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Everything Under the Model: What 7 Leaders Learned the Hard Way About Making AI Work
4 november 2026 · 35 min
The hard part of AI isn't the model — it's everything underneath it and around it. Lees meer
In this season 4 roundup episode of AI Radicals, host Satyen Sangani revisits seven conversations from the season to distill what it actually takes to make AI work inside a real company. The guests span the field: Charlene Li, co-author of Winning with AI; Brian Solis and Dave Wright, ServiceNow's innovation leaders; Erin McIntosh, who runs global data operations at CNA Insurance; Nathalie Berdat, data leader at the BBC; Anant Jhingran, CTO of IBM Software; Eugene Wu, Columbia professor and co-director of the Data Agents and Processes Lab; Francois Ajenstat, Founder and CEO of Golden Analytics; Mark Nelson, Venture Partner at Madrona; Julie De Moyer, who leads data and AI across fifteen beauty brands at LVMH; and Ben Taylor, Former Chief AI Strategist of Dataiku, who predicted most of this three years ago.
Across the season, three things held. First, stop calling it a pilot — if it isn't strategic enough to go into production, it isn't strategic enough to do. Second, trust is earned one reconciled metric at a time, starting not with a problem but with a single number that two teams disagreed about. Third, the rules belong in the system, not in the prompt — a stop sign goes at the intersection, not in the head of the user. And underneath all three, something none of these leaders set out to say: the work is coupled, permeable, and never quite finished. Closer to biology than physics.
"The risk of doing something is better than not doing anything in this space. And I really hate saying that, but that's really where we're at."
Listen to this episode to learn:
Why executives green-light AI pilots precisely because they haven't decided anything yet — and what changes when you frame AI as a strategic initiative instead
How IKEA turned a customer service agent into a billion-euro interior design business by retraining 8,200 people instead of cutting them
Where governance actually bel…
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Open Source, Open Question: Why Model Supremacy Won't Last with Ashu Garg, General Partner at Foundation Capital
21 oktober 2026 · 49 min
Why the most valuable asset in AI won't be the model — it'll be the layer that captures how your business actually makes decisions. Lees meer
In this episode of AI Radicals, host Satyen Sangani sits down with Ashu Garg, General Partner at Foundation Capital and an early investor in one decacorn and six unicorns, to unpack where enduring value in AI will actually accrue. Four years ago, Ashu was non-consensus bullish on open source AI models. Today that view is consensus — and he's moved on to the next thesis: context graphs.
Satyen and Ashu dig into the concept of context graphs — an infrastructure layer that captures the decision traces humans generate as they interact with AI applications, scattered across Slack, email, Salesforce, and a dozen other interfaces. They explore why the gap between open source and closed source models is shrinking to roughly six months, why 75% of tasks no longer need a better model, and what that means for the economics of foundation model providers.
"The best AI applications will really need to capture decision traces over time and then figure out how to use those decision traces to improve the decisions they make in the future. That infrastructure layer is the context graph. And our belief is that the most valuable asset over time will be the context graph."
Listen to this episode to learn:
What a context graph is and why it can only be built in the context of a real application — not as a theoretical enterprise-wide exercise
Why the six-month gap between open and closed source models means application developers must have open source in the mix, and why two-thirds of enterprise value will continue to be captured at the application layer
When to fix problems in the model versus the context layer, and which incumbent application companies Ashu thinks are positioned to survive the AI transition — and which aren't
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“ Advancing your automation journey, it's a little bit like the levels of self-driving. As companies go through th…
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The Control Plane Race: Who Governs the Agents with Victor Dey, AI Industry Analyst
14 oktober 2026 · 49 min
Why the loudest narratives in AI rarely hold up once you talk to the people actually building the technology. Lees meer
In this episode of AI Radicals, host Satyen Sangani talks with Victor Dey, AI and technology analyst contributing to Fast Company, Forbes, and the AI Journal, about what it takes to cut through the noise of enterprise AI — as a journalist who has interviewed the executives running nearly every major data and agent platform in real time.
Victor and Satyen dig into how Victor's practitioner background in data science and NLP shapes the questions he asks CEOs, and why a practitioner's mindset increasingly matters for anyone selling, deploying, or building in AI. They unpack the story behind Victor's counter-narrative on the so-called "SaaS Apocalypse" — how one forthright conversation flipped the market narrative and sparked a wave of CEOs finally speaking up. They explore the race to own the AI "control plane," why competitors are simultaneously building and partnering on orchestration, and why context alone isn't enough: permissions, guardrails, and observability matter just as much.
"What I think is more important than context, or equally important as context, is also permissions. What data did it touch? What points did it touch? Was it denied or was it told not to perform certain actions? I think that needs equal amount of light."
Listen to this episode to learn:
Why the SaaS Apocalypse narrative collapsed once a single CEO with conviction and customer evidence spoke up — and what that reveals about how market stories form and break
Why the AI control plane is simultaneously the most contested and most collaborative space in enterprise tech, with competitors building rival products while partnering on the same capabilities
Why context gets the headlines but permissions, guardrails, and observability deserve equal attention as agents proliferate and incidents mount
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“ With the rise of agents, I think this control plane about whether it be g…
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Metadata to Knowledge Graphs: Mapping the Full AI Data Stack, with Sanjeev Mohan, Principal of SanjMo
7 oktober 2026 · 45 min
Data foundations are the real bottleneck for agentic AI, and most teams are governing the wrong layer. Lees meer
In this episode of AI Radicals, host Satyen Sangani talks with Sanjeev Mohan, industry analyst and author of Designing the AI-Driven Data Foundations, about why AI's biggest promises still rest on unglamorous data fundamentals, and why agents are breaking the old rules of governance.
Sanjeev walks through his layered model of metadata, semantics, context, ontology, and knowledge graphs, and the two dig into where data products fit in that stack. They debate whether governance belongs to the agent or the data itself, why deterministic rules must live with the data rather than be re-explained to every model or prompt, and how agentic systems generate entirely new categories of data that nobody has a framework for governing yet.
"There's a concept 100% related to the whole idea of context and data products, and that is governance. Now we are in the realm of AI, which is a very different beast to govern."
Listen to this episode to learn:
Why semantics, not storage or compute, is becoming the real moat in the AI stack
Why agentic governance has to cover input, output, and runtime, not just clean data in and clean data out
Why data teams are entering a golden era, shifting from backup-and-restore work to business strategy and reusable data products
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“ One definition is that data product is a philosophy. It's not a thing. And what data product says is that when you build your artifact, you build it with the product management best practices. For example, there's a data product owner, it has a version number, and it's backward compatible. It's packaged with SLAs and contracts. So if you are packaging your semantic layer and you're building it using the product management best practices, then that's a data product, very much so.” – Sanjeev Mohan
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Time Stamps
[01:37] Sanjeev’s book Designing the AI-Driven Data Foundations
[03:47] Defining metada…
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Data Products, Not Platforms: A CDO's Playbook for the Agentic Era with Cara Tice, CDO of Early Warning
30 september 2026 · 46 min
Why "America's data steward" is more than a job title, and what it takes to keep fraud intelligence trustworthy in the age of AI agents. Lees meer
In this episode of AI Radicals, guest host Susan Wilson, Sales Leader at Alation, talks with Cara Tice, Chief Data Officer at Early Warning (the company behind Zelle), about what data stewardship really means when trillions of dollars in real-time payments, and the fraud intelligence protecting them, run on your data.
