The Data & AI Chief

Learn what happens when the executive accountable for data strategy is also the executive accountable for the business results that depend on it. Saugata Saha, President of S&P Global Market Intelligence and Chief Enterprise Data Officer at S&P Global, shares how he manages one of the world's largest financial data estates while driving business outcomes across public and private markets. He breaks down the four pillars of S&P Global's data strategy, the federated organizational model that connects data teams to business value, and why capturing ROI from AI requires deliberate workflow transformation.

Key Moments

Why Data Strategy Must Follow Business Strategy (04:57): Saugata challenges the idea that data and business strategy can run in parallel. Market trends, customer pain points, and existing capabilities must come first.

Building an AI-Ready Financial Data Estate (15:10): Scale alone does not create intelligence. Saugata explains why semantic layers and graph databases are the hard work behind connected financial data.

How AI Compresses Post-Acquisition Data Integration (18:29): Manual reconciliation of millions of records is no longer the only path. Discover how AI entity matching accelerated post-acquisition integration.

The Federated Model That Connects Data to Value (22:49): Most large organizations either over-centralize data teams or leave them too embedded to scale. Saugata outlines the federated model that actually bridges both.

Rethinking AI Productivity: From Marginal to Transformative (28:29): Most AI programs stop at training and tooling. Saugata explains why deliberately redesigning workflows is the missing step between AI investment and real ROI.

Key Quotes

“Data strategy and business strategy have to be very tightly connected. And if they're not, that's when value capture does not happen. In fact, I would go so far as to say data strategy actually follows from business strategy.” - Saugata Saha

“Stop treating data as an afterthought or byproduct, but start thinking about data as a key ingredient for value creation and competitive advantage.” - Saugata Saha

“We don't want everybody to become 10% more productive, because that's a little squishy. We want 10% of the people to become a hundred percent more productive so they can do other things.” - Saugata Saha

“If a company can really use data at scale for better decision making, better client service, [and] better outcomes, that creates a lasting edge over the competition.” - Saugata Saha

Mentions

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The Data & AI Chief: Why a Federated Data Team is Crucial for Business Value, with Dow

Private Companies Wait Too Long to Go Public

The Lex Fridman Podcast

Guest Bios 

Saugata serves as President of S&P Global Market Intelligence, leading the division's efforts to deliver essential insights and intelligence to clients worldwide. He is also S&P Global’s Chief Enterprise Data Officer, responsible for driving innovation and excellence in the company’s enterprise data strategy. Saugata is a member of S&P Global’s Executive Leadership Team, contributing to the strategic direction and growth of the organization.

Before joining S&P Global, Saugata was a consultant at McKinsey & Company’s New York office, where he advised clients on strategy, mergers and acquisitions, corporate finance, and operational improvements across various industries, with a strong focus on financial services.

Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Learn why an organization’s ontology, a structured framework for how a business defines, connects, and makes sense of its data and knowledge, is the most valuable and most overlooked asset in any AI strategy. Jessica Talisman, CEO and Founder of The Ontology Pipeline, and Tony Seale, Founder of The Knowledge Graph Guys, break down what it actually takes to build trusted AI, covering everything from semantic layers and knowledge graphs to why provenance is non-negotiable. They explain how organizations can start building their knowledge infrastructure for AI, and make the case for why their ontology is their most defensible competitive asset.

Key Moments

BI Semantic Layers vs. AI Context Layers (02:21): Explore the evolution from 1990s business vocabularies to modern AI context layers. Learn why ontologies are essential for connecting data points beyond traditional BI.

Why Knowledge Graphs are Essential for AI (09:22): Understand why relational databases fail AI's needs. Tony explains how knowledge graphs turn data relationships into "first-class citizens" using open standards.

How to Build Your First Business Ontology (17:04): Stop over-modeling and start delivering. Learn how to anchor your data strategy to high-value use cases and business-language competency questions.

Solving the AI Provenance & Lineage Gap (34:19): Why LLMs lack built-in reliability. Jessica discusses the necessity of injecting data lineage at the retrieval layer to verify AI accuracy and prevent hallucinations.

Why Your Ontology is Your Most Valuable IP (39:27): In the age of commodity AI, your internal data relationships are your only moat. Discover why hosting your ontology with third parties puts your core assets at risk.

