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DataFramed

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 • 

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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 • 

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#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 • 

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 • Learn on the go using the
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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

The role of data analysts is evolving, not disappearing. With generative AI transforming the industry, many wonder if their analytical skills will soon become obsolete. But how is the relationship between human expertise and AI tools really changing? While AI excels at coding, debugging, and automating repetitive tasks, it struggles with understanding complex business problems and domain-specific challenges. What skills should today's data professionals focus on to remain relevant? How can you leverage AI as a partner rather than viewing it as a replacement? The balance between technical expertise and business acumen has never been more critical in navigating this changing landscape.

Mo Chen is a Data & Analytics Manager with over seven years of experience in financial and banking data. Currently at NatWest Group, Mo leads initiatives that enhance data management, automate reporting, and improve decision-making across the organization. After earning an MSc in Finance & Economics from the University of St Andrews, Mo launched a career in risk and credit portfolio management before transitioning into analytics. Blending economics, finance, and data engineering, Mo is skilled at turning large-scale financial data into actionable insight that supports efficiency and strategic planning. Beyond corporate life, Mo has become a passionate educator and community-builder. On YouTube, Mo hosts a fast-growing channel (185K+ subscribers, with millions of views) where he breaks down complex analytics concepts into bite-sized, actionable lessons.

In the episode, Richie and Mo explore the evolving role of data analysts, the impact of AI on coding and debugging, the importance of domain knowledge for career switchers, effective communication strategies in data analysis, and much more.

Links Mentioned in the Show:

Mo’s Website - Build a Data Portfolio WebsiteMo’s YouTube Channel • Connect with 
MoGet Certified as a Data AnalystRelated Episode: Career Skills for Data Professionals with Wes Kao, Co-Founder of MavenRewatch 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

#326 Is the Data Analyst Role Dying Out? with Mo Chen, Data & Analytics Manager at NatWest Group

Leadership in data-driven organizations requires a delicate balance of technical expertise and human understanding. As businesses navigate unprecedented uncertainty in global markets, geopolitics, and technological change, the role of data as a source of truth becomes increasingly vital. But how do you create a culture where data informs decisions at every level? What separates leaders who merely collect data from those who leverage it to drive bold, transformative action? For data professionals looking to advance their careers, the challenge extends beyond technical skills to understanding how data connects to broader business strategy and organizational purpose.

Carolyn Dewar is the founder and global co-leader of McKinsey & Company’s CEO Practice, where she partners with CEOs, founders, boards, and senior executives to help them maximize their effectiveness and lead their organizations through critical moments, including hypergrowth, transformation, crises, and mergers. Drawing on her extensive research and experience, Carolyn works with leaders across all stages of the CEO journey to drive large-scale organizational change, set bold strategies, and shape company culture to align leadership teams, manage external stakeholders, and optimize executive time and operating models. She helps CEOs develop the mindsets and frameworks needed to succeed in their role, ensuring they deliver lasting impact and sustainable growth.

A recognized thought leader, Carolyn is the co-author of CEO Excellence: The Six Mindsets That Distinguish the Best Leaders from the Rest (a New York Times bestseller) and A CEO for All Seasons: Mastering the Cycles of Leadership. She publishes the monthly Strategic CEO newsletter and has contributed over 30 articles to Harvard Business Review, The Conference Board, and McKinsey Quarterly. Carolyn is also a member of the McKinsey Global Institute Council, which advises on MGI’s research on global economic, business, and technology trends. With over 25 years of experience advising clients across industries, including financial services, technology, and consumer sectors, Carolyn is also a sought-after keynote speaker and panelist at global conferences.

In the episode, Richie and Carolyn explore common mistakes for CEOs, the unique responsibilities of a CEO, the importance of data-driven decision-making, fostering a data-centric culture, aligning data and business strategies, and much more.