Cara and Susan dig into why data products have replaced the "one data platform to rule them all" mentality, why integration and change management matter more than any single tool, and why AI doesn't create data problems, it just exposes the ones that were already there.
"Your AI governance is reliant on your data management program. Make sure you've got data governance up and running, because AI is going to rely on your data management controls and the context you create."
Listen to this episode to learn:
Why "data products" — not centralized platforms — are the fastest path to trustworthy, reusable data
Why AI governance is really just an extension of data governance, and why the two leaders need to be joined at the hip
What a new CDO should fix (and what to leave alone) in their first 90 days on the job
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“As a CDO or as a data steward, you need to be obsessed with the customer outcome. Don't lose sight of that. It's easy to lose sight of it when you are focused on plumbing and fixing this issue and that issue. You always have to remind yourself of what's the bigger picture, what's the north star? With data products, you become relentless about the customer outcome because you're delivering to a specific business need, you're creating more capability that is business centric around data. When I think about the features that you would need for data science models, it's the same. Whether your consumer is a data scientist or is a commercial consumer, that data is used to make a decision. So you need to…
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Semantic Coupling & the Interconnected AI Stack with Eugene Wu, Professor at Columbia University
23 september 2026 · 55 min
Why AI agents fail at scale, and the systems-thinking fix nobody's building yet. Lees meer
In this episode of AI Radicals, host Satyen Sangani talks with Eugene Wu, Columbia professor and co-founder of the Data Agents and Processes (DAPLab), about why building reliable AI agents is fundamentally a systems problem, not a model problem.
Eugene explains "semantic coupling," the idea that every layer of an agent's stack, from data retrieval to tool calls to reasoning, is interdependent, so a small failure anywhere can quietly corrupt the final output. He and Satyen draw a parallel to early relational databases absorbing the complexity that applications used to handle themselves, arguing that today's computing infrastructure needs to do the same for agents: managing data flows, enforcing rules deterministically instead of hoping a prompt is followed, and giving agents safe room to explore and fail without real-world consequences. Eugene shares research from his lab on search at massive scale, why even top models struggle to find the right evidence in huge datasets, and a "branchable" computing environment that lets agents try, fail, and roll back cheaply.
"You need somewhere to ground reliability and quality. If it's not probabilistic, and you can rely on it and it's guaranteed, then the system doesn't need to think about it at all."
Listen to this episode to learn:
Why "semantic coupling" makes traditional layered software thinking break down for AI agents
Why pushing rules into the system (not the prompt) turns probabilistic behavior into guaranteed safety
Why compute-level innovations like branchable environments could redefine what we expect agents to do on our behalf
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“ So many people in the data community and companies are working on data search, but how do you evaluate the end-to-end quality? And how much search is even the critical bottleneck in this kind of end-to-end question? Because what does the agent need to do? It needs to take your question, it ne…
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The 90-Day AI Roadmap with Charlene Li, Author
16 september 2026 · 42 min
Why AI transformations fail and the 90-day plan that actually works. Lees meer
In this episode of AI Radicals, host Satyen Sangani sits down with Charlene Li, bestselling author and strategist, to unpack her new book on how organizations can create real value with AI, not just deploy it.
Charlene explains why the biggest mistake leaders make is treating AI as a separate strategy instead of a tool that serves their existing business strategy, and lays out the 90-day framework she and co-author Katia Welch built to help executives move from confusion to a clear AI roadmap. They dig into why "Goldilocks governance" beats both reckless and overly restrictive AI policies, why ownership of AI shouldn't default to IT, and why the obsession with pilots is really a symptom of leaders abdicating strategic responsibility. Charlene also shares real examples from a call center that grew headcount instead of cutting it, to a bank that reskilled instead of laying off, showing what an abundance mindset toward AI looks like in practice.
"Good governance doesn't slow you down. It actually speeds you up, because you know what you are able to do and what you shouldn't be doing."
Listen to this episode to learn:
Why AI should never be treated as its own strategy and how to anchor every AI decision in existing business goals
Why "moving from pilot to production" is really a leadership gap, not a technology gap
Why an abundance mindset is what separates organizations that win with AI from those that stall out
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“ They're green-lighting these pilots because they feel they need to be doing something, but they won't green-light into production because they don't have an overarching AI roadmap that supports their strategy. They haven't put the time and attention to it. They've given it to IT. They have not really thought about what are we trying to do with this? The business leaders have abdicated their responsibility for AI because they themselves don't understand it, and they're not …
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The Case for Federation in the Age of Agents with Anant Jhingran, CTO of IBM Software
9 september 2026 · 53 min
Why the same old data infrastructure playbook won't survive contact with agentic AI, and what actually has to change underneath. Lees meer
In this episode of AI Radicals, host Satyen Sangani talks with IBM Software CTO Anant Jhingran about why enterprise AI's biggest bottleneck isn't the models, it's the decades-old data and integration infrastructure sitting underneath them.
Anant and Satyen dig into why data fundamentals (provenance, metadata, systems of record) haven't changed, even as humans and fixed workflows give way to agents reasoning on the fly. They contrast "AI for data" with "data for AI," why AI's tolerance for messiness still doesn't excuse bad data, and why centralization may matter less as agents run quick, discovery-driven queries instead of big fixed reports. Anant also shares a new focus at IBM: rethinking whether one "golden" code path per product still makes sense when AI makes forking and personalizing variants easy.
"If you think that agents are just going to do the same thing that you're doing except machines instead of people, it's kind of boring, and I don't think that's going to happen. The real change is they're doing something different that we haven’t done before."
Listen to this episode to learn:
Why "AI for data" and "data for AI" are two distinct problems enterprises need to solve separately
Why agentic, discovery-driven workloads may reduce the need to centralize all your data, but raise the stakes on metadata
Why forking products into many tailored variants, instead of one shared code path, could reshape how software gets built
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“ Something that I wouldn't have thought of three months back or six months back, which is how do we actually build products. And the reason is very simple, is that if you just say that AI is going to help us build products faster, then it doesn't actually create a competitive differentiation because everybody else is creating products faster with AI. So you have to both do things differently and p…
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Infinite: Why AI Business Reinvention Beats Automation with ServiceNow’s Brian Solis & Dave Wright
2 september 2026 · 51 min
Why mode one thinking keeps most companies stuck—and what it takes to build a company that can keep reinventing itself with AI. Lees meer
In this episode of AI Radicals, host Satyen Sangani talks with ServiceNow’s Brian Solis and Dave Wright and authors of Infinite, about why so many enterprises get stuck chasing ROI on isolated AI use cases instead of using AI to become something genuinely new.
