The future of business intelligence is being rewritten. Have you ever wondered how AI will unlock the power of unstructured data?

In this episode of Data Radicals, host Satyen Sangani is joined by Chris Aberger, newly-minted VP at Alation to discuss building AI-powered data workflows.

A startup pioneer, Chris explores the importance of metadata in enhancing AI applications within organizations, the significance of quick iterations, and the evolving role of AI engineers.

“ That two-step realization is what's causing a lot of this activity that we're seeing in the market, which is, I know I need to plug into databases. I'm now coming to terms with the fact that this is actually a really tough problem to get right.”

Listen to this episode to learn:

• Why metadata curation and feedback loops are crucial for making AI effective
• The necessity of a fast-paced, iterative approach in developing AI solutions
• How to enable end-users to become builders through AI and metadata tools

Listen now: alation.com/podcast/episodes/ai-builders-metadata-chris-aberger

*Satyen’s narration was created using AI

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“People have realized that, okay, like structured data is actually like the hard problem to get right. And all these organizations' really valuable data is inside their databases in the structured formats. We have to figure out how to make this ready for the AI era. And then the kind of second level problem that people are discovering is how do I make this structured data actually work? Oh, it's metadata. And I think that realization that that kind of two-step realization is what's causing a lot of this activity that we're seeing in the market, which is, I know I need to plug into databases. I'm now coming to terms with the fact that this is actually a really tough problem to get right. In order to get it right, I need to effectively go build a data catalog or metadata provider, and therefore we're seeing a lot of activity in this space.” – Chris Aberger

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Time Stamps

*(02:04): From the Stanford AI Lab to founding Numbers Station

*(12:10): From chat with your data to act with your data: From data users to business builders

*(19:23): The value of metadata to production-ready AI

*(28:46): What are precision agentic workflows?

*(35:35): Empowering enterprise data users to build with AI

*(45:50): Satyen’s takeaways

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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

In the luxury world where artistry is key, how is AI enabling personalization, optimization, and speed?

In this episode of Data Radicals, host Satyen Sangani is joined by Julie De Moyer, Chief Data Officer of LVMH Beauty to break down the role of data and AI in business transformation.

A seasoned strategist and leader of innovation across 15 beauty brands, Julie shares practical examples of AI application in various aspects of LVMH's operations, from product development to supply chain management.

“ The AI is often the cherry on the cake. We're moving towards those new technologies that are helping us dream even bigger.”

Listen to this episode to learn:

• The importance of collaboration, change management, and consumer-centric approaches.
• How to work closely with CEOs to drive meaningful data-driven decisions.
• How to balance AI and human creativity within the luxury beauty industry.

Listen now: https://www.alation.com/podcast/episodes/lvmh-data-ai-strategy-julie-de-moyer

*Satyen’s narration was created using AI

**LVMH is vendor-neutral and this does not constitute an endorsement

**All views and opinions expressed by the speakers are their own

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“ If you look at the making of perfumes or the way we actually make the wines, in other industries, we would use the AI in order to help those, I would say, those scientists to go faster, to optimize their trials. It will never replace the final scent or the final product that is decided on, but it can help with the substitutions of products that might need to go out, as a result of regulatory changes. It might also help with making sure that the quality of the products last as long as possible. We really help those researcher scientists do their job better and easier.” – Julie De Moyer

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Time Stamps

*(01:34): Julie’s background: From economics student to technology leader

*(07:55): AI in action: How stakeholders collaborate

*(14:42): The role of data in luxury today (and 5 ways to apply AI in retail)

*(22:09): Leading data in a multi-brand environment

*(28:18): How to become a trusted AI leader: Key tips

*(33:40): Satyen’s takeaways

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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 Julie on LinkedIn

In this special compilation episode, we're bringing together the most insightful conversations from our latest season exploring the rise of AI. You'll hear from thought leaders like Fortune's AI editor Jeremy Kahn on rebuilding the middle class, marketing executive Michael Olaye on creative acceleration, and Tom Davenport on what real AI transformation looks like. Plus insights from numerous other AI experts including CDOs, academics, and tech entrepreneurs who are shaping our AI future today.

We'll explore practical AI use cases, examine how data leaders can leverage these technologies, and discuss how the CDO role is evolving in response to AI's momentum.

