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DataFramed

The concept of sovereign AI is becoming increasingly critical in our interconnected world. Nations and organizations are grappling with who controls the data, infrastructure, and technology that power artificial intelligence systems. But what does this mean for your work in data science and AI implementation? How do you navigate the complex landscape of data ownership when building AI solutions? As geopolitical tensions influence technology development, understanding the nuances of AI sovereignty isn't just for governments—it's essential for anyone working with data and AI systems to ensure resilience and compliance in an uncertain future.

Russ Wilcox is the CEO of ArtifexAI, advising organizations on technology strategy, AI governance, and policy analysis. With 16 years in machine learning and AI, he focuses on translating complex policy and emerging tech trends into actionable strategy. His work spans government, infrastructure, and enterprise, with a focus on connecting technical capabilities to real-world implementation. A two-time World Economic Forum speaker and TEDx presenter, Wilcox has advised government agencies and Fortune 500 companies on AI strategy, urban intelligence, and technology policy. He also serves on AI ethics boards, lectures at UCLA and Boston University, and develops NLP systems for public- and private-sector use. Russ provides strategic consulting and speaking on AI governance, technology competition, and sustainable infrastructure.

In the episode, Richie and Russ explore the US-China AI race, the philosophical differences in AI approaches, the concept of sovereign AI, the role of data sovereignty, and the potential for AI to transform infrastructure and governance, and much more.

Links Mentioned in the Show:

ArtifexAIRuss’ Website • Connect with 
RussAI-Native Course: Intro to AI for WorkRelated Episode: Harnessing AI to Help Humanity with Sandy Pentland, HAI Fellow at StanfordRewatch RADAR AI • 

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#336 From City Sewers to Sovereign AI with Russ Wilcox, CEO at ArtifexAI

The internet has transformed how we access information, but it's also created unprecedented challenges around trust and reliability. How do we build digital spaces where collaboration thrives and quality information prevails? What separates toxic online environments from productive ones? The principles of neutrality, transparency, and assuming good faith have proven essential in creating sustainable knowledge communities. But these same principles extend far beyond the digital realm—they're fundamental to effective leadership, successful business relationships, and even political discourse. When trust breaks down, everything becomes more difficult. So what practical steps can we take to foster trust in our organizations and communities?

Jimmy Wales is an American-British internet entrepreneur best known as the founder of Wikipedia and co-founder of Fandom. Trained in finance at Auburn University and the University of Alabama, he began his career in quantitative finance before moving into early web ventures, including Bomis and the free encyclopedia project Nupedia. In 2001, he launched Wikipedia, which quickly became one of the most visited websites in the world. To support its growth, he established the Wikimedia Foundation in 2003, where he continues to serve on the Board of Trustees and act as a public spokesperson. He later co-founded Fandom in 2004, expanding the wiki model to entertainment, gaming, and niche communities. Wales has also pursued experiments in collaborative journalism, including WikiTribune and its successor WT Social. His work in open knowledge has earned recognition from organizations such as the World Economic Forum, Time magazine, UNESCO, and the Electronic Frontier Foundation. He has held fellowships and board roles at institutions including Harvard’s Berkman Center and Creative Commons.

In the episode, Richie and Jimmy explore the early challenges of Wikipedia, the importance of trust and neutrality, the role of AI in content creation, and much more.

Links Mentioned in the Show:

WikipediaJimmy’s New Book: The Seven Rules of TrustTrust Café • Connect with 
JimmyBlog: The Trust Triangle of LeadershipAI-Native Course: Intro to AI for WorkRelated Episode: How to Build AI Your Users Can Trust with David Colwell, VP of AI & ML at TricentisExplore AI-Native Learning on DataCamp

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#335 Rebuilding Trust in the Digital Age with Jimmy Wales, Founder at Wikipedia

The AI landscape is evolving at breakneck speed, with new capabilities emerging quarterly that redefine what's possible. For professionals across industries, this creates a constant need to reassess workflows and skills. How do you stay relevant when the technology keeps leapfrogging itself? What happens to traditional roles when AI can increasingly handle complex tasks that once required specialized expertise? With product-market fit becoming a moving target and new positions like forward-deployed engineers emerging, understanding how to navigate this shifting terrain is crucial. The winners won't just be those who adopt AI—but those who can continuously adapt as it evolves.