Brian and Dave unpack their "mode one, mode two" framework: deciding what existing work deserves to scale with AI (mode one) versus using AI to unlock entirely new value the business couldn't create before (mode two). Using stories like Ford's costly rehiring of quality engineers after over-automating, and IKEA's Billie bot freeing thousands of agents to launch a billion-euro design business, they explain how the real ROI conversation starts with strategy, not use cases. They also dig into why most companies are still stuck optimizing yesterday's workflows, why trust and psychological safety are prerequisites for innovation, and why AI governance has to evolve from a checkbox exercise into managing AI as a true enterprise asset.
"AI is not the strategy. If it does become the strategy, it very much limits the impact it's going to have on the organization."
Listen to this episode to learn:
Why leading with use cases limits AI's impact, and how IKEA turned 8,200 agents into a billion-euro business
Why most companies stay stuck optimizing yesterday's workflows instead of reinventing them
Why governing AI as an asset is key as agentic AI scales
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“ You'll see a common set of challenges, like, for example, what's the ROI of AI? That seems to be a popular conversation that has all kinds of different schools of thought around it. AI is not the strategy. If it does become the strategy, it very much limits the impact it's going to have on the organization and how you can measure its success. Where we have the more successful ROI conversations is if we take a step back and look …
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AI Governance in Public Media with Nathalie Berdat, Data Director of Product at the BBC
26 augustus 2026 · 46 min
How data trust breaks—and how to rebuild it before AI makes it worse. Lees meer
In this episode of AI Radicals, host Satyen Sangani sits down with Nathalie Berdat, Data Director of Product at the BBC, to explore how one of the world's most trusted media institutions is rebuilding its data foundations for the AI era.
Nathalie shares how she diagnosed a quiet trust crisis inside the BBC—teams producing conflicting numbers for the same metrics—and led a multi-year effort to fix it: identifying the handful of metrics that actually mattered, building certified "data products" as single sources of truth, and modernizing a legacy platform to support them at scale. She also unpacks why AI governance at a public institution carries different stakes than at a commercial company, how the BBC decides where genAI is (and isn't) allowed to touch editorial content, and what has to be true before agentic AI can responsibly run across an organization like the BBC.
"The governance isn't a compliance checkbox, it's closer to editorial standards. It has to be defensible to a journalist."
Listen to this episode to learn:
Why low trust in data often shows up as two teams presenting two different numbers for the same metric and how to fix it
Why the BBC treats AI governance as an editorial issue, especially when it comes to recommendations and content curation
Why agentic AI requires clear data ownership, documented lineage, and machine-readable governance before it can be deployed responsibly
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“ Building a data product that gives you a very trusted source of truth when it comes to who works and where and what cost center allows you to then expose this product and build on top something like return on investment for our content or program, because then you'll know who has worked, how much it cost us to build and develop a program. You need to know your return on investment for something you'll be commissioning. You'll be investing a lot of effort and time and people on it.” – Nath…
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Is Business Intelligence Truly Dead? Insights from Francois Ajenstat, Founder & CEO of Golden Analytics
19 augustus 2026 · 50 min
Analytics tools are getting a total rewrite for the AI era. What does it actually take to build a "Cursor for data"? Lees meer
In this episode of AI Radicals, host Satyen Sangani is joined by Francois Ajenstat, founder and CEO of Golden Analytics, to discuss how AI is reshaping data analysis workflows.
A three-decade veteran of the analytics space — from Cognos to Microsoft to a decade as Chief Product Officer at Tableau — Francois explores why context and metadata still matter more than ever, and why the next generation of data tools needs to be built with a "slider of autonomy."
"What we generate is we know how data is being used for different use cases and how people traversed our tool to get to that answer... every step that somebody does in Golden is essentially recorded in a time machine."
Listen to this episode to learn:
Why visualization was never the hard part of BI — and what actually is
How Golden built a per-user pricing model to align incentives with customers
Why context and metadata need to be built through the job itself, not managed as an end unto itself
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“As you go through the journey, not every model is great at every part of the analytical flow. Do you use Sonnet for everything or Opus or Fable? When is it appropriate to use different things? There's a factor of cost, there's a factor of latency, accuracy. All those things have to be really considered as you come through it, and how do you make this work also when you've never seen the data in the first hand?” – Francois Ajenstat
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Time Stamps
*(03:12): From Cognos to Microsoft to Tableau: building the BI industry
*(08:32): Is BI dead? Why visualization was never the hard part
*(12:21): Building Golden: two-click dashboards and a constellation of LLMs
*(19:19): Why data isn't software: the unique challenges of AI + data
*(31:41): The blurring boundaries between metadata, context, and BI
*(48:05): Satyen’s takeaways
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Sponsor
This podcast is presented by Alation.
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Rewriting the Governance Playbook for the Agentic Era with Erin McIntosh, VP of Global Data Operations at CNA Insurance
12 augustus 2026 · 38 min
Data quality problems don't just create bad reports; they create mistrust. And once trust is gone, people stop using your systems and start building their own workarounds. Lees meer
In this episode of AI Radicals, host Satyen Sangani sits down with Erin McIntosh, Vice President of Global Data Operations at CNA Insurance, to talk about what it actually takes to modernize data governance at a global commercial insurer in the age of agentic AI. Erin shares how CNA is rethinking decades-old governance playbooks, why "build vs. buy" decisions have been upended by new AI tooling, and how her team is shifting from automating decisions to actually improving them.
Erin also opens up about the hardest part of leading transformation at speed: getting an organization to trust new systems, rebuild processes from the outcome backward instead of the process forward, and move from slow, bureaucratic governance to agentically-led governance that can actually scale.
"Good data governance is actually effective. Bad data governance is actually slow and burdensome."
Listen to this episode to learn:
Why the shift from automating decisions to improving decisions is where real AI ROI comes from
Why agentic governance—not more process—is the path to finally scaling stewardship, compliance, and data quality
Why seeking perfection instead of progress is the biggest waste of time and money in AI deployments today
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“ Each person had to learn which version that they wanted to trust and which one they wanted to use based on their own experience. That became the system of finding the right pieces of information that helped their story. That's really when it clicked for me that this isn't just a data problem, and it wasn't just a reporting problem, and it wasn't just a technology problem. It was a trust problem. Once trust is gone, people don't stop working. They really just build their own version of reality.” – Erin McIntosh
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Time Stamps
*(04:00): A year of rapid transformation…
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Why Enterprise AI Is Entering Its ROI Era with Mark Nelson, Venture Partner at Madrona
29 juli 2026 · 56 min
AI can write code faster than ever. But what if code is no longer the hard part? Lees meer
In the premiere episode of AI Radicals, host Satyen Sangani is joined by Mark Nelson, Venture Partner at Madrona and former CEO of Tableau, to explore what AI is actually changing—and what remains fundamentally the same about building great software and great businesses.
