

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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

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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 Jonathan on LinkedIn

Follow Jonathan on Twitter

Read Jonathan’s book Data Governance for Dummies

Visit Jonathan’s website

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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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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 Edo on LinkedIn

Watch Edo’s TED Talk

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FAQ

How many episodes does Data Radicals have?

Data Radicals currently has 63 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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