Key Quotes

“If you let somebody else take your ontology, learn the essence of what it is that you know that's out of distribution with the rest of the world, you've just given them everything valuable about your company.” - Tony Seale

“The accuracy of the information you receive is reliant upon the lineage or the provenance of the information received from an LLM. It's so important." - Jessica Talisman

“As a business leader, you need to be looking below the surface to the data infrastructure. The key trick to do right now is to turn the power of the models that we've got back upon your own internal infrastructure to build out these rich ontologies and to connect your information.” - Tony Seale

"Your ontology is like your thumbprint, your digital thumbprint for your organization. It's unique to each organization, and how you define things may not be the same as an LLM might define something." - Jessica Talisman

Mentions

The Ontology Pipeline® - A Semantic Knowledge Management Framework | Jessica Talisman

How the Ontology Pipeline Powers Semantic Knowledge Systems | Jessica Talisman

Why Early Knowledge Graph Adopters Will Win the AI Race | The Knowledge Graph Guys

Spec-First Development: Why LLMs Thrive on Structure, Not Vibes | The Knowledge Graph Guys

The Knowledge Graph Academy

Guest Bios 

Tony Seale

For over a decade, Tony has been passionate about linking data. His creative vision for integrating Large Language Models and Knowledge Graphs within large organisations has gained widespread attention, particularly through his popular weekly LinkedIn posts, earning him the reputation of ‘The Knowledge Graph Guy.’

Today, as the founder of The Knowledge Graph Guys, Tony is dedicated to helping organisations harness the power of their data. His consultancy develops cutting-edge Knowledge Graphs that fuel innovation and growth in the rapidly evolving Age of AI.

Jessica Talisman

Jessica Talisman has dedicated her 25-year career to exploring the dynamics of information and knowledge—how it flows across systems, evolves through context, and powers intelligent technologies. Her work spans historical research, educational frameworks, and enterprise-scale applications of artificial intelligence.

Previously a Senior Information Architect at Adobe, Jessica led the development of semantic knowledge graphs to enrich content and contextual understanding. She now serves as CEO and Founder of Ontology Pipeline, where she leads efforts to bridge the worlds of library science and data management - building robust, scalable knowledge systems for the AI era.

Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Discover how WHOOP is building an AI-powered health data infrastructure that is redefining how we understand human health. Emily Capodilupo, Senior Vice President of Research, Algorithms, and Data at WHOOP, explains how continuous physiological data is uncovering new opportunities in predictive health through AI, from presymptomatic disease detection to biological age scoring. She examines the governance challenges of deploying AI in a regulated environment and what it takes to build the data trust required to make it work at scale.

Key Moments:

How WHOOP Built Its AI and Data Foundation (00:57): Emily explains how WHOOP's early focus on elite athlete performance shaped the data collection rigor and multidisciplinary science organization that now powers its predictive health capabilities. She outlines the model she built across AI, machine learning, clinical research, and digital signal processing, and why starting with the highest-demand use case created a data foundation built to scale.

The Power of Continuous Data (06:21): Emily draws on WHOOP's sleep research to show how continuous physiological data reveals patterns that would be invisible without longitudinal tracking. She shares findings linking sleep architecture to metabolic disease, cancer risk, and cognitive decline, illustrating why the depth and continuity of a data set determine what insights are actually possible.

The Data Governance Challenge of Acting on Sensitive Data (13:17): Emily shares how WHOOP's respiratory rate data could detect COVID infection up to three days before symptom onset in over 80% of cases, but a denied FDA application left the company holding actionable insights it was legally prohibited from sharing. She examines the governance tension that emerges when your data capabilities move faster than the regulatory frameworks designed to govern them.

Turning Complex Multi-Signal Data Into a Single Actionable Metric (27:32): Emily introduces WHOOP's Healthspan feature, which translates physiological and behavioral data across nine components into a single biological age score tied to all-cause mortality risk. She explains why distilling complex data into one number is more motivating than presenting raw risk statistics, pointing to research that shows how age-based framing drives stronger behavior change.

Building Data Trust and Privacy Infrastructure at Scale (31:40): As WHOOP moves into FDA-cleared products and more sensitive data collection, Emily outlines the governance principles that underpin member trust. She argues that for any organization building on sensitive personal data, the asymmetry between earning trust and losing it should be a foundational design constraint.