Having led companies through the rise of databases, cloud computing, SaaS, and self-service analytics, Mark offers a rare perspective on today's AI boom. He explains why judgment and customer understanding are becoming the new competitive advantage, why enterprise buyers are shifting from AI experimentation to demanding measurable ROI, and why today's token-based pricing models may be rewarding the wrong behavior.
"Code is easy to generate. Great software isn't. The bottleneck has shifted to understanding what to build."
Listen to this episode to learn:
Why generating code is no longer the bottleneck – but building great software still is
Why enterprise AI is entering an ROI-driven phase where customers expect measurable business value
Why the next generation of AI companies will win by understanding customers, not just building better models
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“ We all come with towering strengths and our own weaknesses. Not just being a product person, not just being an engineer, not just being a salesperson, all of those skill sets. One thing I'll always say about any founder that is true is like, Do you understand your customer? Do you understand what you're solving and why? Do you really kind of first personally feel that pain? Understanding who they're building for and what problem they're solving for.” – Mark Nelson
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Time Stamps
*(02:21): Why AI is different from every technology wave before it
*(07:48): AI won't replace judgment—and that's what matters most
*(12:17): What venture investors are really looking for in AI founders
*(20:27): AI makes code cheap—but great software is still hard to build
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AI Radicals: Season 4 Trailer
22 juli 2026 · 2 min
AI Radicals is back for Season 4 — and trust in AI has never been more contested. Lees meer
This season, host Satyen Sangani, CEO and co-founder of Alation, sits down with leaders, builders, and operators working at the edge of AI transformation to ask the question everyone's dancing around: can we actually trust the systems we're building? New conversations dig into data quality, governance, and the feedback loops that separate AI that works from AI that just demos well.
If you care about making AI matter inside your company, your team, or your own career — Season 4 starts July 29, 2026.
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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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Why AI Builders Need a Metadata Goldmine with Chris Aberger, VP at Alation
18 juni 2025 · 47 min
The future of business intelligence is being rewritten. Have you ever wondered how AI will unlock the power of unstructured data? Lees meer
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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T…
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Perfume, Power, Prediction: Inside a Luxury Giant's Data and AI Strategy with Julie De Moyer, Chief Data Officer of LVMH Beauty
28 mei 2025 · 35 min
In the luxury world where artistry is key, how is AI enabling personalization, optimization, and speed? Lees meer
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 r…
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The Rise of AI: Voices from the Frontlines
14 mei 2025 · 39 min
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. Lees meer
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, consulta…
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Delegate to Innovate: How Letting Go Makes You a Better Leader with Todd James, Founder & CEO of Aurora Insights
30 april 2025 · 50 min
What does it take to turn AI hype into operational value at enterprise scale? Lees meer
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 caree…
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Data Products for Dummies with Sanjeev Mohan, Principal at SanjMo
16 april 2025 · 49 min
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. Lees meer
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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Sponso…
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GTM is a Data Management Problem — How AI (& Better Data) Can Fix It with Copy.ai’s CEO, Paul Yacoubian
2 april 2025 · 47 min
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. Lees meer
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 …
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From Back Office to Boardroom: The CDO's AI Opportunity with Ryan den Rooijen & Wade Munsie
19 maart 2025 · 57 min
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? Lees meer
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 he…
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Declarative Computing in an AI World with Jeff Chou, Co-founder & CEO at Sync Computing
5 maart 2025 · 44 min
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? Lees meer
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? …
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Redesigning Processes for the Age of AI Agents with Tom Davenport
19 februari 2025 · 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. Lees meer
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 o…
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LLMs Decoded: A Starter's Guide to AI with Raza Habib, co-founder & CEO of Humanloop
5 februari 2025 · 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. Lees meer
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 …
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Empowering AI Practitioners with Wendy Turner-Williams, CEO of TheAssociation.AI
22 januari 2025 · 52 min
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. Lees meer
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
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“ 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
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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
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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
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Using AI to Revolutionize CX with Michael Olaye, EVP & Managing Director at Hero Digital
8 januari 2025 · 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. Lees meer
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 cus…
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From Statecraft to Codebreaking: The Big Data Origin Story with Chris Wiggins, Chief Data Scientist at The New York Times
18 december 2024 · 46 min
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. Lees meer
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 impacte…
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The Art of Data Leadership: Lessons from Taylor Culver
4 december 2024 · 47 min
What does it take for data leaders to deliver real business value? In this episode, Taylor Culver, founder of XenoDATA, shares practical strategies for success, including: Lees meer
Focus on the right problems: Taylor explains the importance of refining problem statements for actionable, data-driven solutions.
Engage like a salesperson: Actively listening to stakeholders and identifying pain points is key to building impactful use cases.
Adopt a product management mindset: Taylor emphasizes weaving governance and architecture into customer-centric data strategies.
While the path of the data leader is fraught with obstacles, success is possible. Taylor offers time-tested strategies to help data and business leaders alike make a measurable impact.
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“What data leaders should just own is the path to me is probably going to be fraught with failure, but I need to be able to pivot and I need to be agile. I can very much serve myself by adhering to a common set of principles, which I'm going to practice consistently and continually adapt and adjust in the way I engage with my stakeholders and identify their problems and lean in or lean out on data management techniques or delivering certain solutions. It comes down to intent. Do you genuinely want to help people in your business solve problems with data? Do you genuinely want to grow? Do you genuinely recognize that there's not a magic bullet to doing this? Those are the data leaders who will be successful despite facing adversity.” – Taylor Culver
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Time Stamps
*(07:01): The data-business people problem
*(17:50): How data leaders can tackle business problems in 3 steps
*(26:22): Is data a strategic function or an enablement function?
*(33:50): Strategy: Data offense vs. data defense
*(40:45): Data is a people business: the value of trust
*(46:20): Our takeaways
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Sponsor
This podcast is presented by Alation.
Learn more:
* Subscribe to the newsletter: https://www.alation.com/podcast/
* Alati…
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Data Intelligence in Flux: The Impact of AI with Stewart Bond, VP at IDC
13 november 2024 · 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. Lees meer
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 Profi…
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AI and the Workforce: Copilots or Competitors? with Jeremy Kahn, AI Editor at Fortune
30 oktober 2024 · 48 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. Lees meer
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 som…
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Gen AI at Work: Inside the Digital Bank Revolution with Dr. Geraldine Wong, CDO at GXS Bank
23 oktober 2024 · 42 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. Lees meer
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…
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Season 3 Trailer
16 oktober 2024 · 2 min
The Data Radicals podcast returns on October 23rd! Lees meer
This season, we're exploring how trusted data drives value, whether that’s building game-changing AI or transforming data into dollars – and business results. You’ll hear from data leaders, journalists, and AI trailblazers like Dr. Geraldine Wong, CDO of GXS Bank, New York Times Chief Data Scientist, Chris Wiggins, and Jeremy Kahn, AI Editor at Fortune Magazine. And so many more! Get ready to discover groundbreaking strategies from some of the most innovative minds in data today.
Welcome to season three of Data Radicals! Powered by the team here at Alation.