Listen to this episode to learn:

• How AI copilots are fundamentally changing the way we work and uplifting workers across industries.
• Why it's important to have data leaders who can translate between technical possibilities and business realities.
• How solid data infrastructure, clear business objectives, and thoughtful human oversight leads to successful AI transformation.

Whether you're a data professional, business leader, or simply curious about AI's impact, this episode offers perspectives from those on the frontlines of the AI revolution.

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“I think this is gonna be a tremendously transformative technology, and I think there's some really big positive effects, particularly I think we are gonna see a huge uplift in labor productivity, and I don't think we're gonna see sort of mass joblessness from this technology. I think actually this is a technology that could enable people to sort of be lifted back up into the middle class.” – Jeremy Kahn

“You can do stuff that would take weeks before in days. You can collaborate with people who have no technical knowledge on technical things. We have tools now that you can code by like verbally speaking natural language. We have tools that you can do design without having any design skills. So, I think it's opened up a whole new site for agencies, consultancies, companies, but it's also opened a whole new site for a new like economy of like content creators. When you build anything with AI, having a human in that loop where we are today, having humans in that loop, checking that also, it's good.” – Michael Olaye

“It's not rocket science. It's having senior people who are interested in analytical, decision making, hiring people who can do the work. The day-to-day work of analytics, both the data management and the data analysis. And ultimately having some sort of unique data that is proprietary to you, that will really differentiate you. Because ultimately data is a fuel of analytics and AI. And if you don't have something distinctive, you're gonna have the same models that everybody else has.” – Tom Davenport

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Timestamps

*(01:18): AI as copilot

*(04:30): AI: It’s still early days

*(06:13): AI Use Cases

*(11:26): How data leaders can leverage AI

*(14:07): Data leadership and systems thinking

*(18:17): The future (and limits) of the CDO

*(29:15): Predictions

*(35:16): Career advice

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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/

* David’s LinkedIn Profile: https://www.linkedin.com/in/davidwchao/

What does it take to turn AI hype into operational value at enterprise scale?

In this episode of Data Radicals, host Satyen Sangani sits down with Todd James, former Chief Data and Technology Officer at 84.51° and Kroger executive, to unpack the realities of leading AI transformation inside one of America’s largest grocery retailers.

Drawing on a career that spans the Coast Guard, consulting, and Fortune 100 leadership, Todd shares how scaling AI isn’t just about building advanced algorithms—it’s about solving real problems, embedding with the business, and building reusable infrastructure that lasts.

“If the people at the consumption end of your sciences aren’t bought in, they don’t get implemented.”

Listen to this episode to learn:

• How effective leaders “delegate to innovate” and create space for team growth
   
• How AI reduced Kroger’s pick-order travel time by 10% and truck route distance by 8.6%
   
• Why the CDO role is likely transitional—and what’s next for data leadership
   
• What it takes to embed data science into the operational core of a business
  
   

This conversation is a must-listen for anyone building AI programs, leading data teams, or navigating digital transformation.

Listen now: https://www.alation.com/podcast/episodes/delegate-innovate-todd-james

*Satyen’s narration was created using AI

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“ I think we should go into every project, every initiative, saying, ‘I own an outcome around bringing people along and convincing them.’ If you're doing that right, you're probably spending more than half of the project or half the initiative on managing those organizational dynamics. Working with people and how they think and how they feel to be able to drive them to an outcome to listen. I think that's more than half the work that needs to happen in the space. We talk about data and analytics and how hard the math is and how cool the outcomes are, but this is transformation. This is about people.” – Todd James

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Time Stamps

*(01:46): Todd’s career journey: From the Coast Guard to Fidelity

*(10:03): Great leaders delegate: Advice for aspiring executives

*(12:53): AI retail use cases: Quicker routes and personalization at scale

*(26:12): Reapplying data-science processes across distinct problems

*(40:21): Is the CDO role transitional?

*(48:27): Satyen’s takeaways

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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 Todd on LinkedIn

Learn more about Aurora Insights

What if the future of data management isn’t just about governance—but about growth, speed, and strategic advantage? From reshaping data operations to unlocking new levels of productivity, data products and AI agents are redefining what’s possible in the world of data.

In this episode of Data Radicals, Satyen Sangani sits down with Sanjeev Mohan, Principal at SanjMo, to discuss the definition, impact, and lifecycle of data products. They also examine how AI agents are revolutionizing job functions and industries, and practical applications for harnessing this technology’s full potential.

