#338 The New Paradigm for Enterprise AI Governance with Blake Brannon, Chief Innovation Officer at OneTrust

AI governance is becoming critical as organizations deploy more intelligent systems across their operations. With predictions of over a billion AI agents entering the workforce in the coming years, traditional governance approaches simply cannot keep pace. How do you ensure your AI systems are using data responsibly without slowing down innovation? What happens when an AI agent makes decisions that were never explicitly programmed? And how do you build governance processes that scale alongside rapidly expanding AI adoption while maintaining trust with customers and regulators?Blake Brannon is Chief Innovation Officer at OneTrust, where he leads product vision and strategic direction for the company’s AI-ready governance platform. He has been with OneTrust since 2017, previously serving as Chief Technology Officer, and has played a key role in scaling the platform to support privacy, data governance, risk, and responsible AI initiatives for large enterprises. Blake is based in Atlanta and holds an academic background from the Georgia Institute of Technology, with early research experience in network systems and wireless communications.In the episode, Richie and Blake explore AI governance disasters, the importance of consent and data use, the rise of AI agents, the challenges of scaling governance processes, the need for continuous observability, the role of governance committees, strategies for effective AI governance in organizations, and much more.Links Mentioned in the Show:OneTrustConnect with BlakeAI-Native Course: Intro to AI for WorkRelated Episode: From City Sewers to Sovereign AI with Russ Wilcox, CEO at ArtifexAIExplore AI-Native Learning on DataCampNew 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

2025 was the year AI stopped being a curiosity and started reshaping real work. From data analysts speeding up entire workflows in minutes, to managers learning how to lead hybrid teams of humans and agents, the pace of change has been relentless. Across DataFramed this year, one theme kept surfacing: AI isn’t replacing data professionals—it’s raising the bar on what good looks like. Skills are shifting, careers are becoming more fluid, and organizations are being forced to rethink how they build teams, make decisions, and govern technology that now reasons, plans, and acts on our behalf. This Best of 2025 episode pulls together the most important ideas, voices, and debates from a year that fundamentally changed how data and AI show up in practice.In this special year-end roundup, Richie revisits the standout moments from DataFramed in 2025, spanning careers, business intelligence, data literacy, AI agents, industry use cases, and responsible AI foundations. You’ll hear why the data analyst role is evolving rather than disappearing, how hybrid human–AI teams are becoming the norm, and why communication remains the most underrated skill in data careers, the state of BI and data storytelling, the shift from training to behavior change in data and AI literacy, the rapid rise of agentic systems powered by reasoning at inference time. We also dive into real-world applications across healthcare, finance, and enterprise operations, alongside hard truths about data quality, governance, and model lineage. Finally, we spotlight advances in data science, NLP, and synthetic data—rounding out a year defined by faster cycles, higher expectations, and a renewed focus on getting the fundamentals right as AI scales.Episodes Featured in this Recap:#326 Is the Data Analyst Role Dying Out? with Mo Chen, Data &amp; Analytics Manager at NatWest Group#319 Building &amp; Managing Human+Agent Hybrid Teams with Karen Ng, Head of Product at HubSpot#295 How To Get Hired As A Data Or AI Engineer with Deepak Goyal, CEO &amp; Founder at Azurelib Academy#294 Six Skills Data Professionals Need To Succeed with Abhijit Bhaduri, Brand Evangelist &amp; Former General Manager of Global L&amp;D at Microsoft#333 Creating an AI-First Data Team with Bilal Zia, Head of Data Science &amp; Analytics at DuoLingo#310 The State of BI in 2025 with Howard Dresner, Godfather of BI#306 The Next Generation of Business Intelligence with Colin Zima, CEO at Omni#298 Data Storytelling Skills to Increase Your Impact with Kat Greenbrook, Author of The Data Storyteller's Handbook#323 The Evolution of Data Literacy &amp; AI Literacy with Jordan Morrow, Godfather of Data Literacy#305 <a...

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’ WebsiteConnect with RussAI-Native Course: Intro to AI for WorkRelated Episode: Harnessing AI to Help Humanity with Sandy Pentland, HAI Fellow at StanfordRewatch RADAR AI&nbsp;New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

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 CaféConnect 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 &amp; ML at TricentisExplore AI-Native Learning on DataCampNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

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 &amp; co-authored Winning with Data. He has worked or works with Looker, Kustomer, Monte Carlo, Dremio, Omni, Hex, Spot, Arbitrum, Sui &amp; 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 VenturesConnect with TomTom’s BlogGavin Baker on MediumAI-Native Course: Intro to AI for WorkRelated Episode: Data &amp; AI Trends in 2024, with Tom Tunguz, General Partner at Theory VenturesRewatch RADAR AI&nbsp;New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

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 &amp; 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 dollarsConnect with BilalAI-Native Course: Intro to AI for WorkRelated Episode: The Future of Data &amp; AI Education Just Arrived with Jonathan Cornelissen &amp; Yusuf SaberRewatch RADAR AI&nbsp;New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

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 ReportConnect with DavidCourse: Artificial Intelligence (AI) LeadershipRelated Episode: Building &amp; Managing Human+Agent Hybrid Teams with Karen Ng, Head of Product at HubSpotRewatch RADAR AI&nbsp;New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

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 &amp; 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 WorkNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for busines

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&amp;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.ioConnect with SandySkill Track: Artificial Intelligence (AI) LeadershipRelated Episode: The Human Element of AI-Driven Transformation with Steve Lucas, CEO at BoomiRewatch RADAR AI&nbsp;New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

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 &amp; 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 MediaConnect 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&nbsp;New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business