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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/
Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
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The AI Echo of Saul Alinsky's Legacy
6 maart 2024 · 29 min
Saul Alinsky's book Rules for Radicals held significant lessons for grassroots movements and political activism when it was published in 1971. It also inspired the title and theme of this podcast. In his book, Saul outlines approaches for unifying people and motivating them to work toward a common goal. Today, these same strategies can be used for data culture change management, triggering transformative action within organizations. Lees meer
Saul was an American activist and political theorist who lived from 1909 to 1972. His work organized impoverished communities and gave them tools to drive social change, and won him national attention and notability. In the final episode of the season, Satyen sits down with a ChatGPT-infused version of Saul to discuss the role of data in driving social change, the application of community organization tactics in corporate settings, and the key principles from Rules for Radicals.
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“On the global stage, data analytics and humanitarian efforts is akin to having a crystal ball. It's not about predicting the future, but about making informed, timely decisions that can save lives. The possibilities are indeed limitless. With data and analytics, we're not just changing the game. We're rewriting the rules and designing a better playbook for humanity.” – Saul GP Talinsky
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Episode Timestamps:
*(03:57): Bridging community organizing with data science
*(12:46): Empowering teams: Upskilling organizational agility
*(14:35): Democratizing data and crafting a culture of insight and empowerment
*(17:17): The timeless role of data in driving social change
*(20:54): How to unlock societal transformation with data and analytics
*(26:26): The evolution of radical change
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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/…
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Vector Databases 101 with Edo Liberty, CEO & Founder of Pinecone
21 februari 2024 · 42 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. Lees meer
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 integratio…
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Meshy Data Orgs: Data Teams in a Product-Thinking World with Sanjeevan Bala, Group Chief Data & AI Officer at ITV
7 februari 2024 · 46 min
Folks in the data space are familiar with the concept of data literacy. However, a new idea is on the rise: business literacy. Whether folks sit in product, marketing, or commercial, there needs to be a productive balance between understanding business context and technical expertise of each department. This shared comprehension means ideas are more likely to be deployed and productionalized because everyone has deeper domain knowledge and business understanding. Lees meer
Sanjeevan Bala is making business literacy a top priority at his organization. He is the Group Chief Data and AI Officer at ITV, an Alation customer. There, he is responsible for driving the digital data and AI transformation and leading an offensive growth strategy that enhances how they produce, promote, distribute, and monetize content. Sanjeevan is an international thought leader, has won numerous awards for his work, and was named the most influential person in data by DataIQ. Satyen and Sanjeevan discuss the idea of a Data Product Manager, the importance of business literacy, and the power of experimentation.
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“I think because we went down the data as a product notion, that leadership role was a Data Product Manager. Incorporate product thinking in the way in which data is developed, designed, and used. I think what's beautiful about product thinking is it's very well adapted and equipped for understanding competing objectives and competing needs. Creating methods by which you're trying to either align or prioritize those needs. But, critically allows you to prioritize around the right things because you're constantly looking at how do you make sure you can productionize and scale and realize the full value? What does it take to do that? That goes way beyond what you're doing in data. That gets into organizational change, that gets into last mile technologies that you may not have thought about.” – Sanjeevan Bala
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Time Stamps
*(05:16): How to define your organizational identity
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The Impact of Analytics in a Zero-Sum Game with Ari Kaplan, Head of Evangelism at Databricks
24 januari 2024 · 44 min
Whether you work in retail, healthcare, or CPG, data analytics is key to making your business stand out. You’re able to find new sources of data, synthesize them, and then work with business folks to get better and better insights. Even with all of the advantages analytics offers us, sometimes there’s hesitancy to invest in data. In sports, it’s the exact opposite. The use of data is felt immediately in game wins, player selection, and gate revenue. Lees meer
Known as “The Real Moneyball guy,” Ari Kaplan has revolutionized sports through analytics and is a leading influencer in the area, as well as in AI and data. He helped create analytics departments for the Chicago Cubs, Los Angeles Dodgers, and Baltimore Orioles. Ari is now Head of Evangelism at Databricks where his team helped the Texas Rangers clinch their first World Series title. Satyen and Ari discuss data analytics in sports, how data intelligence platforms are shifting the landscape, and the concept of generation AI.
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“Even if we change nothing else, to be able to make better predictions of player development, finding what skills are better in the draft, predicting injuries and so on, that's part of the competitive advantages. How can we ingest this data? It's a ton of data. Terabytes of data every game, multiply that by dozens and dozens of teams at all levels around the world. Right now, teams are struggling to store it, process it on a daily basis. Teams that could do that faster will be an advantage. For listeners, if you're not in the baseball world, same idea. If you're in retail, CPG, healthcare, it's finding new sources of data, proprietary, nonproprietary. How could you synthesize it? Then, how can you start working with the business people to get better and better and better insights?” – Ari Kaplan
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Time Stamps:
*(03:00): The birth of Moneyball
*(10:11): How the Texas Rangers hit a data home run
*(15:49): The next evolution: data intelligence
*(27:17): Partnering for success in t…
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Beyond Frictionless Living with Nate Anderson, Deputy Editor at Ars Technica
10 januari 2024 · 40 min
When it comes to our relationship with technology, be like philosopher Friedrich Nietzsche and practice mindfulness. We usually think mindfulness means setting boundaries like screen time limits. However, we should think about the goals and values we want from technology, like greater human connection, improving efficiency, or driving knowledge. This introspective thinking enables us to be intentional about how and why we’re using technology. Without mindfulness, instead of you driving the tech, the tech may be driving you. Lees meer
Nate Anderson lives by and continues to share Nietzsche’s philosophies today. Nate is the Deputy Editor at Ars Technica, where he covers technology law, politics, and culture. He combined his high-tech background with a love of writing to freelance at publications like The Economist and Foreign Policy. Nate is also the author of In Emergency, Break Glass: What Nietzsche Can Teach Us About Joyful Living in a Tech-Saturated World. Satyen and Nate discuss forming positive connections with technology, saying “yes” to life, and what Nietzsche would have to say about tech.
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“Connection to other people is important. We use technology to create that connection. That might mean a Friday night game group over Zoom or Twitch or multiplayer with your friends. As long as you have the goal in mind, that's where it requires your creativity. That's where you're using the tools creatively to produce outcomes that you want in life. The problem with not thinking in a goal-directed way is that technology itself is not completely neutral. Technology has no goals of its own. It was created by people and companies who have plenty of goals and some of those don't necessarily take you to places where you would choose to go. That's why if you don't have a goal-driven approach to technology, you may find technology is actually driving you.” – Nate Anderson
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Time Stamps:
*(04:25): Why Nietzsche? Why now?
*(15:07): Offer agency, not just prescriptive …
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AI Supply & Demand with Guy Scriven, U.S. Technology Editor at The Economist
13 december 2023 · 48 min
Thanks to GenAI, we have an overabundance of tools, models, and capabilities. However, the use and impact of these advancements is yet to be known. That’s why in the age of technological innovation, traditional skills like fact-checking are more important than ever to ensure that the technology and predictions are correct. Lees meer
Guy Scriven, U.S. Technology Editor at The Economist, is on the frontlines of the AI explosion. In his tenure at the publication, he has served as a researcher and climate risk correspondent, and has grown his affinity for telling data-driven stories. Satyen and Guy discuss the role of data in journalism, instilling a culture of debate, and the unsexy – but critical – side of AI.