Tomasz Tunguz is a General Partner at Theory Ventures, a $235m early-stage venture capital firm. He blogs at tomtunguz.com & co-authored Winning with Data. He has worked or works with Looker, Kustomer, Monte Carlo, Dremio, Omni, Hex, Spot, Arbitrum, Sui & many others. He was previously the product manager for Google's social media monetization team, including the Google-MySpace partnership, and managed the launches of AdSense into six new markets in Europe and Asia. Before Google, Tunguz developed systems for the Department of Homeland Security at Appian Corporation.

In the episode, Richie and Tom explore the rapid investment in AI, the evolution of AI models like Gemini 3, the role of AI agents in productivity, the shifting job market, the impact of AI on customer success and product management, and much more.

Links Mentioned in the Show:

Theory Ventures • Connect with 
TomTom’s BlogGavin Baker on MediumAI-Native Course: Intro to AI for WorkRelated Episode: Data & AI Trends in 2024, with Tom Tunguz, General Partner at Theory VenturesRewatch RADAR AI • 

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#334 The State of Data & AI with Tom Tunguz, VC at Theory Ventures

Data science leadership is about more than just technical expertise—it’s about building trust, embracing AI, and delivering real business impact. As organizations evolve toward AI-first strategies, data teams have an unprecedented opportunity to lead that transformation. But how do you turn a traditional analytics function into an AI-driven powerhouse that drives decision-making across the business? What’s the right structure to balance deep technical specialization with seamless business integration? From building credibility through high-impact forecasting to creating psychological safety around AI adoption, effective data leadership today requires both technical rigor and visionary communication. The landscape is shifting fast, but with the right approach, data science can stand as a true pillar of innovation alongside engineering, product, and design.

Bilal Zia is currently the Head of Data Science & Analytics at Duolingo, an EdTech company whose mission is to develop the best education in the world and make it universally available. Previously, he spent two years helping to build and lead an interdisciplinary Central Science team at Amazon, comprising economists, data and applied scientists, survey specialists, user researchers, and engineers. Before that, he spent fifteen years in the Research Department of the World Bank in Washington, D.C., pursuing an applied academic career. He holds a Ph.D. in Economics from the Massachusetts Institute of Technology, and his interests span economics, data science, machine learning/AI, psychology, and user research.

In the episode, Richie and Bilal explore rebuilding an underperforming data team, fostering trust with leadership, embedding data scientists within product teams, leveraging AI for productivity, the future of synthetic A/B testing, and much more.

Links Mentioned in the Show:

DuolingoDuolingo Blog: How machine learning supercharged our revenue by millions of dollars • Connect with 
BilalAI-Native Course: Intro to AI for WorkRelated Episode: The Future of Data & AI Education Just Arrived with Jonathan Cornelissen & Yusuf SaberRewatch RADAR AI • 

New to DataCamp?

 • Learn on the go using the
 DataCamp mobile app • Empower your business with world-class data and AI skills with
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#333 Creating an AI-First Data Team with Bilal Zia, Head of Data Science & Analytics at DuoLingo

The relationship between data governance and AI quality is more critical than ever. As organizations rush to implement AI solutions, many are discovering that without proper data hygiene and testing protocols, they're building on shaky foundations. How do you ensure your AI systems are making decisions based on accurate, appropriate information? What benchmarking strategies can help you measure real improvement rather than just increased output? With AI now touching everything from code generation to legal documents, the consequences of poor quality control extend far beyond simple errors—they can damage reputation, violate regulations, or even put licenses at risk.

David Colwell is the Vice President of Artificial Intelligence and Machine Learning at Tricentis, a global leader in continuous testing and quality engineering. He founded the company’s AI division in 2018 with a mission to make quality assurance more effective and engaging through applied AI innovation. With over 15 years of experience in AI, software testing, and automation, David has played a key role in shaping Tricentis’ intelligent testing strategy. His team developed Vision AI, a patented computer vision–based automation capability within Tosca, and continues to pioneer work in large language model agents and AI-driven quality engineering. Before joining Tricentis, David led testing and innovation initiatives at DX Solutions and OnePath, building automation frameworks and leading teams to deliver scalable, AI-enabled testing solutions. Based in Sydney, he remains focused on advancing practical, trustworthy applications of AI in enterprise software development.

