The Data & AI Chief

ThoughtSpot

0.0（0）
テクノロジー
アップデート：隔週

Meet the world’s top data and AI leaders transforming how we do business. Hear case studies, industry insights, and personal lessons from the executives leading the data and AI 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. [CLAIM:FI41QQVR]

4時間前

Discover three must-read books from Linda Hill, Sanjeev Mohan, and Harveer Singh on scaling AI innovation, AI-ready data, and human-centered AI.

In this special episode of The Data & AI Chief, host Cindi Howson sits down with three incredible authors to explore what it really takes to lead through the AI era, from scaling innovation to building AI-ready data foundations to designing systems that keep humans at the center. Get ready for a deep dive into: Scaling innovation and leading through uncertainty with Linda Hill, Harvard Business School Professor and author of Genius at Scale: How Great Leaders Drive Innovation Building AI-ready data foundations with Sanjeev Mohan, Principal at SanjMo and author of Designing the AI-Driven Data Foundations Designing accountable, human-centered AI with Harveer Singh, Co-Founder and CIO of Rizz Wireless and author of When Data Moves Consider this your fall reading list for leading through the next phase of AI. Key Moments: Why Scaling Innovation Requires a Culture Shift (01:03): Linda Hill, author of Genius at Scale, unpacks her ABC model of architects, bridgers, and catalysts in scaling AI innovation. The Timeless Fundamentals Behind AI-Ready Data (32:40): Sanjeev Mohan, author of Designing the AI-Driven Data Foundations, explains why data fundamentals haven't changed even as the tools have. Why Every Data Pipeline Decision Has a Human Consequence (58:38): Harveer Singh, author of When Data Moves, shares personal stories showing the human stakes behind every data decision. Key Quotes: “Innovation is almost always the result of collaboration, experimentation, learning of people who are different, who have different perspectives, different expertise.” - Linda Hill “I strongly believe that to achieve the promises of AI, we should really focus on the foundational pieces, because those things never go away.” - Sanjeev Mohan “The more and more we are embedding these AI agents into our technology, we need to make sure that there is an accountability factor, there is a responsibility factor that is put into it before these decisions are made.” - Harveer Singh Mentions: Genius at Scale: How Great Leaders Drive Innovation Designing the AI-Driven Data Foundations When Data Moves The AI Fairness Test Nobody Runs Guest Bios: Linda Hill Linda A. Hill is the Wallace Brett Donham Professor of Business Administration at Harvard Business School and Faculty Chair of the Leadership Initiative. She is widely recognized as one of the world’s foremost experts on leadership and innovation. Hill is the coauthor of Genius at Scale: How Great Leaders Drive Innovation (March 2026), which introduces three essential roles for leading innovation across organizations and ecosystems: architect, bridger, and catalyst. The book was shortlisted for the 2025 Thinkers50 Innovation Award. She is also the coauthor of the award-winning books Collective Genius and Being the Boss. Her TED talk on leading collective creativity has garnered more than three million views. Sanjeev Mohan Sanjeev Mohan is a recognized thought leader in cloud technologies, modern data architectures, analytics, and artificial intelligence. With a keen focus on emerging trends and technologies, Sanjeev hosts It Depends podcast and authors regular Medium blogs. He is also the author of Data Product for Dummies. Formerly a Vice President at Gartner, Sanjeev was renowned for his in-depth research and strategic insights, shaping the research agenda for data and analytics globally. Over the past three years, he has led SanjMo, a consultancy specializing in technical advisory services that elevate category and brand awareness for clients. Sanjeev is the author of Designing the AI-Driven Data Foundations, a practical guide to building modern data foundations for the AI era. Harveer Singh Harveer Singh is a globally recognized data, AI, and fintech leader who bridges enterprise-scale leadership with entrepreneurial execution. Across 25+ years, he has transformed financial institutions spanning banking, payments, telecom, and Web3. A recognized industry leader, Harveer's work has earned DataIQ Top 10 CDOs, American Banker Innovation of the Year, and American Asian Outstanding 50 honors. In 2025, Harveer co-founded Rizz Wireless, an AI-native wireless company, where he serves as Co-Founder, CIO, and Board Member. He is also co-creator of RZTO, the company's Solana-based rewards token, which reimagines how loyalty and telecom rewards work and is listed on Gate.io. Harveer is the author of When Data Moves, which challenges the tech industry to remember that data systems serve humans, not machines. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.
8月19日