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“We've had this long period of experimentation and excitement. That's been basically marked by the supply side of AI just really ramping up. You've had loads of model makers releasing new models. You've had the cloud players buying enormous amounts of specialized AI chips. You've had thousands of AI application startups who are going to build on top of the model makers, who then use the AI chips from the cloud providers. You've had this boom in the supply side of AI. Now, the big question is whether the enterprise demand meets that and what shape it takes. I think we don't really have a good sense of that until at least the first couple of quarters of next year.” – Guy Scriven
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Time Stamps:
*(02:22): Less reporting, more commentary
*(13:32): Dataset discovery
*(22:34): ChatGPT’s hallucination problem
*(34:38): AI headlines on the rise
*(41:48): What’s the next big AI story?
*(46:10): Satyen’s Takeaways
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Links
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Hard Filters and Nuanced Intuition with Scott Hartley, Author of The Fuzzy and the Techie
29 november 2023 · 49 min
The best kind of data radical is one who knows how to balance their technical expertise with their fuzzy side. Skills like storytelling, empathy, and ethics are becoming invaluable in the tech space. The ability to balance both enables data folks to recognize patterns where others might miss them. This type of integrative thinking can guide people on their next investment, whether they’re investing time, money, or resources. Lees meer
Scott Hartley is a global early-stage investor and author of The Fuzzy and the Techie: Why the Liberal Arts Will Rule the Digital World. His passion lies in emerging markets and big ideas that improve lives, particularly in financial services, health, supply chain, and logistics. Scott has served as a Presidential Innovation Fellow at the White House and has co-founded two venture capital firms: Everywhere Ventures and Two Culture Capital. Satyen and Scott discuss the techie and fuzzy sides of Silicon Valley, the advancement of tech, and how Scott chooses his next investment.
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“I love this thought around data collection and big data is one thing, it's collecting information. But, then turning that information into knowledge and into wisdom. In one part, can be done through unstructured to structured data, through things like LLMs that are enabling us to move out of the information noise into a bit more knowledge noise, and then maybe into wisdom specificity. I still think that there's a leap there that's going to be human-driven. Whether it's a person sitting there interpreting or it's a team of engineers thinking about the sensitivities, the data tagging. There are human decisions in the mix somewhere along that chain, as we're taking on structured data and turning it into structured knowledge and wisdom. All these things to say, that even these deeply technical infrastructure-level technologies, have elements of humanity in them.” – Scott Hartley
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Time Stamps:
*(10:55): The genesis behind The Fuzzy and the Techie
*(18:1…
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The Precision Prescription with Maddy Want, VP of Data, Betting & Gaming at Fanatics, Inc.
8 november 2023 · 49 min
Precision in technology is powerful. When it comes to services like Uber, people know the exact location of the driver and how much the trip will cost. Precision helps banks lend money to folks with bad credit, but who took the initiative of telling a bank when they would miss a payment. Precision can even help deliver urgent medical supplies via drones in countries that need it most. Precision in technology means users have total visibility on location, price, and competitors, and they’re able to achieve better outcomes. Lees meer
Maddy Want is the VP of Data for Betting and Gaming at Fanatics. Maddy has over a decade of data product experience spanning diverse web and app services, and has served companies like Audible, upday, and Index Exchange. When Maddy joined Fanatics, she was responsible for creating the data strategy, hiring the data team, and partnering with tech. Satyen and Maddy discuss her new book, Precisely, data governance, and why precision matters.
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“We've gone to total visibility on location, total visibility on price, and ability to shop across competitors. To me, the big theme out of all of those things is it's not about the technology itself, it's not about drones, or it's not about auction mechanics like that power Uber. Those things are cool, but it's about the capability that it's given to the customers, or the patients, or whoever. The theme there is that they have more precision. They can be more precise about what kind of change they're requesting or they're affecting, and they can have an outcome that's much more tailored to them.” – Maddy Want
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Time Stamps:
*(05:45): The disconnect between public policy and tech
*(13:09): The focus on precision
*(20:18): Writing Precisely
*(29:50): Maddy’s role at Fanatics
*(39:27): Structuring the team
*(47:19): Satyen’s Takeaways
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Everything You Wanted To Know About LLMs, but Were Too Afraid To Ask with Matthew Lynley, Founding Writer of Supervised
25 oktober 2023 · 49 min
With the rise of GenAI, LLMs are now accessible to everyone. They start with a very easy learning curve that grows more complicated the deeper you go. But, not all models are created equal. It’s critical to design effective prompts so users stay focused and have context that will drive how productive the model is. Lees meer
In this episode, Matthew Lynley, Founding Writer of Supervised, delivers a crash course on LLMs. From the basics of what they are, to vector databases, to trends in the market, you’ll learn everything about LLMs that you’ve always wanted to know. Matthew has spent the last decade reporting on the tech industry at publications like Business Insider, The Wall Street Journal, BuzzFeed News, and TechCrunch. He founded the AI newsletter, Supervised, with the goal of helping readers understand the implications of new technologies and the team building it. Satyen and Matt discuss the inspiration behind Supervised, LLMs, and the rivalry between Databricks and Snowflake.
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“This idea of, ‘How does an LLM work?’ I think, the second you touch one for the first time, you get it right away. Now, there's an enormous level of intricacy and complication once you go a single step deeper, which is the differences between the LLMs. How do you think about crafting the right prompt? Knowing that they can go off the rails really fast if you're not careful, and the whole network of tools that are associated on top of it. But, when you think from an education perspective, the education really only starts when you are talking to people that are like, ‘This is really cool. I've tried it, it's awesome. It’s cool as hell. But how can I use it to improve my business?’ Then it starts to get complicated. Then you have to start understanding how expensive is OpenAI? How do you integrate it? Do I go closed source or open source? The learning curve starts off very, very, very easy because you can get it right away. Then, it quickly becomes one of the hardest possible products to…
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Measuring the (Data) Culture of Medicine with Dr. Bapu Jena, Joseph P. Newhouse Professor at Harvard Medical School
11 oktober 2023 · 52 min
The art of medicine happens when physicians combine data and knowledge to deliver better patient outcomes. A physician that relies both on guidelines and their learned experience is creating a culture of data and insights and improving the lives of patients. Whether you’re a doctor or a data leader, knowing how to balance data and intuition will always drive better results. Lees meer
Dr. Bapu Jena is an economist, physician, and Joseph P. Newhouse Professor of Health Care Policy at Harvard Medical School. He bridges his professions to explore the economics of healthcare productivity and medical innovation. Satyen and Bapu discuss leveraging data in healthcare, applying AI in medicine, and measuring the innovation of doctors.