Links Mentioned in the Show:

CEO Excellence: The Six Mindsets That Distinguish the Best Leaders from the Rest • Connect with 
CarolynSkill Track: Artificial Intelligence (AI) LeadershipRelated Episode: From Panic to Profit, Via Data with Bill Canady, CEO at Arrowhead Engineered ProductsRewatch RADAR AI • 

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 • Learn on the go using the
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#325 Using Data to Master the Cycles of Leadership with Carolyn Dewar, Global Practice Leader at McKinsey

Behavioral science is revolutionizing how businesses connect with customers and influence decisions. By understanding the psychological principles that drive human behavior, companies can create more effective marketing strategies and product experiences. But how can you apply these insights in your data-driven work? What simple changes could dramatically improve how your audience responds to your messaging? The difference between abstract and concrete language can quadruple memorability, and timing your communications around 'fresh start' moments can increase receptivity by over 50%. Whether you're designing user experiences or communicating insights, understanding these hidden patterns of human behavior could be your competitive advantage.

Richard Shotton is the founder of Astroten, a consultancy that applies behavioral science to marketing, helping brands of all sizes solve business challenges with insights from psychology. As a keynote speaker, he is known for exploring consumer psychology, the impact of behavioral experiments, and how biases shape decision-making. He began his career in media planning over 20 years ago, working on accounts such as Coca-Cola, Lexus, Halifax, Peugeot, and comparethemarket. He has since held senior roles including Head of Insight at ZenithOptimedia and Head of Behavioral Science at Manning Gottlieb, while also conducting experiments featured in publications such as Marketing Week, The Drum, Campaign, Admap, and Mediatel. Richard is the author of two acclaimed books: The Choice Factory (2018), which was named Best Sales & Marketing Book at the 2019 Business Book Awards and voted #1 in the BBH World Cup of Advertising Books; and The Illusion of Choice (2023), which highlights the most important psychological biases business leaders can harness for competitive advantage.

In the episode, the two Richards explore the power of behavioral science in marketing, the impact of visual language, the role of social proof, the importance of simplicity in communication, how biases influence decision-making, the fresh start effect, the ethical considerations of using behavioral insights, and much more.

Links Mentioned in the Show:

Richard’s Book—Hacking the Human Mind: The behavioral science secrets behind 17 of the world's best brandsAstrotenBlog: To create strong memories, use concrete language • Connect with 
RichardCourse: Marketing Analytics for BusinessRelated Episode: Career Skills for Data Professionals with Wes Kao, Co-Founder of MavenRewatch 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

#324 Using Behavioral Science to Hack Your Customers Minds with Richard Shotton, Founder at Astroten

Data literacy and AI literacy are becoming essential skills in today's digital landscape. As organizations collect more data and deploy AI solutions, the ability to understand, interpret, and make decisions with these tools is increasingly valuable. But how do we develop these skills effectively across an organization? What does successful implementation of data and AI literacy programs look like in practice? The journey to becoming data literate doesn't require becoming a data scientist—it's about building confidence and comfort with data in your specific role. From change management strategies to measuring real value, understanding how to foster these skills can transform both individual careers and organizational outcomes.

Jordan Morrow is known as the "Godfather of Data Literacy," having helped pioneer and invent the entire field. He is also the founder and CEO of Bodhi Data and currently is the Senior Vice President of Data & AI Transformation for AgileOne, helping to utilize data and AI in the total talent management space.

Jordan is a global trailblazer in the world of data literacy and enjoys his time traveling the world speaking and/or helping companies. He served as the Chair of the Advisory Board for The Data Literacy Project, has spoken at numerous conferences around the world, and is an active voice in the data and analytics community. He has also helped companies and organizations around the world, including the United Nations, build and/or understand data literacy.

In the episode, Richie and Jordan explore the progress and challenges in data literacy, the integration of AI literacy, the importance of storytelling and decision-making in data training, how organizations can foster a data-driven culture, practical tips for using AI in meetings and personal productivity, and much more.

