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- 6 days ago41 min
S&P Global’s Chief Data Officer on Turning Data into Business Outcomes
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 : 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 : 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 : 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 : 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 : 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 S&P Global Agrees to Acquire With Intelligence from Motive Partners for $1.8 Billion, Establishing Its Leadership in Private Markets Intelligence 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.
- 3 weeks ago53 min
How Semantic Layers and Ontologies Create Trusted AI
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 : 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 : 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 : 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 : 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 : 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. Pr
- Apr 8, 202640 min
Inside WHOOP's Wearables AI Engine for Predictive Health
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 : 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 : 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 : 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 : 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 : 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 </p
- Mar 25, 202642 min
A Wharton AI Research Leader's Formula for Responsible AI
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 : 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 : 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 : 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 : 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 : 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 The Wall Street Journal: The Boss Has a Message: Use AI or You’re Fired 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, Era
- Mar 11, 202646 min
How a Serial CDAO Scales AI in Insurance with Verisk
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 : 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 : 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 : 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 : 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 : 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
- Feb 25, 202641 min
How AI Augments Human Creativity at Scale: The WPP Blueprint
Learn how AI agents are reshaping enterprise decision-making, AI governance, and brand creativity. Daniel Hulme, Chief AI Officer at WPP & CEO of Satalia/Conscium, explains how AI agents, decision intelligence, and his concept of “brand brains” (AI systems designed to create brand-specific, production-grade content) are changing how organizations operate. He shares why companies don’t have data problems but decision-making problems, and how AI can augment human creativity at scale. Key Moments: From Academic AI Research to Enterprise AI Systems : Daniel traces his 25-year journey in AI, from studying intelligence and consciousness at UCL to building real-world systems inside global enterprises. He explains how curiosity about what it means to be human ultimately shaped his approach to building practical, responsible AI at scale. AI Agents and Risk: Why AI Needs Governance : Daniel introduces a defining metaphor, describing AI agents as intoxicated graduates—confident, fast, and often wrong. He uses this framing to explain why unchecked agent deployment is risky and why governance, testing, and supervision are essential as organizations scale AI. What Most Organizations Get Wrong About AI Testing: : Daniel breaks down the difference between testing for knowledge versus testing for real capability. He argues that most companies stop at surface-level validation, creating a false sense of safety and trust. How AI Changes Business Decision-Making : Daniel challenges the traditional analytics mindset, arguing that dashboards and insights rarely lead to better decisions. He explains why AI should be designed to make decisions directly and why humans are fundamentally bounded when dealing with complex optimization problems. Brand Brains and the Future of Creative Differentiation : Daniel introduces the concept of “brand brains,” explaining why generic generative AI content won’t create competitive advantage. He shows how agentic systems can produce brand-specific, production-grade content that actually differentiates businesses. Key Quotes: “ There are many things that our brains do that are different to large language models that I think will inspire us to create much more energy-efficient machines.” - Daniel Hulme “Giving human beings better insights doesn't typically lead to better decisions… So working backwards from the problem to the data historically, for me, has been a success.” - Daniel Hulme “The reality is that those agents will go wrong… So there's going to be much more emphasis over the next year or so on governance [and] on making sure that they are capable of doing that job.” - Daniel Hulme Mentions WPP’s AI “brains” Will AI ever be better than humans at predicting what humans want? | WPP The Hidden Spring: A Journey to the Source of Consciousness by Mark Solms Guest Bio Dr. Daniel Hulme is a globally recognised expert in Artificial Intelligence (AI) and investor in emerging technologies. He's the CEO of Satalia, an award-winning AI company that was acquired by the world's largest marketing company in 2021, WPP, where he is now the Chief AI Officer. Daniel has been recognised as one of the world's leading keynote speakers as well as one of the top ten Chief AI Officers globally. Amongst his many technology investments, Daniel is also Founder and CEO of the World's first commercial research organisation to understand Machine Consciousness, Conscium. With over 25 years academic experience with AI, Daniel received his Masters and Doctorate in AI at UCL. He was previously Director of UCL’s Applied AI Masters Programme, where he is now UCL’s Computer Science Entrepreneur-in-Residence. Daniel is also an Impact Board Member of St Andrew’s University Computer Science department and the University of Sussex Informatics department, focused on using AI to solve business and social problems. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.