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“We have put a premium on the innovativeness of the technology. There could be a new molecule that attacks a pathway that has never been attacked before. If that molecule doesn't improve life expectancy or improve quality of life, then there's not a lot of value to me in that innovation, even though it's certainly innovative. I care more about whether or not it impacts patients' lives. The correlator to that is that you could have a medication which does not appear to be that quote, unquote, ‘innovative,’ at all because it's just a reboot, in some respect, of other medications. But, it's taken in a way that people are more likely to be adherent to. Those types of technologies are sometimes pooh-poohed on, but they could be very valuable because what ultimately matters is the outcome of whether or not a person gets better when they're on that medication, not how innovative it is. This is also a problem when it comes to data-driven interventions, as well. Because, there's a lot of interest in AI and non-medical technologies, or non-life science technologies. The key there is you've got to demonstrate that there's some outcome benefit.” – Bapu Jena
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Time Stamps:
*(03:23): Predictable randomness
*(12:13): Data points tracking intensit…
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Mastering Your Own Destiny with Andy Palmer & Dr. Michael Stonebraker, Co-founders of Tamr
27 september 2023 · 46 min
Starting a revolution is no easy task. Just ask Dr. Michael Stonebraker and Andy Palmer, co-founders of Tamr, the enterprise data mastering company. Their path to innovation begins with a universal problem. They also collaborate with other data radicals who challenge them to think differently and help them grow. Lees meer
Michael is a database pioneer, MIT professor, and entrepreneur. He has founded nine database startups over 40 years and won the A.M. Turing Award in 2014. Andy is a serial entrepreneur and founder, board member, and advisor for over 50 start-ups. Satyen, Michael, and Andy discuss Tamr’s tech evolution, third normal form, and probabilistic methods.
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“There's a lot of work to be done in these big enterprises of getting all the data cataloged, getting it all mastered and curated, and then delivering it out for lots of people to consume. Early on at Tamr, we did a lot of stuff on-premise and those projects just took so much longer and you ended up doing a whole bunch of infrastructure stuff that's just not required. We’re really encouraging all of our customers to think cloud native, multi-tenant infrastructure as the de facto starting point because that'll let them get to better outcomes much faster.” – Andy Palmer
“Data products and data mastering are basically a cloud problem. And so you want to be cloud native, you want to run software as a service, you want to be friendly to the cloud vendors. Tamr spent a lot of time over the last two or three years doing exactly that. There's a big difference between running on the cloud and being cloud native and running software as a service. That's what we're focused on big time right now. After that, I think there's a lot of research directions we're paying attention to. Trying to build more semantics into tables to be able to leverage. You can think of this as leveraging more exhaustive catalogs to do our stuff better. I think that's something we're thinking about a bunch.” – Dr. Michael Stonebraker
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The Human Side of Data Leadership
13 september 2023 · 25 min
Over the last two seasons of Data Radicals, we’ve seen that data experts have been promoted to leadership roles. It’s proof that organizations are seeing the value of data and the significance of establishing a data culture. Lees meer
In this episode, you’ll hear from past guests like Stan McChrystal, Tricia Wang, and Paul Leonardi as they discuss traits of a successful data leader, adapting your data strategies, and the importance of soft skills.
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“I found that if I told somebody to do a task, they might try to do that task. But if I say, ‘Create this effect,’ they owned it because they felt a level of responsibility for what approach that they chose, and it made it much stickier.” – Stan McChrystal, Retired US Army General
“I think having gone through the valley of suffering myself, I have a massive amount of respect for founders because they carry a weight that most people will never realize. So it's hard for me not to like them.” – Jepson Taylor, Chief AI Strategist at Dataiku
“Those CDOs that are most successful quickly establish trust within business, with business sponsors. They work with the business sponsors to identify what are the one or two or three most important things to them and see if they can solve those questions, even if it’s with a very small subset of data, to begin to develop that relationship, that trust.” – Randy Bean, Author of Fail Fast, Learn Faster
“You have to be able to have a learner’s mindset. You have to understand what different teams and functions do and how they play into a bigger picture so that you can get into cause and effect. And then when you start to do that, you have a lot more ability to actually have impact.” – Wendy Turner-Williams, CDO at Tableau
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Time Stamps:
*(00:48): Randy Bean: Alignment with expectations
*(02:39): Jennifer Belissent: The diplomatic CDO
*(05:01): Taylor Brown: Lead by example
*(05:44): Ashish Thusoo: The DNA of a CDO
*(07:48): Stan McChrystal: The strength of humility
*(15:40)…
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Competing Apart, Sharing Together with Michael James, SVP, Head of Data Strategy & Analytics at the NBA
30 augustus 2023 · 48 min
How does the NBA use data to compete and improve? When it comes to driving business growth with data, transparent communication makes success a slam dunk. By sharing innovative ideas and best practices across the business, one all-star team elevates the success of others across the entire organization. Lees meer
Michael James, SVP and Head of Data Strategy and Analytics at the NBA, is committed to creating a better fan experience and making better business decisions through collaboration. In this role, he bridges executive leadership and technical expertise to create a data-driven culture in constant pursuit of innovation. Satyen and Mike discuss the NBA’s digital transformation, the future of GenAI in the league, and attracting more people to sports business analytics.
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“We have very active communication with our teams. You build up a relationship over time and you start to realize, ‘If this person is sharing this thing that worked, we have a good sense of who else might be able to benefit from it.’ We'll make sure to package that up in a way that is not only informative, ‘Here's what the team did,’ but also has the tangible next steps. ‘Here's what you can actually do with this to drive the business.’ And it's no different on the data side. We've built a ton of data products through the years at the league level for our teams, also for different departments within our league office as well. But, the goal of all of those products is to make sure that we are driving better business decisions, we're driving a better fan experience, and, ultimately, that's going to lead to more revenue.” – Michael James
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Time Stamps:
*(13:16): Sharing the (data) ball among the league
*(17:34): Establishing best practices across an enterprise
*(37:08): Measuring performance to measure culture
*(41:16): Improving DEI in data
*(46:57): Satyen’s Takeaways
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Sponsor
This podcast is presented by Alation.
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Frameworks and the Art of Simplification with Dave Kellogg, EIR at Balderton Capital and author of Kellblog
16 augustus 2023 · 50 min
As data radicals, we want to deliver insights that enable our organizations to know more, which we often do by providing an answer. Instead, we should be thinking about frameworks we can implement to communicate our ideas in simple and consumable ways. Lees meer
Dave Kellogg knows the importance of a framework. Dave is one of the leading enterprise executives in software today and currently serves as the Executive in Residence at Balderton Capital. His blog, Kellblog, is a highly regarded content hub for software leaders, drawing on his experience as an angel investor, board member, advisor, and thought leader. Satyen and Dave discuss the evolution of the data industry, problem solving with frameworks, and mapping your business in a complicated world.