Links Mentioned in the Show:

Pre-order Jordan’s upcoming book - Data and AI Skills: Gain the Confidence You Need to SucceedJordan’s BooksConnect with JordanDataCamp Webinar Featuring the Godparents of Data Literacy - Jordan Morrow and Valerie LoganRelated Episode: Scaling Responsible AI Literacy with Uthman Ali, Global Head of Responsible AI at BPRewatch RADAR AI 

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#323 The Evolution of Data Literacy & AI Literacy with Jordan Morrow, Godfather of Data Literacy

The relationship between AI assistants and data professionals is evolving rapidly, creating both opportunities and challenges. These tools can supercharge workflows by generating SQL, assisting with exploratory analysis, and connecting directly to databases—but they're far from perfect. How do you maintain the right balance between leveraging AI capabilities and preserving your fundamental skills? As data teams face mounting pressure to deliver AI-ready data and demonstrate business value, what strategies can ensure your work remains trustworthy? With issues ranging from biased algorithms to poor data quality potentially leading to serious risks, how can organizations implement responsible AI practices while still capitalizing on the positive applications of this technology?

Christina Stathopoulos is an international data specialist who regularly serves as an executive advisor, consultant, educator, and public speaker. With expertise in analytics, data strategy, and data visualization, she has built a distinguished career in technology, including roles at Fortune 500 companies. Most recently, she spent over five years at Google and Waze, leading data strategy and driving cross-team projects. Her professional journey has spanned both the United States and Spain, where she has combined her passion for data, technology, and education to make data more accessible and impactful for all. Christina also plays a unique role as a “data translator,” helping to bridge the gap between business and technical teams to unlock the full value of data assets. She is the founder of Dare to Data, a consultancy created to formalize and structure her work with some of the world’s leading companies, supporting and empowering them in their data and AI journeys. Current and past clients include IBM, PepsiCo, PUMA, Shell, Whirlpool, Nitto, and Amazon Web Services.

In the episode, Richie and Christina explore the role of AI agents in data analysis, the evolving workflow with AI assistance, the importance of maintaining foundational skills, the integration of AI in data strategy, the significance of trustworthy AI, and much more.

Links Mentioned in the Show:

Dare to DataJulius AIConnect with ChristinaCourse - Introduction to SQL with AIRelated Episode: The Data to AI Journey with Gerrit Kazmaier, VP & GM of Data Analytics at Google CloudRewatch RADAR AI 

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Learn on the go using the DataCamp mobile app

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#322 How Next-Gen Data Analytics Powers Your AI Strategy with Christina Stathopoulos, Founder at Dare to Data

Financial institutions are racing to harness the power of AI, but the path to implementation is filled with challenges. From feature engineering to model deployment, the technical complexities of AI adoption in finance require careful navigation of both technological and regulatory landscapes. How do you build AI systems that satisfy strict compliance requirements while still delivering business value? What skills should teams prioritize as AI tools become more accessible through natural language interfaces? With the pressure to reduce model development time from months to days, how can organizations maintain proper governance while still moving at the speed modern business demands?

Vijay is a seasoned analytics, product, and technology executive. As EVP of Global Solutions & Analytics at Experian, he runs the department that creates Experian's Ascend financial AI platform. Promoted multiple times in eight years, Vijay now leads a team of more than 70 at Experian. He is one of the youngest execs at Experian, believing strongly in understanding and accepting risk. He has built and run data, engineering, and IT teams, and created market-leading products.

In the episode, Richie and Vijay explore the impact of generative AI on the finance industry, the development of Experian's Ascend platform, the challenges of fraud prevention, education and compliance in AI deployment, and much more.

Links Mentioned in the Show:

ExperianExperian AscendConnect with VijayCourse: Implementing AI Solutions in BusinessRelated Episode: How Generative AI is Transforming Finance with Andrew Reiskind, CDO at MastercardRewatch RADAR AI 

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#321 Developing Financial AI Products at Experian with Vijay Mehta, EVP of Global Solutions & Analytics at Experian

The manufacturing floor is undergoing a technological revolution with industrial AI at its center. From predictive maintenance to quality control, AI is transforming how products are designed, produced, and maintained. But implementing these technologies isn't just about installing sensors and software—it's about empowering your workforce to embrace new tools and processes. How do you overcome AI hesitancy among experienced workers? What skills should your team develop to make the most of these new capabilities? And with limited resources, how do you prioritize which AI applications will deliver the greatest impact for your specific manufacturing challenges? The answers might be simpler than you think.