Listen to this episode to learn:

• What data products are and their importance in delivering measurable value, building trust, and improving user experience.
• The challenges in adopting data products, including the need for a cultural shift within organizations and the potential resistance to change.
• How generative AI and autonomous agents can revolutionize data management, business processes, and job functions.

Discover how forward-thinking data leaders are using these tools not just to manage data—but to build trust, accelerate outcomes, and drive measurable business value.

*Satyen’s narration was created using AI

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“ The systems that are running really well, in a lot of organizations, why would they rip out and then go down the path of data products? Because, the problem with data products is it's also a cultural issue. It's a mindshift and you have to think from a completely different long-term point of view. We are so used to – in IT – somebody gives me a problem, I'm like, ‘Yes, I got it. I'll solve it for you.’ Then you move on to the next problem. With data products, it's a mindset shift.” – Sanjeev Mohan

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Time Stamps

*(02:28): What is a data product?

*(12:30): Why data products demand a mindset shift in IT

*(27:19): What is an AI agent?

*(36:39): How will data management evolve with the advent of AI?

*(47:56): Satyen’s takeaways

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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 Sanjeev on LinkedIn

Learn more about SanjMo

Download Sanjeev’s book Data Products for Dummies

Subscribe to Sanjeev’s podcast It Depends

Generative AI is reshaping the way go-to-market teams create content, optimize workflows, and drive velocity — but only when it’s powered by the right context and data.

In this episode of Data Radicals, Alation CMO David Chao sits down with Paul Yacoubian, CEO and co-founder of Copy.ai, to explore how large language models (LLMs) are transforming content creation and sales execution at scale. Paul shares lessons from building Copy.ai since 2020 and how his team is helping over 15 million users streamline operations through AI-powered automation.

You’ll learn why content without context falls flat, how siloed systems slow down GTM execution, and why AI isn’t replacing roles — it’s augmenting them to unlock new levels of efficiency and insight.

Listen to this episode to learn:

• How LLMs enable end-to-end automation by taking in data, executing workflows, and generating outputs — without manual bottlenecks.
• Why unifying siloed systems is critical to improving GTM velocity, content relevancy, and business decision-making.
• How AI is transforming — not replacing — roles like SDRs and marketers, and what that means for the future of sales teams.
• What CEOs and business leaders must do to operationalize AI successfully: from standardizing best practices to enabling faster, data-driven decisions.

If you're navigating the challenges of scaling AI in your GTM org — from data sprawl to inefficient workflows — this episode offers practical strategies, fresh perspectives, and a blueprint for AI-powered transformation.

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“ The most important problem to solve is how close can you get to customers? How close can you get everyone at the company close to the market and close to customers in every interaction? That's never been possible before. Content is one way that we take action. The other way we take action is how do we deliver the content?  If we know who we are trying to reach out to now, we can predict and understand what content is going to be hyper-relevant to that person. Once you have that production process for content, now you can go create the content and distribute it right through your SDR, right to that account.” – Paul Yacoubian

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Time Stamps

*(02:18): Why high-quality content needs context

*(09:11): Best practices for leveraging LLM tools

*(11:42): The path to insight: from data silos to shared context – and better content

*(31:00): Which roles will be replaced or augmented by AI?

*(38:29): How should CEOs approach AI?

*(45:45): David’s takeaways

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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

Connect with Paul on LinkedIn

Learn more about Copy.ai

As technology rapidly evolves and businesses focus on getting real results, data jobs are shifting. Many data tasks now fall under the CIO or CTO, data leaders are moving into roles that affect bigger business plans, and more companies are using self-service data tools or seeking a path to AI—making CDO-led teams less necessary. How can data leaders adapt?

In this episode of Data Radicals, Satyen Sangani talks with Ryan den Rooijen, Writer and Consultant at Qstar.ai, and Wade Munsie, Interim Director of Data & AI at Heathrow. With years of experience in data leadership, Ryan and Wade explore how the CDO role is changing, the challenges in data and AI, and why the job isn’t always what people expect.

Listen to this episode to learn:

• The future of data leadership, including how AI is changing the way we use data and why it's important to stay flexible and focused on real business results.
• Why data leaders need to go beyond their usual tasks and help improve the whole business.
• How AI and smart computer systems are shaping data management and what these new technologies could mean for the future of the industry.