How Vanguard is Architecting its AI Semantic Layer

Semantic layers and ontologies have moved from nice-to-have data modeling tools to the foundational engine required for enterprise AI. In this episode, Raman Tallamraju, Senior Director and Head of Enterprise Data Architecture and Engineering at Vanguard, breaks down how Vanguard is architecting its AI semantic layer to turn scattered institutional knowledge into reliable, agent-ready context. He shares why autonomous agents expose decades of hidden data debt, how to bridge domain-specific definitions like clients versus prospects, and how to balance building a unified semantic layer with a pragmatic, federated data operating model. Key Moments: Why Data Is Your Differentiator (02:36): Across four asset managers, Raman shares the one lesson that holds: data is the real differentiator in an AI-first world. Why Semantic Layers and Ontologies Are a Priority (14:12): Autonomous agents remove the human workaround, exposing years of technical debt in data modeling that tribal knowledge used to hide. Why Context Makes or Breaks Your AI Agents (19:18): Context is everything. Agents act confidently on wrong answers when terms like client, prospect, and lead go undefined. Launching an AI-Ready Data Program: Where to Start (24:54): Raman advises starting with a real business use case tied to points of economic leverage, rather than trying to boil the ocean. Why You Need a Federated Data Model (36:51): Raman explains why a single central platform isn't practical at a global firm, and the four levers he uses to earn real business ownership of data. Key Quotes: “If we're going to go in an AI-first world and everybody has access to the same frontier models… well, what is going to be differentiated about you? Data will be your differentiator, and the companies that bring the best data game are going to have enduring advantages over those that don't.” - Raman Tallamraju “You want your data to be well-defined. You want your data to be trusted. You want your data to be well-connected. You want your data to be contextualized. You want your data to be consumed in a multimodal way, and you want it to be ready for humans and machines at scale.” - Raman Tallamraju “If you're going to start with tech and you're going to go towards the shiny toys, those are good, but without the data foundations, they're going to sit in the garage. So I started calling it the Ferraris in the garage problem. Unless you get the data strategy running ahead, these Ferraris are going to run out of gas.” - Raman Tallamraju Mentions: The Innovator's Dilemma by Clay Christensen 57% of enterprises traced a wrong AI answer to missing business context — Credible bets portable, open-source semantic code beats proprietary metadata Guest Bio: Raman Tallamraju has twenty years leading enterprise technology and data strategy across four of the world’s largest asset managers — Vanguard, T. Rowe Price, Capital Group, and Fidelity. Appointed Officer and Group Vice President at T. Rowe Price, where Raman built and led a 300-person global technology organization responsible for the firm’s enterprise architecture and digital transformation. Wharton CTO Program, 2024. Raman has operated across the full investment value chain — research and trading platforms, client experience, distribution technology, and enterprise data infrastructure — with portfolio accountability exceeding $100M. Raman’s career has been defined by taking on complex, high-stakes technology transformations and delivering measurable business outcomes: modernizing legacy estates, building scalable platforms, and creating the organizational structures that sustain them. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.
8月5日

