Data Radicals

What if the future of data management isn’t just about governance—but about growth, speed, and strategic advantage? From reshaping data operations to unlocking new levels of productivity, data products and AI agents are redefining what’s possible in the world of data.In this episode of Data Radicals, Satyen Sangani sits down with Sanjeev Mohan, Principal at SanjMo, to discuss the definition, impact, and lifecycle of data products. They also examine how AI agents are revolutionizing job functions and industries, and practical applications for harnessing this technology’s full potential.Listen to this episode to learn:What data products are and their importance in delivering measurable value, building trust, and improving user experience.The challenges in adopting data products, including the need for a cultural shift within organizations and the potential resistance to change.How generative AI and autonomous agents can revolutionize data management, business processes, and job functions.Discover how forward-thinking data leaders are using these tools not just to manage data—but to build trust, accelerate outcomes, and drive measurable business value.*Satyen’s narration was created using AI--------“ The systems that are running really well, in a lot of organizations, why would they rip out and then go down the path of data products? Because, the problem with data products is it's also a cultural issue. It's a mindshift and you have to think from a completely different long-term point of view. We are so used to – in IT – somebody gives me a problem, I'm like, ‘Yes, I got it. I'll solve it for you.’ Then you move on to the next problem. With data products, it's a mindset shift.” – Sanjeev Mohan--------Time Stamps*(02:28): What is a data product?*(12:30): Why data products demand a mindset shift in IT*(27:19): What is an AI agent?*(36:39): How will data management evolve with the advent of AI?*(47:56): Satyen’s takeaways--------SponsorThis 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/--------LinksConnect with Sanjeev on LinkedInLearn more about SanjMoDownload Sanjeev’s book Data Products for DummiesSubscribe 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.--------“ The most important problem to solve is how close can you get to customers? How close can you get everyone at the company close to the market and close to customers in every interaction? That's never been possible before. Content is one way that we take action. The other way we take action is how do we deliver the content?  If we know who we are trying to reach out to now, we can predict and understand what content is going to be hyper-relevant to that person. Once you have that production process for content, now you can go create the content and distribute it right through your SDR, right to that account.” – Paul Yacoubian--------Time Stamps*(02:18): Why high-quality content needs context*(09:11): Best practices for leveraging LLM tools*(11:42): The path to insight: from data silos to shared context – and better content*(31:00): Which roles will be replaced or augmented by AI?*(38:29): How should CEOs approach AI?*(45:45): David’s takeaways--------SponsorThis 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/--------LinksConnect with Paul on LinkedInLearn 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--------“ For many of these organizations, there really is an onus on people like ourselves to prove ourselves in the organization. I think the biggest data culture challenge is really how do we make ourselves relevant to the day-to-day of the employee? How do we make sure that if somebody is on an oil rig or in a store or in a call center or on a trading floor or in a lab, they are going to do something different because of us? Because if they're not doing something different because of us, then honestly, we don't deserve to be here.” – Ryan den Rooijen“ Traditionally, CDOs were in place to wrangle and collate the data and curate the data to a point that it was perfect. That was the ideal for a long time. I think that's probably an impossible task these days with all the different types of unstructured data around there. But also, is it needed? If we keep pushing for that nth degree, you are never going to achieve it. If you keep pushing for that from a quality point of view and a curation point of view, you forget about why you were there in the first place, which is value. If you don't get to that value point quick enough, it's very hard to explain why you were owed that budget in the first place, where all that cost went.” – Wade Munsie--------Time Stamps*(02:23): Why is the chief data officer in trouble?*(12:00): The CDO as a transformational role*(23:52): Redefining data culture as value-driven*(32:50): Data leaders as systems thinkers*(45:09): How will AI impact data teams?*(55:48): Satyen’s Takeaways--------SponsorThis 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/--------LinksConnect with Ryan on LinkedInConnect with Wade on LinkedInRead Ryan and Wade’s MIT Sloan article The Chief Data Officer Role: What’s NextRead 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--------“ We have this high-level thesis we call Declarative Computing. Which the idea is, let's flip the story, instead of a human having to pick the resources and pick all these configurations. That's really hard. Most people don't know any of that stuff, but what people do understand is the outcome. How long did it take? How much did it cost? What was the latency? These are very understandable. These metrics are tied much more to the business, I would say. Our whole thesis is why can't we flip the story? Why can't you declare the outcomes that you want?” – Jeff Chou--------Time Stamps*(04:10): What is analog computing? How can it minimize energy consumption?*(13:06): What is declarative computing? Scaling compute, minimizing costs*(21:32): Optimizing cloud compute (and the challenges of vendor lock-in)*(31:23): The need for high reliability in production*(39:14): The future of compute: Specialization*(43:00): Satyen’s takeaways--------SponsorThis 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/--------LinksConnect with Jeff on LinkedInLearn 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.What You'll Learn in This Episode:🔹 Agentic AI is still in its infancy – AI agents can act autonomously, but most businesses are only using them for simple, low-stakes tasks. Scaling their use requires overcoming reliability challenges.🔹 AI alone won’t drive value—process redesign is critical – Businesses can’t just add AI to existing workflows and expect results. As Tom puts it, "Economic value requires that we change the way we do our work. And there has to be some intentional design activity. It can't just evolve."🔹 The C-suite is overcrowded—AI leadership must evolve – With CIOs, CDOs, CTOs, and more, organizations often suffer from fragmented leadership. Tom argues for business-driven executives who can oversee AI, data, and digital strategy holistically.AI is transforming industries, but the organizations that truly succeed will be those that rethink their leadership, workflows, and data strategies. Whether you're a CDO, CIO, or data professional, this episode offers actionable insights from one of the most influential thinkers in AI and analytics.*Satyen’s narration was created using AI--------“ There is a generative AI component, but it's not just generative AI. It's probably analytical AI as well, it's probably still APIs, it's probably still transaction systems, ERP and CRM and so on.  There'll have to be a lot of integration, which means that it's going to be a fair amount of work for companies to pull this off. I think vendors will help and they'll provide lots of tools, but I think companies will have to figure out what they want to accomplish with it and make it happen and that will take some time and effort.” – Tom Davenport--------Time Stamps*(02:27): Agentic AI use cases: Is AI the new software?*(10:52): The CDO's role in the age of AI*(20:57): What is AGI? Is it coming soon?*(26:43): How can organizations transform with data?*(35:38): The need to redesign processes for AI*(38:58): Satyen’s takeaways--------SponsorThis 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/--------LinksConnect with Tom on LinkedInRead Tom’s MIT Sloan article Five Trends in AI and Data Science for 2025Read Tom’s Harvard Business Review article How Gen AI and Analytical AI Differ — and When to Use EachOrder Tom’s book All Hands on Tech: The AI-Powered Citizen RevolutionOrder Tom’s book All in on AI: How Smart Companies Win Big with Artificial IntelligenceOrder 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--------“ In our experience, fine-tuning is very useful as an optimization step. But, it's not where we recommend people to start. When people are trying to customize these models, we encourage them as much as possible to push the limits of prompt engineering with the most powerful model they can before they consider fine-tuning. The reason that we suggest that is that it's much faster to change a prompt and see what the impact is. It's often sufficient to customize the models and it's less destructive. If you fine-tune a model and you want to update it later, you kind of have to start from scratch. You have to go back to the base model with your label data set and re fine-tune from the beginning. If you're customizing the model via prompts and you want to make a change, you just go change the text and you can see the difference. There's a much faster iteration cycle and you can get most of the benefit.” – Raza Habib--------Time Stamps*(01:26): Raza’s career journey: From academia to industry*(12:46): What is active learning?*(17:20): How LLMs diverge from traditional software processes*(24:53): What is data leakage?*(35:56): How can software engineers adapt in the age of AI?*(47:04): Satyen’s takeaways--------SponsorThis 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/--------LinksConnect with Raza on LinkedInLearn more about HumanloopListen to Raza’s podcastOrder Information Theory, Inference and Learning Algorithms by David MacKay