In the episode, Richie and David explore AI disasters in legal settings, the balance between AI productivity and quality, the evolving role of data scientists, and the importance of benchmarks and data governance in AI development, and much more.

Links Mentioned in the Show:

Tricentis 2025 Quality Transformation Report • Connect with 
DavidCourse: Artificial Intelligence (AI) LeadershipRelated Episode: Building & Managing Human+Agent Hybrid Teams with Karen Ng, Head of Product at HubSpotRewatch RADAR AI • 

New to DataCamp?

 • Learn on the go using the
 DataCamp mobile app • Empower your business with world-class data and AI skills with
 DataCamp for business

#332 How to Build AI Your Users Can Trust with David Colwell, VP of AI & ML at Tricentis

The future of education is being reshaped by AI-powered personalization. Traditional online learning platforms offer static content that doesn't adapt to individual needs, but new technologies are creating truly interactive experiences that respond to each learner's context, pace, and goals. How can personalized AI tutoring bridge the gap between mass education and the gold standard of one-on-one human tutoring? What if every professional could have a private tutor that understands their industry, role, and specific challenges? As organizations invest in upskilling their workforce, the question becomes: how can we leverage AI to make learning more engaging, effective, and accessible for everyone?

As the Co-Founder & CEO of DataCamp, Jonathan Cornelissen has helped grow DataCamp to upskill over 10M+ learners and 2800+ teams and enterprise clients. He is interested in everything related to data science, education, and entrepreneurship. He holds a Ph.D. in financial econometrics and was the original author of an R package for quantitative finance.

Yusuf Saber is a technology leader and entrepreneur with extensive experience building and scaling data-driven organizations across the Middle East. He is the Founder of Optima and a Venture Partner at COTU Ventures, with previous leadership roles at talabat, including VP of Data and Senior Director of Data Science and Engineering. Earlier in his career, he co-founded BulkWhiz and Trustious, and led data science initiatives at Careem. Yusuf holds research experience from ETH Zurich and began his career as an engineering intern at Mentor Graphics.

In the episode, Richie, Jo and Yusuf explore the innovative AI-driven learning platform Optima, its unique approach to personalized education, the potential for AI to enhance learning experiences, the future of AI in education, the challenges and opportunities in creating dynamic, context-aware learning environments, and much more.

Links Mentioned in the Show:

 • Read more about 
the announcementTry the AI-Native Courses:Intro to SQLIntro to AI for Work

New to DataCamp?

 • Learn on the go using the
 DataCamp mobile app • Empower your business with world-class data and AI skills with
 DataCamp for busines

#331 The Future of Data & AI Education Just Arrived with Jonathan Cornelissen & Yusuf Saber

Data storytelling isn't just about presenting numbers—it's about creating shared wisdom that drives better decision-making. In our increasingly polarized world, we often miss that most people actually have reasonable views hidden behind the loudest voices. But how can technology help us cut through the noise and build genuine understanding? What if AI could help us share stories across different communities and contexts, making our collective knowledge more accessible? From reducing unnecessary meetings to enabling more effective collaboration, the way we exchange information is evolving rapidly. Are you prepared for a future where AI helps us communicate more effectively rather than replacing human judgment?

Professor Alex “Sandy” Pentland is a leading computational scientist, co-founder of the MIT Media Lab and Media Lab Asia, and a HAI Fellow at Stanford. Recognized by Forbes as one of the world’s most powerful data scientists, he played a key role in shaping the GDPR through the World Economic Forum and contributed to the UN’s Sustainable Development Goals as one of the Secretary General’s “Data Revolutionaries.” His accolades include MIT’s Toshiba Chair, election to the U.S. National Academy of Engineering, the Harvard Business Review McKinsey Award, and the DARPA 40th Anniversary of the Internet Award. Pentland has served on advisory boards for organizations such as the UN Secretary General, UN Foundation, Consumers Union, and formerly for the OECD, Google, AT&T, and Nissan. Companies originating from his lab have driven major innovations, including India’s Aadhaar digital identity system, Alibaba’s news and advertising arm, and the world’s largest rural health service network.

His more recent ventures span mental health (Ginger.io), AI interaction management (Cogito), delivery optimization (Wise Systems), financial privacy (Akoya), and fairness in social services (Prosperia). A mentor to over 80 PhD students—many now leading in academia, research, or entrepreneurship—Pentland helped pioneer fields such as computational social science, wearable computing, and modern biometrics. His books include Social Physics, Honest Signals, Building the New Economy, and Trusted Data.