Key Quotes:

"It takes 13 years to earn the trust and one mistake to lose it. And that kind of asymmetry is constantly top of mind." - Emily Capodilupo

"We were able to show that we could detect COVID up to three days before symptom onset in over 80% of cases." - Emily Capodilupo

“ WHOOP has been collecting data [for] over 12 years. We're working on a lot of new types of algorithms that are able to help people understand their bodies in ways that we might not have appreciated…even just a couple years ago.” - Emily Capodilupo

"One of the ways that AI has advanced the product... is this ability to chat with WHOOP in natural language, the way you might chat to a doctor or a trainer or a coach." - Emily Capodilupo

Mentions

Harvard Study | Analyzing changes in respiratory rate to predict the risk of COVID-19 infection 

Cornell Study Uses WHOOP Sleep Data to Monitor Patients at Risk for Alzheimer’s

Can Data Help Us Sleep Better? | WHOOP

There's More to Sleep than Sleep Need: The Importance of Sleep Consistency | WHOOP

Cribsheet & Expecting Better 2 Books Collection Set By Emily Oster 

The Family Firm: A Data-Driven Guide to Better Decision Making in the Early School Years By Emily Oster 

Guest Bio 

Emily Capodilupo is an award-winning AI and research leader with more than 13 years of experience building and scaling science-driven organizations in fast-paced startup environments. She began her career as an emergency medical technician before studying neurobiology and human sleep at Harvard University and conducting research at Brigham and Women’s Hospital. Emily is driven by a passion for using data to solve hard problems and advance our understanding of human physiology. Along the way, she "accidentally" became a data scientist, recognizing that the biggest breakthroughs in health require not just rigorous science, but big data and bold technology. 

As WHOOP’s first employee, Emily founded and now leads the company’s science organization, pioneering a new model of health that begins long before diagnosable illness and is continuous, personalized, AI-powered, and designed to empower individuals to take the driver’s seat in their own well-being. She has built and scaled multidisciplinary teams across artificial intelligence, machine learning, digital signal processing, clinical research, and engineering to translate real-time physiological data into actionable insights that improve performance, resilience, and long-term health. Emily’s work sits at the intersection of wearable technology, digital biomarkers, and predictive health, helping shift healthcare from reactive treatment to proactive optimization.

Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Learn why scaling AI is as much a human challenge as it is a technological one. Stefano Puntoni, Co-Director of Wharton Human-AI Research and Professor at The Wharton School, examines the limits of data-driven decision making in the age of AI and why insights so often fail to translate into action. He breaks down the psychology behind AI resistance and outlines the leadership and change management strategies needed to turn AI potential into real organizational impact.

Key Moments:

Why More Data Doesn’t Lead to Better Decisions (02:26): Stefano challenges the assumption that smarter algorithms automatically produce smarter decisions. He argues that decision quality depends on rigorous conceptual thinking before turning to data. Without clearly defining objectives, alternatives, and success criteria, analytics efforts rarely translate into meaningful action.

Conversational AI and the Lowering of the Cost of Action (07:26): Stefano explains how conversational AI brings decision makers closer to data by reducing friction. By lowering the cost of experimentation, AI enables managers to test hypotheses in real time instead of waiting days for analysis. This shift moves organizations from analysis paralysis to faster, more confident action.

Rethinking Your Role in the Age of AI (17:16): For professionals navigating disruption, Stefano outlines two paths forward. One is becoming a complement to AI by upskilling and using the technology as a productivity multiplier. The other is pivoting toward skills AI is less likely to replace, such as strategy, orchestration, and human judgment.

The AWARE Framework: Pairing Technical Rollout with Human Rollout (22:41): Stefano introduces the AWARE framework to help leaders anticipate and manage the human reactions to AI transformation. He argues that every technical implementation must be matched with structured communication, identity support, and organizational alignment. Without this dual-track approach, even well-designed AI systems can fail to gain traction.

Change Management, AI Literacy, and the Gap in Organizational Readiness (31:11): Only a small percentage of organizations have formal AI change management programs. Stefano questions whether companies are truly prepared for large-scale AI transformation. He emphasizes that AI literacy, leadership accountability, and structured change management will determine whether AI investments translate into sustained performance.

Key Quotes:

“ The leaders need to know why we are doing AI. AI is not a strategy; AI is just a tool. So what is it that we're trying to achieve?” - Stefano Puntoni

“ I think the problem is that technology is almost like taking all the oxygen from the room. There's so much attention and urgency around the tech itself that we often forget the people around it.” - Stefano Puntoni

“You don't want to be the substitute to the technology because if that is what you do, then there's no future. But if you're a complement, the technology might be a multiplier of your productivity.” - Stefano Puntoni

Mentions

Decision-Driven Analytics: Leveraging Human Intelligence to Unlock the Power of Data

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2025 Report Accountable Acceleration: Gen AI Fast-Tracks Into the Enterprise

How AI Affects Our Sense of Self

Why Gen AI Feels So Threatening to Workers

Conversational AI: The Next Frontier of Digital Platform Monetization

Guest Bio 

Stefano Puntoni is the Sebastian S. Kresge Professor of Marketing at The Wharton School. Prior to joining Penn, Stefano was a professor of marketing and head of department at the Rotterdam School of Management, Erasmus University, in the Netherlands. He holds a PhD in marketing from London Business School and a degree in Statistics and Economics from the University of Padova, in his native Italy.