How Grant Thornton Reimagines Core Processes in the AI Era

Imagine redesigning your most important processes from a blank sheet of paper, with no legacy systems to hold you back. Alpa Lally, Chief Product Officer at Grant Thornton, shares how the company is transforming into a frontier firm by reimagining core processes instead of automating them. She unpacks how embedded analytics reshapes client conversations, why AI governance is what helps you move faster, and how to reinvent your career in the age of AI. Key Moments: From Structural Engineering to Product Leadership (01:36): Alpa explains how designing buildings taught her the vision, strategy, and foundation-up thinking she now applies to product. Becoming a Frontier Firm (06:27): Rethinking how work gets done for 13,000-plus professionals means recognizing that automating a broken process just does the wrong thing faster. Embedding ThoughtSpot Analytics (14:57): By putting insights in auditors' hands on day one, Grant Thornton shifts client conversations from data gathering to strategy. The AI Proof Gap: Governance Enables Speed (19:32): Alpa reframes governance as what lets you move faster, sharing that 78% of firms doubt they'd pass an AI governance audit. Is Product Management Dead? (24:33): The role is evolving, not dying, and AI raises the value of deciding which opportunities are actually worth pursuing. Key Quotes: “A frontier firm uses AI not just to improve how work gets done, but fundamentally rethinks how the work gets done. It's moving beyond productivity gains and creating entirely new ways to deliver value.” - Alpa Lally “The challenge is if you simply automate a broken process, you end up doing the wrong thing faster.” - Alpa Lally “What ThoughtSpot has done is [give us] our ability to embed them immediately into the flow [which] has opened up a whole level of different insights that we can offer, not to our practitioners only, but imagine an auditor showing up on day one to your client and having these insights already calculated, already delivered.” - Alpa Lally Mentions Grant Thornton’s 2026 AI Impact Survey Report Wall Street is debating the AI buildout. Enterprises just answered: More than 80% say their GPUs run at half capacity or less Hers for the Taking by Tracey Newell Guest Bio Alpa Lally is the Chief Product Officer at Grant Thornton. Alpa is an experienced product management executive with several years in designing, developing, and launching consumer and business products for large to mid-sized to small organizations, fintechs, startups, and Fortune 500. She has led global cross-functional teams to drive high growth targets while staying laser-focused on solving customer and consumer problems. Alpa leverages data and analytics to drive successful business outcomes that deliver results and achieve corporate growth goals. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.
7月22日

How Google Is Building Enterprise Trust in Agentic AI

For most enterprises, autonomous AI agents still feel like a risk waiting to happen. Andi Gutmans, Vice President and General Manager for Data Cloud at Google, joins Cindi Howson to explain what it takes to build the data foundation that trustworthy agentic AI depends on. He breaks down how organizations can finally activate the 90 percent of enterprise data that's unstructured, why tokenmaxxing is the wrong way to measure AI value, and how open standards like Apache Iceberg are helping leaders tear down fragmented, multi-cloud data silos and unify data across clouds. Key Moments: Perception vs. Reality in Trusting Autonomous AI (03:55): Waymo is safer than a human driver by the numbers, but most people still choose Uber. Andi explains why closing that trust gap takes time. Activating the 90% of Unstructured Data (09:40): Most enterprise data sits in images, PDFs, and contracts, never touched by traditional analytics. Andi explains how AI finally makes that dark data usable. Crawl, Walk, Run: Scaling Autonomous Agents in Production (16:37): Virgin Voyages cut weather disruption rebooking from six hours to eleven minutes with autonomous agents. Andi explains the thresholds that made scaling safe. Token Efficiency Over Tokenmaxxing (19:54): Andi pushes back on tokenmaxxing as a measure of progress. Real success means the best outcome with the fewest tokens and the cheapest model for the job. Breaking Down Data Silos with a Borderless, Open Lakehouse (27:01): Most enterprises run on a messy mix of multiple clouds. Andi explains how open standards like Apache Iceberg tear down those walls. Key Quotes: “Over ninety percent of enterprise data is actually unstructured data... AI is really good at reasoning around this unstructured data, and so we can make a hundred percent of the data state light up.” - Andi Gutmans “If you take a Waymo, you're actually eighty percent less likely to get into a hurtful accident than if you take a service with a human driver. Still, most people prefer to take the Uber versus the Waymo… It's going to take time to earn the trust to understand what quality outcomes we can drive.” - Andi Gutmans “ The way we're thinking about the data platform is the best context is the context that drives the outcomes you need with the least amount of tokens and processing.” - Andi Gutmans Mentions Blue Ocean Strategy by W. Chan Kim and Renée Mauborgne Virgin Voyages case study (Google Cloud) Vodafone, Google Cloud and TM Forum Unveil Framework for Self-Optimising Autonomous Networks Guest Bio Andi Gutmans is the General Manager and Vice President for Data Cloud at Google, which includes operational databases, analytical data platforms, open lakehouse technologies, and Business Intelligence. His core focus involves building, managing, and scaling highly innovative data services to deliver a leading AI-native data platform for businesses. Before joining Google Cloud, Gutmans was VP Analytics at AWS, where he managed services such as Amazon Redshift, AWS Glue, and Amazon EMR. Prior to that, he co-founded and served as CEO of Zend Technologies, a company built around the open-source PHP language, of which he was a co-creator. Andi Gutmans is a highly experienced leader with over 20 years dedicated to open source contributions. He is particularly renowned as a co-creator of the open-source PHP language, which has become one of the most widely used languages for web development. He is an emeritus member of the Apache Software Foundation and served on the Eclipse Foundation’s board of directors. Gutmans holds a bachelor's degree in Computer Science from the Technion, Israel Institute of Technology. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.
7月8日