In the episode, Richie and Sandy explore the role of storytelling in data and AI, how technology reshapes our narratives, the impact of AI on decision-making, the importance of shared wisdom in communities, and much more.

Links Mentioned in the Show:

MIT Media LabSandy’s Booksdeliberation.io • Connect with 
SandySkill Track: Artificial Intelligence (AI) LeadershipRelated Episode: The Human Element of AI-Driven Transformation with Steve Lucas, CEO at BoomiRewatch RADAR AI • 

New to DataCamp?

 • Learn on the go using the
 DataCamp mobile app • Empower your business with world-class data and AI skills with
 DataCamp for business

#330 Harnessing AI to Help Humanity with Professor Sandy Pentland, HAI Fellow at Stanford, Co-founder of MIT Media Lab

Data quality and AI reliability are two sides of the same coin in today's technology landscape. Organizations rushing to implement AI solutions often discover that their underlying data infrastructure isn't prepared for these new demands. But what specific data quality controls are needed to support successful AI implementations? How do you monitor unstructured data that feeds into your AI systems? When hallucinations occur, is it really the model at fault, or is your data the true culprit? Understanding the relationship between data quality and AI performance is becoming essential knowledge for professionals looking to build trustworthy AI systems.

Shane Murray is a seasoned data and analytics executive with extensive experience leading digital transformation and data strategy across global media and technology organizations. He currently serves as Senior Vice President of Digital Platform Analytics at Versant Media, where he oversees the development and optimization of analytics capabilities that drive audience engagement and business growth. In addition to his corporate leadership role, he is a founding member of InvestInData, an angel investor collective of data leaders supporting early-stage startups advancing innovation in data and AI. Prior to joining Versant Media, Shane spent over three years at Monte Carlo, where he helped shape AI product strategy and customer success initiatives as Field CTO.

Earlier, he spent nearly a decade at The New York Times, culminating as SVP of Data & Insights, where he was instrumental in scaling the company’s data platforms and analytics functions during its digital transformation. His earlier career includes senior analytics roles at Accenture Interactive, Memetrics, and Woolcott Research. Based in New York, Shane continues to be an active voice in the data community, blending strategic vision with deep technical expertise to advance the role of data in modern business.

In the episode, Richie and Shane explore AI disasters and success stories, the concept of being AI-ready, essential roles and skills for AI projects, data quality's impact on AI, and much more.

Links Mentioned in the Show:

Versant Media • Connect with 
ShaneCourse: Responsible AI PracticesRelated Episode: Scaling Data Quality in the Age of Generative AI with Barr Moses, CEO of Monte Carlo Data, Prukalpa Sankar, Cofounder at Atlan, and George Fraser, CEO at FivetranRewatch RADAR AI • 

New to DataCamp?

 • Learn on the go using the
 DataCamp mobile app • Empower your business with world-class data and AI skills with
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#329 Building Trust in AI Agents with Shane Murray, Senior Vice President of Digital Platform Analytics at Versant Media

The promise of AI in enterprise settings is enormous, but so are the privacy and security challenges. How do you harness AI's capabilities while keeping sensitive data protected within your organization's boundaries? Private AI—using your own models, data, and infrastructure—offers a solution, but implementation isn't straightforward. What governance frameworks need to be in place? How do you evaluate non-deterministic AI systems? When should you build in-house versus leveraging cloud services? As data and software teams evolve in this new landscape, understanding the technical requirements and workflow changes is essential for organizations looking to maintain control over their AI destiny.

Manasi Vartak is Chief AI Architect and VP of Product Management (AI Platform) at Cloudera. She is a product and AI leader with more than a decade of experience at the intersection of AI infrastructure, enterprise software, and go-to-market strategy. At Cloudera, she leads product and engineering teams building low-code and high-code generative AI platforms, driving the company’s enterprise AI strategy and enabling trusted AI adoption across global organizations. Before joining Cloudera through its acquisition of Verta, Manasi was the founder and CEO of Verta, where she transformed her MIT research into enterprise-ready ML infrastructure. She scaled the company to multi-million ARR, serving Fortune 500 clients in finance, insurance, and capital markets, and led the launch of enterprise MLOps and GenAI products used in mission-critical workloads. Manasi earned her PhD in Computer Science from MIT, where she pioneered model management systems such as ModelDB — foundational work that influenced the development of tools like MLflow. Earlier in her career, she held research and engineering roles at Twitter, Facebook, Google, and Microsoft.