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“People need the world simplified for them, and if you don't do it, somebody else will. A confused buyer is just going to buy from the market leader. If you're running a startup, you're definitionally not that. The burden of simplicity is on you. If you want to be successful, you need to have a very simple explanation of why someone should buy your stuff.” – Dave Kellogg
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Time Stamps:
*(03:58): The evolution of BI and the BI customer
*(22:25): Solving complex problems with simplifying frameworks
*(32:41): Defining “data intelligence”
*(43:16): The DNA of a D&A career
*(47:44): Satyen’s Takeaways
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Sponsor
This podcast is presented by Alation.
Learn more:
* Subscribe to the newsletter: https://www.alation.com/podcast/
* Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/
* Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/
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Links
Follow Dave on LinkedIn
Follow Dave on Twitter
Read Dave’s blog
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Perfect is The Enemy of The Good with Ameen Kazerouni, CTO at Orange Theory Fitness
2 augustus 2023 · 46 min
If you’re on a journey of fitness, you know that perfectionism is your enemy. The same goes for data. There will always be another achievement that you wish to reach. Instead, focus on creating habits that will lead you to better data decisions and long term health. Lees meer
Ameen Kazerouni knows this journey well. Ameen has spent his career at the intersection of science, data, and technology to create intuitive, data-driven experiences. In his role as CTO of Orangetheory Fitness, he is driving consumer wellness journeys by turning workout data into feedback and personalized recommendations. In this episode, Satyen and Ameen discuss data-driven exercise, keeping humans in the feedback loop, and AI data governance.
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“We make a lot of investment in demystifying the Orangetheory workout. And there's a lot of parallels to data, and I love that because when you think about data in an organization, ‘Well, it's going to be a multimillion dollar investment.’ And it can get so overwhelming that instead of being like, ‘Let's start piece by piece,’ the instinct becomes, ‘Let's just keep guessing instead.’ Which is never a good idea. You should never, ‘Let's just revert to not using data at all because it's going to be really difficult to use data perfectly.’ It's the same thing with fitness. You don't revert to doing nothing at all because meeting all the requirements will be hard. Showing up and getting started is what gets you going.” – Ameen Kazerouni
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Time Stamps:
*(04:42): AI in the workout
*(13:56): Data as a habit
*(25:45): AI data governance
*(38:36): The future of connected health
*(44:08): 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/
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https://www.linkedin.com/in/ssangani/
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Links
Follow Ameen on LinkedIn
Hosted by Simplecast, an…
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Don’t Say Data Literacy with Wendy Batchelder, CDO & SVP, Global Data Governance at Salesforce
19 juli 2023 · 49 min
We’ve all heard the term “data literacy” by now. Although there is general consensus regarding the importance of knowing how to read, write, and communicate with data, some folks may take issue with the term itself. Wendy Batchelder, CDO at Salesforce, wants to reframe the conversation and focus on how people can leverage data to do their jobs better. Lees meer
Wendy is a technology executive who has spent her career tackling business problems with technical solutions and transforming diverse team members into leaders. In her current role at Salesforce, Wendy is helping the right people access the right data at the right time — with the right controls. In this episode, Satyen and Wendy discuss the problems with data literacy training, the power of answering “so what?” questions, and the value of advocating for DEI in tech.
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“You have to drop the jargon and get down to what are you trying to explain? If you're trying to help people to use more data for decision making, then just introduce the data. Don't sit down and say, ‘We're gonna talk about data literacy,’ because everyone's eyes gloss over and you lose their interest and their attention. It just doesn't give you a lot of respect. Part of our job as data experts is to help people to use data better and that's the conversation that should be had. But, the second you say things like data literacy, the tone of the conversation totally shifts.” – Wendy Batchelder
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Time Stamps:
*(06:41): Data strategy at Salesforce
*(26:37): Keeping up with connectivity
*(28:40): Data literacy denial
*(35:22): DE&I in data
*(47:18): Satyen’s Takeaways
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Sponsor
This podcast is presented by Alation.
Learn more:
* Subscribe to the newsletter: https://www.alation.com/podcast/
* Alation’s LinkedIn Profile: https://www.linkedin.com/company/alation/
* Satyen’s LinkedIn Profile: https://www.linkedin.com/in/ssangani/
--------
Links
Follow Wendy on LinkedIn
Read Wendy’s book Data Governance Handbook
Hosted …
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Asking the Right Questions with Frank Farrall, Principal, AI/Data Ecosystems at Deloitte
5 juli 2023 · 46 min
Before search engines, we had to rely on memory and investigation to answer our questions. Then, search engines made answers instantly available. Now, in the age of AI, we have to engineer our questions to get the best results. Lees meer
Frank Farrall, Deloitte's Strategy & Analytics Ecosystems and Alliances Leader, knows that asking the right questions is just as critical as knowing the answers. Frank is a global business builder with 20 years of experience helping clients and startups become billion-dollar businesses through AI and digital transformation.
In this episode, Satyen and Frank discuss identifying worthy investments, the sexiness of prompt engineering, and efficient engagement with AI.
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“I think in a lot of cases, prompt engineering will at least become a skill that knowledge workers, creative workers use to get an outcome from the technology. I think some people will be highly, highly specialized. I actually think prompts are going to have value in organizations and I think prompt libraries and how you manage prompts will become a set of IP and something that's highly valuable inside organizations. I think prompt engineering has a very significant future ahead of it. I think all of us are going to have to learn some level of prompt engineering to be effective in the future.” – Frank Farrall
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Time Stamps:
*(02:42): Defining the AI ecosystem
*(07:55): How to identify a worthy investment
*(23:23): How “sexy” is prompt engineering?
*(41:44): The future of generative AI
*(44:43): 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 Frank on LinkedIn
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Building the Company You Wish You Could Buy From with Mike Capone, CEO of Qlik
21 juni 2023 · 42 min
As folks in the data space, we’re introverts by nature. But, getting out of our comfort zone can open you up to endless possibilities. As one person who’s gone from CIO to CEO can tell you, the key to growth is getting comfortable with feeling uncomfortable. Lees meer
That person is Mike Capone, CEO of Qlik, where he’s revolutionizing the business intelligence landscape through data. In this episode, Mike shares with Satyen how his decades of experience in product development, data science, and go-to-market operations influence his role as CEO today. Satyen and Mike discuss transitioning from CIO to CEO, navigating economic downturns, and stepping out of your comfort zone.
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“Now is the time to get closer to your best customers. They're the ones who sustain you through these periods of economic ups and downs. The reality is for both of us and both of our companies, companies need data and analytics now more than ever. How are you going to navigate this uncertainty? You're going to navigate it through data. The conversation like, ‘Hey, we don't want to spend any more money on data and analytics because the environment is tough right now,’ is actually counterintuitive. The reality is you need data and you need real-time data to get through it because your old data models are useless.” – Mike Capone
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Time Stamps:
*(02:51): The growth of Qlik
*(08:37): The relationship between private equity and software
*(20:00): From CIO to CEO
*(27:29): Navigating rough economic times with data and analytics
*(33:10): Maintaining long-term landscape leadership
*(40:24): 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/
--------
Links
Follow Mike on LinkedIn
Follow Mike on Twitter
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