How Swire Coca-Cola Turned Governance Into an AI Growth Engine

For most data leaders, governance feels like the thing standing between them and progress. Bharathi Rajan, Vice President of Digital, AI, and Data at Swire Coca-Cola, joins Cindi Howson to share how she built the data foundation powering a $3 billion supply chain operation. She breaks down how to turn data quality into a business accelerator, get ahead of demand signals, and build the foundation every AI initiative actually depends on. Key Moments: Building a Data Foundation Across a $3B Supply Chain (06:39): The starting point wasn't strategy. It was figuring out where the data was, who had it, and how to get it into the hands of actual decision-makers. Reframing Data Governance (10:22): Governance is the foundation that makes better decisions possible. Bharathi shares how education and relationship-building drove the mindset shift at Swire Coca-Cola. AI as an Enablement Factor (11:50): Bharathi reframes AI as a tool for operational efficiency and more impactful work, not a headcount threat. Building the Plant of the Future (14:41): A new $475 million Colorado facility gives Swire Coca-Cola a rare chance to design data and AI infrastructure from scratch. The Skills That Will Matter Most in an AI World (17:58): Bharathi breaks down what she tells young people and aspiring data leaders about building a career that AI can't replace. Key Quotes: “Governance is not red tape. It is important. If you want to make decisions with the right data, you need to have governance. You have to have that quality data flowing in.” - Bharathi Rajan “If you're passionate about something and then you know how to use technology to enable that, that makes the difference.” - Bharathi Rajan “AI, it's an enablement factor for us internally as to how can I help the enterprise really grow, create operational efficiencies, but also have people do more impactful work.” - Bharathi Rajan Mentions Swire Coca-Cola to Build $475 Million Bottling Plant in Colorado Springs, CO DataIQ100: The most influential people in data and AI 2025 AI Women Power List Honorees The Let Them Theory by Mel Robbins Guest Bio Bharathi Rajan is a results-driven and experienced Chief Data Officer and AI strategist. In her current role as Vice President Digital, AI & Data at Swire Coca-Cola, USA, Bharathi has saved multi-millions by adopting new tech stack and bringing in capabilities critical for Enterprise growth and performance. While consistently leading innovation and enabling AI & data literacy, Bharathi consecutively drives data and systems architecture confluence across the enterprise. Prior to joining Swire Coca-Cola, USA, Bharathi operated as a Senior Director of Operations in Data and Infrastructre for three years, focusing on Enterprise Reporting and Analysis (ERA), where she was specifically hired for her distinctive capabilities in data strategy, cloud migration, and creative efficiencies. Bharathi is currently ranked #3 DataIQ100 North America for 2026. She has attended several panel discussions, in Emory University, Women in Tech, AI & Manufacturing and DataIQ. In 2026, Bharathi delivered a keynote on Data Leader as the Transformation Architect at the DataIQ summit. Additionally, Bharathi has spoken at various summits like Microsoft Ignite, Databricks, AI&Data summit and Snowflake summit over the years. Bharathi is also a mentor with WLDA Ventures and Women Tech Council. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.
6月24日