In the episode, Richie and Manasi explore AI's role in financial services, the challenges of AI adoption in enterprises, the importance of data governance, the evolving skills needed for AI development, the future of AI agents, and much more.

Links Mentioned in the Show:

ClouderaCloudera Evolve ConferenceCloudera Agent Studio • Connect with 
ManasiCourse: Introduction to AI AgentsRelated Episode: RAG 2.0 and The New Era of RAG Agents with Douwe Kiela, CEO at Contextual AI & Adjunct Professor at Stanford UniversityRewatch RADAR AI • 

New to DataCamp?

 • Learn on the go using the
 DataCamp mobile app • Empower your business with world-class data and AI skills with
 DataCamp for business

#328 The Challenges of Enterprise Agentic AI with Manasi Vartak, Chief AI Architect at Cloudera

The journey from startup to billion-dollar enterprise requires more than just a great product—it demands strategic alignment between sales and marketing. How do you identify your ideal customer profile when you're just starting out? What data signals help you find the twins of your successful early adopters? With AI now automating everything from competitive analysis to content creation, the traditional boundaries between departments are blurring. But what personality traits should you look for when building teams that can scale with your growth? And how do you ensure your data strategy supports rather than hinders your AI ambitions in this rapidly evolving landscape?

Denise Persson is CMO at Snowflake and has 20 years of technology marketing experience at high-growth companies. Prior to joining Snowflake, she served as CMO for Apigee, an API platform company that went public in 2015 and Google acquired in 2016. She began her career at collaboration software company Genesys, where she built and led a global marketing organization. Denise also helped lead Genesys through its expansion to become a successful IPO and acquired company. Denise holds a BA in Business Administration and Economics from Stockholm University, and holds an MBA from Georgetown University.

Chris Degnan is the former CRO at Snowflake and has over 15 years of enterprise technology sales experience. Before working at Snowflake, Chris served as the AVP of the West at EMC, and prior to that as VP Western Region at Aveksa, where he helped grow the business 250% year-over-year. Before Aveksa, Chris spent eight years at EMC and managed a team responsible for 175 select accounts. Prior to EMC, Chris worked in enterprise sales at Informatica and Covalent Technologies (acquired by VMware). He holds a BA from the University of Delaware.

In the episode, Richie, Denise, and Chris explore the journey to a billion-dollar ARR, the importance of customer obsession, aligning sales and marketing, leveraging data for decision-making, and the role of AI in scaling operations, and much more.

Links Mentioned in the Show:

SnowflakeSnowflake BUILD • Connect with 
Denise • and 
ChrisSnowflake is FREE on DataCamp this weekRelated Episode: Adding AI to the Data Warehouse with Sridhar Ramaswamy, CEO at SnowflakeRewatch RADAR AI • 

New to DataCamp?

 • Learn on the go using the
 DataCamp mobile app • Empower your business with world-class data and AI skills with
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#327 Building a Sales and Marketing Capability for Data Applications with Denise Persson, CMO at Snowflake, and Chris Degnan, former CRO at Snowflake

In this episode of DataFramed, we speak with Noah Gift, founder of Pragmatic AI Labs and prolific author about operationalizing machine learning in organizations and his new book Practical MLOPs. 

Throughout the episode, Noah discusses his background, his philosophy around pragmatic AI, the differences between data science in academia and the real world, how data scientists can become more action-oriented by creating solutions that solve real-world problems, the importance of dev-ops, his most recent book on the practical guide to MLOps, how data science can be compared to Brazilian jiu-jitsu, what data scientists should learn to scale the amount of value they deliver, his thoughts on auto-ml and automation, and more. 

Relevant links from the interview:

We’d love your feedback! Let us know which topics you’d like us to cover and what you think of DataFramed by answering this 30-second surveyUnsettled: What Climate Science Tells Us, What It Doesn't, and Why It MattersCheck out Noah's booksCheck out Noah's course on DataCampConnect with Noah on LinkedInGain access to DataCamp's full course library at a discount!

#71 Scaling Machine Learning Adoption: A Pragmatic Approach

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