Barbara Humpton is President and CEO of Siemens Corporation, responsible for strategy and engagement in Siemens’ largest market. Under her leadership, Siemens USA operates across all 50 states and Puerto Rico with 45,000 employees and generated $21.1 billion in revenue in fiscal year 2024. She champions the role of technology in expanding what’s humanly possible and is a strong advocate for workforce development, mentorship, and building sustainable work-life integration. Previously, she was President and CEO of Siemens Government Technologies, leading delivery of Siemens’ products and services to U.S. federal agencies. Before joining Siemens in 2011, she held senior roles at Booz Allen Hamilton and Lockheed Martin, where she oversaw programs in national security, biometrics, border protection, and critical infrastructure, including the FBI’s Next Generation Identification and TSA’s Transportation Workers’ Identification Credential.

Olympia Brikis is a seasoned technology and business leader with over a decade of experience in AI research. As the Technology and Engineering Director for Siemens' Industrial AI Research in the U.S., she leads AI strategy, technology roadmapping, and R&D for next-gen AI products. Olympia has a strong track record in developing Generative AI products that integrate industrial and digital ecosystems, driving real-world business impact. She is a recognized thought leader with numerous patents and peer-reviewed publications in AI for manufacturing, predictive analytics, and digital twins. Olympia actively engages with executives, policymakers, and AI practitioners on AI's role in enterprise strategy and workforce transformation. With a background in Computer Science from LMU Munich and an MBA from Wharton, she bridges AI research, product strategy, and enterprise adoption, mentoring the next generation of AI leaders.

In the episode, Richie, Barbara, and Olympia explore the transformative power of AI in manufacturing, from predictive maintenance to digital twins, the role of industrial AI in enhancing productivity, the importance of empowering workers with new technology, real-world applications, overcoming AI hesitancy, and much more.

Links Mentioned in the Show:

Siemens Industrial AI Suite • Connect with 
Barbara • and 
OlympiaCourse: Implementing AI Solutions in BusinessRelated Episode: Master Your Inner Game to Avoid Burnout with Klaus Kleinfeld, Former CEO at Alcoa and SiemensRewatch RADAR AI • where...

#320 The Next Industrial Revolution is Industrial AI | Barbara Humpton, CEO at Siemens USA and Olympia Brikis, Director of Industrial AI at Siemens USA

As we close out our focus on how the venture capital industry identifies and decides which future companies to fund, it might be easy to fall into the trap of thinking that the latest methods for discovering future unicorns are ubiquitous among all VCs. However, many VCs still work ‘the old way,’ using data to back up human assumptions. But what happens when a data engineer pivots to VC? What does a data-driven, data-first approach look like, and how does it compare to the incumbent processes?

Dr. Andre Retterath is a Partner in Earlybird’s Munich Office, focussing on enterprise software with a particular interest in developer, data and productivity tools, alongside AI-centric products and robotics. Before transitioning into VC in 2017, he gained more than 5 years of experience as a process automation and predictive maintenance engineer at ThyssenKrupp and further insights as a management consultant at GE North America. Andre also has his own VC, AI & data newsletter, Data-Driven VC.

In the episode, Richie and Andre explore the concept of data-driven venture capital, the challenges of traditional VC and why digitization has had a huge impact on the industry, the data-driven VC process, the use of modern data and AI technologies in identifying potentially successful projects, the human element in VC, the challenges and opportunities of early-stage investments, the importance of early identification of these ventures, cultural and organizational indicators and much more. 

Links Mentioned in the Show:

Data-Driven VCEarlybird VCAleph AlphaPareto PrincipleRelated Episode: Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision ScientistSign up to RADAR: AI Edition

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#207 Data Driven Venture Capital with Andre Retterath, Partner at Earlybird VC

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