From capitalizing on generative AI to redefining the CDO role, this episode offers a wealth of knowledge for anyone looking to understand the real-world challenges and opportunities in the data landscape. Tune in to hear practical advice and visionary thoughts from top data leaders.

*Satyen’s narration was created using AI

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“ For many of these organizations, there really is an onus on people like ourselves to prove ourselves in the organization. I think the biggest data culture challenge is really how do we make ourselves relevant to the day-to-day of the employee? How do we make sure that if somebody is on an oil rig or in a store or in a call center or on a trading floor or in a lab, they are going to do something different because of us? Because if they're not doing something different because of us, then honestly, we don't deserve to be here.” – Ryan den Rooijen

“ Traditionally, CDOs were in place to wrangle and collate the data and curate the data to a point that it was perfect. That was the ideal for a long time. I think that's probably an impossible task these days with all the different types of unstructured data around there. But also, is it needed? If we keep pushing for that nth degree, you are never going to achieve it. If you keep pushing for that from a quality point of view and a curation point of view, you forget about why you were there in the first place, which is value. If you don't get to that value point quick enough, it's very hard to explain why you were owed that budget in the first place, where all that cost went.” – Wade Munsie

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Time Stamps

*(02:23): Why is the chief data officer in trouble?

*(12:00): The CDO as a transformational role

*(23:52): Redefining data culture as value-driven

*(32:50): Data leaders as systems thinkers

*(45:09): How will AI impact data teams?

*(55:48): Satyen’s Takeaways

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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 Ryan on LinkedIn

Connect with Wade on LinkedIn

Read Ryan and Wade’s MIT Sloan article The Chief Data Officer Role: What’s Next

Read Ryan and Wade’s series Chief Data Officers Are In Trouble

Cloud costs are skyrocketing, and for data teams running AI inference, Spark jobs, and big data workloads, optimization is no easy task. Tuning these workloads for efficiency without disrupting production is a major challenge—but what if there was a better way?

In this episode of Data Radicals, Satyen Sangani sits down with Jeff Chou, CEO and co-founder of Sync Computing, to explore a revolutionary approach to cloud optimization. Sync’s closed-loop tuning engine continuously fine-tunes workloads in real-time—without manual adjustments. The result? 50-60% cost savings on Spark jobs and massive efficiency gains for AI workloads.

Listen to his episode to learn:

• Why declarative computing is the future—letting engineers define their desired outcomes instead of manually configuring infrastructure.
• How Sync Computing slashes cloud costs by dynamically adjusting resources in production, ensuring efficiency without sacrificing reliability.
• The game-changing impact of Sync’s partnership with NVIDIA to optimize GPU workloads, where the stakes—and costs—are even higher.

If you’re managing cloud workloads, this conversation is a must-listen. Discover how cutting-edge AI-powered optimization is reshaping efficiency for Databricks, AI inference, Spark, and beyond.

*Satyen is an investor of Sync Computing

*Satyen’s narration was created using AI

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“ We have this high-level thesis we call Declarative Computing. Which the idea is, let's flip the story, instead of a human having to pick the resources and pick all these configurations. That's really hard. Most people don't know any of that stuff, but what people do understand is the outcome. How long did it take? How much did it cost? What was the latency? These are very understandable. These metrics are tied much more to the business, I would say. Our whole thesis is why can't we flip the story? Why can't you declare the outcomes that you want?” – Jeff Chou

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Time Stamps

*(04:10): What is analog computing? How can it minimize energy consumption?

*(13:06): What is declarative computing? Scaling compute, minimizing costs

*(21:32): Optimizing cloud compute (and the challenges of vendor lock-in)

*(31:23): The need for high reliability in production

*(39:14): The future of compute: Specialization

*(43:00): Satyen’s takeaways

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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 Jeff on LinkedIn

Learn more about Sync Computing

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.

🔹 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

Connect with Tom on LinkedIn

Read Tom’s MIT Sloan article Five Trends in AI and Data Science for 2025

Read Tom’s Harvard Business Review article How Gen AI and Analytical AI Differ — and When to Use Each

Order Tom’s book All Hands on Tech: The AI-Powered Citizen Revolution

Order Tom’s book All in on AI: How Smart Companies Win Big with Artificial Intelligence

Order Tom’s book Competing on Analytics: The New Science of Winning

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

Connect with Raza on LinkedIn

Learn more about Humanloop

Listen to Raza’s podcast

Order Information Theory, Inference and Learning Algorithms by David MacKay