How to Transform a SaaS Company with AI from ELMO CTO

What happens when a software company building AI tools for HR teams uses those same tools to transform itself? Josh McKenzie, Chief Technology Officer at ELMO Software Group, shares how his team rebuilt their entire software development lifecycle around AI agents and redrew the boundaries of every engineering role. He breaks down how to lead that shift without losing people's trust, why domain expertise is the real SaaS moat, and how the right analytics partner unlocks decisions HR teams have never been able to make before. Key Moments: The SaaS Moat: What AI Can't Erode (06:37): Josh argues SaaS value runs deeper than software. Accountability, compliance, and domain expertise keep purpose-built platforms irreplaceable. How ELMO's AI Journey Started (10:23): ELMO started by mapping every role against AI impact. Turning that lens on their own engineering team set the full transformation in motion. Why ELMO Chose ThoughtSpot Over Building Its Own Analytics (18:42): A homegrown tool requiring too much user expertise led ELMO to look elsewhere. ThoughtSpot Spotter and natural language capabilities closed the gap. Why HR Teams Are the Most Underserved (20:21): Payroll here, benchmarking data there, performance data somewhere else. HR teams have been drowning in spreadsheet hell for years. Josh explains how AI finally closes that gap. From Engineer to CTO: Build a Team of Complements (24:17): Josh reflects on the mindset shift that defined his path to the C-suite. Great leadership means building a team whose strengths cover your blind spots. Key Quotes: “ ThoughtSpot was particularly interesting for us… The big thing for us was the Spotter product. Allowing users to bridge that data analyst gap was really important. So, that product has yielded really, really great results for us.” - Josh McKenzie “I think it's really important that we instill a culture where it's okay to fail, and it's okay to make a mistake. You want to be vocal about your mistakes so others don't repeat the same mistake.” - Josh McKenzie “My belief is you want to focus on your secret sauce. So, what is the thing that makes your business super successful? And for us, that's where we came to look at ThoughtSpot. It has a really nice visual user interface and allows you to create some great dashboards.” - Josh McKenzie Mentions Hiring and Onboarding Taking Longer Despite Widespread AI Adoption, New Australian Research Finds The 5 Levels of AI Coding (Why Most of You Won't Make It Past Level 2) WireGuard: Next Generation Kernel Network Tunnel | Jason A. Donenfeld Guest Bio As the Chief Technology Officer, Josh McKenzie is responsible for both technical strategy and delivery (build, release and operation) of the ELMO product suite. Josh has a proven track record of successfully leading technology teams and implementing transformative strategies that enhance efficiency, drive growth, and elevate overall technological capabilities. Josh has 20 years of experience in technology, primarily in FinTech. Before joining ELMO in 2024, Josh held executive and senior positions at Lendi Group, OFX, ASX and Westpac. Josh holds a Bachelor of Computer Science from the University of Newcastle and an MBA from the University of Sydney. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.
6月10日

How AI is Scaled Across Global Supply Chains with Ligentia

Explore how a global supply chain company turned its data platform into a customer-facing product designed to operate at the speed of disruption. Boris Rabkin, Chief Information Officer at Ligentia, shares how the company executed that shift through a deliberate phased approach and a partnership with ThoughtSpot. He breaks down how to build a data foundation that scales, what it takes to embed analytics where decisions happen, and how to structure AI ownership and governance across a global regulatory environment. Key Moments: From Reactive to Proactive with Agentic AI (04:46): Supply chain disruption response has changed from slow email chains and fragmented data to agentic systems that flag issues and test decisions in real time. Boris illustrates how Ligentia navigated that shift firsthand. Embedding Analytics Into the Customer Platform (09:00): Boris explains why bolting analytics onto a separate tool creates friction and why embedding intelligence directly into the existing customer platform is the better call. How to Phase a Data Transformation That Sticks (12:12): Boris outlines three phases: stabilize the foundation, standardize definitions, then build a usable experience. Skipping the plumbing is where most transformations fail. Where AI Ownership Really Belongs in the Enterprise (14:03): Understand why AI ownership should sit where value is created. Learn how centralized governance ensures data accuracy and security across the organization. What the Asyad Acquisition Unlocks for Ligentia (22:49): Boris shares how the new investment opens doors to scale the platform globally, automate logistics workflows, and monetize data beyond services. Key Quotes: “ We wanted to control the brand experience, the same login for our customers. Removing the friction and having the experience of being in one trusted platform for making those decisions… This is where [ThoughtSpot] came in.” - Boris Rabkin “I think AI should be owned where value is created. It shouldn't be a centralized function inside a lab. If it's not close to the product and the people that are using it, AI won't create the value.” - Boris Rabkin “Speed is one thing, but confidence in the data is something that really drives decisions.” - Boris Rabkin Mentions The EU AI Act’s ‘Wait and See’ Window Is Closing Asyad Group and Ligentia Join Forces to Accelerate Global Growth and Enhance Technology-Driven Supply Chain Solutions ThoughtSpot Supply Chain Solutions & Case Studies The Acquired Podcast: Formula 1 | From Bankrupt Teams to a Global Sports Empire The Acquired Podcast: Costco | How a Wholesale Club Built a Customer Fanaticism Guest Bio Boris Rabkin is the Chief Information Officer at Ligentia. As a Chief Information Officer and Board Member, he brings a distinctive blend of strategic vision and execution capabilities to drive business growth and operational excellence through digital transformation. With extensive experience leading global teams and technology initiatives, Boris is driven by a passion for leveraging data, AI, and automation to build scalable, secure, and resilient enterprises that deliver lasting value. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.
5月27日