There’s a digital revolution happening – and it’s poised to impact data leaders across all industries. During this time of never-ending change, it’s crucial to have data practitioners at the center of holistic AI transformation as regulatory compliance and ethical standards come into the fold.Businesses of every size will encounter these complex regulations. Learn about these challenges and how connecting practitioners across fields can create more compliant and trusted AI environments. This episode is packed with practical guidelines and future-focused strategies designed to empower data leaders with the insights they need to build effective, ethical AI.*Satyen’s narration was created using AI--------“ Each state or each country having their own AI policies or privacy policies, frankly, doesn't make any sense. Because, most people, especially if you're on cloud, you may not even know where your data sits.  There's basic principles and there's basic practices that you can define that are tech agnostic, that you can still have your own tech stack and your own tools. There's lots of solutions and players that work in those components, but you can give basic guidelines to say, here's the steps and the processes and the pieces that you need to put in place. Here's how they form together to create an encapsulation of trust.” – Wendy Turner-Williams--------Time Stamps*(02:20): Enabling the AI community *(13:49): How does The Association.AI put regulatory theory into practice?*(21:01): Why AI practitioners need places to knowledge share*(34:23): The rise of the CIO: Risk talks*(39:46): AI predictions: What will change? What won’t?*(50:30): Satyen’s takeaways--------SponsorThis 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/--------LinksConnect with Wendy on LinkedInLearn more about TheAssociation.AIOrder Unleashing the Power of Data with Trusted AI