His research has appeared in several leading journals, including Journal of Consumer Research, Journal of Marketing Research, Journal of Marketing, Nature Human Behavior, and Management Science. He also writes regularly for managerial outlets such as Harvard Business Review and MIT Sloan Management Review. Most of his ongoing research investigates how new technology is changing consumption and society, including how humans are adopting and evolving with AI.

Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Discover how enterprise AI and data strategy are operationalized at scale in one of the most highly regulated industries in the world. Louis DiModugno, Global Chief Data Officer at Verisk, shares how he builds AI-ready data foundations across 40+ petabytes of insurance and risk data, and the best practices behind embedding AI into enterprise products. He discusses unstructured data, deepfakes, and the shift from governance to observability, offering practical insights for data leaders scaling AI responsibly.

Key Moments:

From Military Leadership to Chief Data Officer: Data Integrity as a Competitive Advantage (03:02): Louis shares how his experience as a U.S. Air Force Colonel has shaped his approach to data governance, data quality, and enterprise AI leadership. He explains why integrity, service, and operational excellence are essential foundations for modern CDOs building trusted, decision-ready data environments.

Building AI-Ready Data Foundations at a 40+ Petabyte Scale (17:13): Managing more than 40 petabytes of insurance and risk data, Louis breaks down how Verisk transforms complex, multi-source data into AI-ready infrastructure. From entity resolution and master data management to benchmarking and predictive analytics, he outlines what it takes to prepare enterprise data for AI and advanced analytics at scale.

Designing an AI-First Data Strategy for Enterprise Decision Intelligence (20:00): Louis breaks down how Verisk evolved toward an AI-first data strategy across more than 150 insurance and analytics products. Rather than treating AI as an add-on, he explains how embedding AI into core workflows enables smarter underwriting, pricing, regulatory reporting, and risk management. He also discusses the strategic role ThoughtSpot plays in delivering natural language search, embedded analytics, and scalable AI-driven decision making.

AI Fraud, Deepfakes, and Risk Management in Financial Services (26:11): As AI-generated images and synthetic claims become more sophisticated, Louis discusses how the insurance industry is combating deepfake fraud and AI-driven manipulation. He shares best practices around AI risk management, vendor partnerships, and regulatory collaboration to protect policyholders and maintain trust.

Unstructured Data and AI: Why Governance Still Matters (29:28): Louis explores how expanding beyond structured data is reshaping enterprise AI. He explains why incorporating unstructured data into vector databases, graph models, and knowledge systems can significantly improve model accuracy and decision confidence. At the same time, he emphasizes that stronger governance (or observability as he reframes it) is essential as organizations scale AI across regulated industries.

Key Quotes:

“The more data that you bring to the equation, the more elements that you have in the algorithm, the higher level of accuracy you should be able to reach with your outcomes.” - Louis DiModugno

“I've tried to move away from using the word governance as much as I like to use the word observability, because I really think observability shows more aspects of what it is that we are doing with the data.” - Louis DiModugno

“The underlying aspect of what ThoughtSpot's delivering to them is our insights that not only give them their answer, but also give them insights that maybe they weren't looking specifically for. One of the big benefits of ThoughtSpot is that it's trying to anticipate what you're asking for.” - Louis DiModugno

“We've partnered with ThoughtSpot, which brings AI embedded within its product. By having our data available through the data sets that we populate through the ThoughtSpot products, we've got the opportunity to utilize Spotter and the natural language processing capabilities to interact with the data, so that you can ‘talk with your data’.” - Louis DiModugno

Mentions

From Months to Weeks: How Verisk Scaled Embedded Analytics

Breaking Down Digital Media Fraud for Claims in the AI Era

Randy Bean’s 2026 AI & Data Leadership Executive Benchmark Survey

Guest Bio 

Louis DiModugno brings more than 20 years of career experience in data and analytics to his new role. He has held several leadership positions in insurance and (re)insurance at firms including The Hartford and AXA US, where he served as the company’s inaugural Chief Data & Analytics Officer. Most recently, DiModugno pioneered the role of Chief Data and Technology Officer for Hartford Steam Boiler.

Before entering the private sector, DiModugno served with distinction as a Colonel in the U.S. Air Force and Air Force Reserves. He has held teaching positions at Rensselaer Polytechnic Institute, and he currently serves on the Chief Data Officer Advisory Council for the George Mason University School of Business.

Hear more from Cindi Howson here. Sponsored by ThoughtSpot.