- Feb 11, 202643 min
How Nasdaq Architected a $90 Trillion Data Ecosystem
Discover how Nasdaq uses data platforms at a massive scale to power markets and prepare for AI. Angie Ruan, Nasdaq’s CTO of Capital Access Platforms, explains how large-scale data systems support market integrity, transparency, and decision-making across public and private markets. She defines what it really means to be AI-ready, how leaders should modernize data platforms, and how market fundamentals help separate real AI value from hype.Key Moments:Why Nasdaq Is More Than a Stock Exchange : Angie reframes Nasdaq as a global technology company rather than a traditional exchange, explaining how data, platforms, and engineering underpin trust, resilience, and transparency across public and private markets.The Scale of Market Data Powering the Global Financial System : Angie breaks down the massive scale of Nasdaq’s data ecosystem, including hundreds of billions of market messages per day and platforms supporting more than $90 trillion in assets. She explains how data quality and reliability are foundational to market integrity and decision-making.Building a Unified Data Intelligence Platform at Nasdaq : Angie explains how Nasdaq approaches data architecture, governance, and platform design to create a unified data intelligence layer. She shares why access control, operational efficiency, and data trust matter more than raw data volume when enabling analytics and AI at scale.The AI-Ready Playbook for Data and AI Leaders : Drawing on her experience across startups and large enterprises, Angie outlines a practical framework for data and AI transformation. She emphasizes cloud adoption, breaking down silos, listening to business needs, and treating platform modernization as both a technical and organizational challenge.Is AI a Bubble? Using Market Data to Separate Hype from Reality : Angie applies a data-driven lens to the AI bubble debate, examining earnings growth, margins, return on equity, and capital investment. She explains why current financial indicators suggest today’s AI moment differs fundamentally from past technology bubbles.Key Quotes:“ The foundation of any data strategy is actually cloud… If you don't put the data or the actual system in the cloud, it's much harder in terms of services and platform, let alone AI.” - Angie Ruan“Data is great, but the more important [thing]... is how we put it all together.” - Angie Ruan“ The world is going to change so fast… Being curious [and] continuing to learn, it is so important.” - Angie RuanMentionsInside the Invitation-Only Stock Market for the WealthyNasdaq eVestment: The Institutional Intelligence Platform Powering $90T+ in DecisionsGuest Bio Angie Ruan is the Chief Technology Officer, Capital Access Platforms at Nasdaq. An award-winning industry leader, Ms. Ruan holds four technical patents and has been instrumental in driving digital transformation across many industries, including enterprise application, e-commerce, payment, and capital markets. She most recently served as Vice President of Engineering at Chime before returning to Nasdaq where she was the Senior Vice President of Global Technology, responsible for overseeing the development of Key Market Technology Products and Corporate Platforms. Prior to joining Nasdaq, Ms. Ruan served as the Global Group Technology Vice President of consumer experiences and platform for American Express, where she was responsible for the digital transformation of American Express web and mobile technology. Before then, she was the Unit CIO for U.S. Consumer and U.S. Small Business, and was also Head of Engineering for Global PayPal Retail and Merchant product lines. As well, she held various executive engineering leadership roles at eBay including building the eBay messaging system, creating the eBay mobile platform, and transforming the DevOps organization.Recognized as one of Silicon Valley's Women of Influence, Ms. Ruan holds an M.S. in Computer Science from the University of California, Santa Barbara, and studied undergraduate in Computer Science at Tsinghua University of China. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.
- Jan 28, 202648 min
How OneDigital Is Reframing AI Agents as Coworkers
Discover why the future of AI at work is more human than you think. Vinay Gidwaney, Chief Product Officer at OneDigital, shares how treating AI agents as talent rather than technology is changing AI adoption at work. He explains people-first change management, managing AI coworkers, building trust in human AI teams, and why real transformation depends on equity, access, and better decision-making.Key Moments:AI as a Coworker : Vinay introduces “Ben,” an AI benefits consultant at OneDigital, to illustrate a fundamentally different way of thinking about AI at work. Instead of positioning AI as automation or replacement, he explains how AI coworkers are designed to augment human expertise and support better decision-making.Hiring, Training, and Managing AI Like Employees : Vinay walks through OneDigital’s intern-to-apprentice model for AI, including job descriptions, cultural fit, human managers, and ongoing supervision. He shows how applying HR rigor to AI builds trust, accountability, and clarity while helping employees see AI as part of the team, not a threat.Why AI Projects Fail: The Misguided Focus on Tools over Talent : Vinay argues that AI fails when treated like a traditional IT rollout. He emphasizes that AI adoption is fundamentally a people and change-management challenge, calling on HR leaders to lead the shift in how humans and AI work together.Recognizing the Limits and Risks of AI Automation : Vinay explains why fully autonomous AI agents often fall short in knowledge-based organizations. He cautions leaders to be skeptical of automation-first promises and introduces a more realistic model centered on cognition, human oversight, and thoughtful ROI evaluation.The Future of Work in an AI World : Vinay reflects on his career and argues that software alone is no longer a defensible moat. He emphasizes speed, insight, services, and human judgment as the true sources of lasting value.Key Quotes:“ If you treat AI like any other technology that you've adopted in your company. It's not going to work out as well as you'd like… It's not a technology thing, it's a people thing.” - Vinay Gidwaney“ AI is a great way to spread human talent in your organization because they mirror what your humans are doing.” - Vinay Gidwaney“AI is talent and you have to treat AI in the same way that you treat the talent within your organization.” - Vinay GidwaneyMentionsHow to Train Your AI ‘Coworker’What are AI agent types? How to choose one for your dataReconfiguring work: Change management in the age of gen AIGuest Bio Vinay Gidwaney is the Chief Product Officer at OneDigital, a national insurance, employee benefits, HR, and financial services company serving 100,000 employers and 10 million families, with over $1B in revenue. He is responsible for defining and executing the technology and AI strategy as the company reinvents the insurance and wealth management industries with innovative products.Previously, Vinay led the technology strategy for CIC Health during one of the largest COVID-19 public health campaigns in U.S. history. Collaborating with public partners, he helped set up award-winning vaccination efforts at iconic locations such as Gillette Stadium and Fenway Park, and at numerous community sites, administering 1.2 million COVID-19 vaccinations in less than seven months.A 2002 winner of the prestigious MIT Technology Review Magazine Top 100 Innovators Under 35 award, Vinay is a proud father to Leela, Niam, Kayvion and Samay. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.
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About The Data & AI Chief
Meet the world’s top data and analytics leaders transforming how we do business. Hear case studies, industry insights, and personal lessons from the executives leading the data revolution. Join host Cindi Howson, Chief Data & AI Strategy Officer at ThoughtSpot, every other Wednesday to meet the leaders and teams at the cutting edge.
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