Any digital marketing leader will tell you that data and marketing strategies go hand-in-hand. In this episode, Michael Olaye, EVP and Managing Director of Hero Digital, shares his journey and practical strategies for success, drawing from his career path that began with door-to-door job hunting and led to spearheading major digital initiatives.Michael emphasizes the central role of data in digital marketing, from informing internal business decisions to enhancing customer experiences, and discusses the dual focus of AI in driving internal efficiency while offering robust public-facing tools.He highlights the critical interplay between data governance and AI ethics, stressing the importance of businesses being 'AI ready.' By exploring customer journeys and leveraging data for innovation, Michael demonstrates how insights can shape product development and business strategies.As a forward-thinker, he shares his enthusiasm for emerging technologies like learning agents and multimodal models, envisioning a transformative future for business operations. Through candid anecdotes and expert advice, Michael delivers actionable insights on harnessing data and AI to drive innovation and customer satisfaction.--------“Some clients do not know that they're sitting on gold, they do not know that. They have tons of data that they've never done anything with and then they focus on the most simplistic things: media, SEO, social media content, website content. Then you come in and you're like, ‘Hey, we can help your customer service be more efficient by understanding how the data, how long it takes a call to go through. We can help you process products more better by understanding the transaction from seeing something online to going in store, to buying it, to returning it.’ Looking at those data sets and seeing patterns or bringing them together to see journeys, that's where the secret lies.” – Michael Olaye--------Time Stamps*(03:37): How Michael uses data for customer experience*(11:39): AI in marketing today: The role of data*(20:54): The dangers of bad data in AI*(23:30): How do you find high-value data?*(30:17): Understanding data and the brand-loyalty debate*(38:16): David’s takeaways--------SponsorThis 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/--------LinksConnect with Michael on LinkedInLearn more about Leonardo.AILearn more about WaldoLearn more about FireflyLearn more about Google Graveyard

If you’re a history buff in the data world, you know that there’s a complex interplay between data, statecraft, and machine learning. The history of data visualization is entwined with societal governance and technological advancements, starting from the usage of statistics for statecraft in the 18th century to the transformative innovations during World War II that birthed computation and data science as we know it. And because of the subjective design choices that underpin data gathering and analysis, there’s an inherently political nature of deciding what data to collect and how to utilize it, which is critical in understanding both historical and contemporary data practices.As we move into the modern applications of data science and the advent of AI technologies, deep reinforcement learning and the integration with generative AI models, these technologies are reshaping the field by enabling computers to process and interact with unstructured data in unprecedented ways. Satyen and Chris discuss his book How Data Happened, the origins of data science and the role of Alan Turing in the creation of digital computing, and the challenges generative AI brings around model interoperability.*Satyen’s narration was created using AI--------“In the last two years, one of the major techniques for advancing the most eye-popping products has been RLHF, Reinforcement Learning from Human Feedback. There's innumerable subjective design choices happening there, which eventually become encoded in a product. But, the presentation of it as though it's somehow unbiased and free from any subjective design choices is illusory.” – Chris Wiggins--------Time Stamps*(01:36): How did Chris come to write How Data Happened?*(10:33): World War II as the springboard for data science and digital computing*(18:37): The tension between objectivity and subjectivity in data today*(25:36): What is Reinforcement Learning from Human Feedback (RLHF)? *(36:03): How has Gen AI impacted data science?*(44:53): Satyen’s takeaways--------SponsorThis 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/--------LinksConnect with Chris on LinkedInOrder Chris’s book How Data Happened

In a dynamic discussion, Taylor Culver, founder of XenoDATA and expert in data management, shares his insights on effective data leadership. He emphasizes the need to define problem statements clearly for actionable solutions. Engaging stakeholders like a salesperson is key, along with a product management mindset to adapt data strategies. Taylor discusses navigating the duality of data governance, particularly in finance, and the vital role of mentorship and collaboration in the evolving landscape of data leadership.