Why Most Enterprise AI Pilots Fail: Lessons on Trust and Deployment

Understand how to close the gap between AI experimentation and enterprise production. Shub Agarwal, Founder of the AI Trust Lab at USC and author of Successful AI Product Creation: A Nine-Step Framework, shares his AI product management framework for taking enterprise AI strategy from demo to production, drawing on two decades of product leadership at Amazon and Fortune 50 firms. He breaks down why experimentation must tie directly to business OKRs, the four mindset shifts leaders need to scale AI responsibly, and how the AI Trust Lab is building a benchmark evaluation framework for AI model trust and governance. Key Moments: Why 80% of AI Projects Never Reach Production (02:13): Shub traces the root cause of stalled AI programs to a missing system for moving from demo to deployment. Most teams have no repeatable path to production. Shub's Nine-Step Framework for Building AI Products (06:00): Most AI projects start with a cool model instead of a painful problem. Shub walks through the three phases of his framework: discovery, execution, and excellence. The Case Against "Fix Your Data First" (12:41): Conventional wisdom says clean your data before building AI. Shub challenges that, arguing modern LLMs offer far more flexibility with imperfect data. Four Mindset Shifts for Scaling Enterprise AI (16:35): Shub outlines the four shifts separating organizations that scale AI from those that stall, from measuring AI performance differently to embedding trust from day one. Inside Shub's AI Trust Lab at USC (23:54): Major foundation models are already being benchmarked on trust and safety. Shub explains the lab's mission to build a standardized evaluation framework for AI model governance. Why Enterprise AI Governance Needs Multiple Disciplines (28:36): AI models can be sycophantic, manipulative, or lack candor. Shub argues that building trustworthy AI demands an interdisciplinary approach. Key Quotes: “I think the fundamental problem that organizations are facing today… is not that they have a lack of experimentation in the demo aspect. The challenge is they don't know how to take those demos to production, and that is where I saw the gap.” - Shub Agarwal “I do think data is the fuel for AI… But I think today organizations are crippled by this ‘fix your data, and then we'll build AI’, and they never build AI. They never build use cases that are adding value.” - Shub Agarwal “There's no FICO scores for models, so I decided to create one. I built this lab… bringing the computer scientists, the researchers, the applied AI researchers, the policy, and the communication people together to think of what is trust, define it, and ultimately measure and evaluate it.” - Shub Agarwal Mentions USC AI Trust Hub Successful AI Product Creation: A Nine-Step Framework by Shub Agarwal Four Steps to Epiphany: Successful Strategies for Products That Win by Steve Blank Masters of Scale podcast with Reid Hoffman Guest Bio Shub Agarwal is an associate professor of professional practice at the University of Southern California, an industry executive, and an advisor to start-ups and academic institutions. He holds an MBA from the University of California, Los Angeles (UCLA), and an MS from Carnegie Mellon University (CMU). He is the author of two books: Solve Catch-22 of Product Management and Successful AI Product Creation: A 9-Step Framework. He has made significant contributions to the fields of artificial intelligence and machine learning, holding several U.S. and global patents for his work, and is also a published author of several technical research papers. With around two decades of extensive experience in product management and leadership, his journey has been marked by a relentless pursuit of leveraging AI technologies to create impactful products that redefine industry standards. His industry experience includes leadership roles at Amazon, Silicon Valley start-ups, and other Fortune 50 firms. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

すべて見る（145）

Meet the world’s top data and AI leaders transforming how we do business. Hear case studies, industry insights, and personal lessons from the executives leading the data and AI 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. [CLAIM:FI41QQVR]