DataFramed – Détails, épisodes et analyse

The rapid rise of generative AI is changing how businesses operate, but with this change comes new challenges. How do you navigate the balance between innovation and risk, especially in a regulated industry? As organizations race to adopt AI, it’s crucial to ensure that these technologies are not only transformative but also responsible. What steps can you take to harness AI’s potential while maintaining control and transparency? And how can you build excitement and trust around AI within your organization, ensuring that everyone is ready to embrace this new era?

Steve Holden is the Senior Vice President and Head of Single-Family Analytics at Fannie Mae, leading a team of data science professionals, supporting loan underwriting, pricing and acquisition, securitization, loss mitigation, and loan liquidation for the company’s multi-trillion-dollar Single-Family mortgage portfolio. He is also responsible for all Generative AI initiatives across the enterprise. His team provides real-time analytic solutions that guide thousands of daily business decisions necessary to manage this extensive mortgage portfolio. The team comprises experts in econometric models, machine learning, data engineering, data visualization, software engineering, and analytic infrastructure design. Holden previously served as Vice President of Credit Portfolio Management Analytics at Fannie Mae. Before joining Fannie Mae in 1999, he held several analytic leadership roles and worked on economic issues at the Economic Strategy Institute and the U.S. Bureau of Labor Statistics.

In the episode Adel and Steve explore opportunities in generative AI, building a GenAI program, use-case prioritization, driving excitement and engagement for an AI-first culture, skills transformation, governance as a competitive advantage, challenges of scaling AI, future trends in AI, and much more. 

Links Mentioned in the Show:

• Fannie Mae
• Steve’s recent DataCamp Webinar: Bringing Generative AI to the Enterprise
• Video: Andrej Karpathy - [1hr Talk] Intro to Large Language Models
• Skill Track - AI Business Fundamentals
• Related Episode: Generative AI at EY with John Thompson, Head of AI at EY
• Rewatch sessions from RADAR: AI Edition

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• Data & AI Video Creator

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The pressure to innovate with AI is immense. There is seemingly a race against the clock for organizations to incorporate AI into their product offering, aside from continual digital transformation. As the speed of AI development accelerates, many organizations struggle to keep up, facing challenges from data readiness to changing traditional business processes. How can businesses ensure that their AI initiatives not only align with strategic goals but also foster real, tangible progress? What steps can leaders take to build AI fluency across their teams and turn potential into actionable outcomes?

Alison McCauley is a Best-Selling Author, Keynote Speaker, AI Strategist. She is Chief Advocacy Officer at Think with AI and Founder of Unblocked Future, a consultancy that leads the way in adopting emerging technologies, and has been collaborating with AI pioneers since 2010. With nearly 30 years of experience at the intersection of enterprise and disruptive innovation, Alison specializes in unlocking business value from cutting-edge technologies by focusing on the human aspects of change. She has been recognized as a Top Voice in AI, authored the book Unblocked, is a keynote speaker at global conferences, and her writings have appeared in Harvard Business Review, Forbes, and Venture Beat. Additionally, over 90,000 students have taken her LinkedIn course.

In the episode, Richie and Alison explore digital transformation and AI’s role in it, strategic alignment and shifting mindsets, AI fluency, challenges in data readiness, organizational resistance fuelled by fear, the role of management in AI transformation, practical steps to avoid AI risks, the long term impact of AI in the future and much more. 

Links Mentioned in the Show:

• Think with AI
• Unlocked Future
• Unblocked: How Blockchains Will Change Your Business (and What to Do About It)
• Connect with Alison
• Course - Artificial Intelligence (AI) Strategy
• Related Episode: How are Businesses Really Using AI? With Tathagat Varma, Global TechOps Leader at Walmart Global Tech
• Rewatch sessions from RADAR: AI Edition

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

One of the best applications of data science is that it allows experimentation within any organization at scale. The ability to test a new checkout feature, the color of a button, and analyze whether that improves customer experiences can be truly magical when done correctly. However, doing this at scale means that the entire organization needs to be bought into the experimentation agenda. So how do you do this and how do you make sure this becomes part of your organization’s culture?

Amit Mondal is the VP & Head of Digital Analytics & Experimentation at American Express. Throughout his career Amit has been a financial services leader in digital, analytics/data science and risk management, driving digital strategies and investments, while creating a data driven & experimentation first culture for Amex. Amit currently leads a global team of 200+ Data Scientists, Statisticians, Experimenters, Analysts, and Data experts.

In the episode, Adel and Amit explore the importance of experimentation at American Express, key components of experimentation strategies, ownership and coordination in experimentation processes, the pillars that feed into a culture of experimentation, frameworks for building successful experiments, robust experiment design, challenges and trends across industries and much more. 

Links Mentioned in the Show:

• American Express
• Decoding Marketing Mix Modeling
• [Course] A/B Testing in Python
• Related Episode: Data & AI at Tesco with Venkat Raghavan, Director of Analytics and Science at Tesco
• Rewatch sessions from RADAR: AI Edition

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

Historically in elite team sports, there has often been a dynamic between players and their inherent abilities, and the vision of the coach. In many sports, we’ve seen coaching strategies influence the future of how the game is played.

As the era of professionalism swept across many elite sports in the 90s, we saw the highest-level sports teams achieve a competitive edge by looking at the data, with sports fans often noticing a difference in the ‘feel’ of the way their team plays. In Basketball specifically, we have recently seen the rise of the 3-pointer, a riskier and much more difficult shot to accurately hit, even for professional players. But what has driven the rise of the 3-pointer? Is it another trend among coaches, or does the answer lie with data-based insights and the analysts producing these insights?

Seth Partnow is the Director of North American Sports at StatsBomb, where he previously served as their Director of Basketball Analytics. Prior to joining StatsBomb in 2021, Seth was the Director of Basketball Research for the Milwaukee Bucks basketball team. Seth is also an accomplished Analyst and Author, having worked as an NBA Analyst for The Athletic since 2019 and having published his own book on basketball analytics, The Midrange Theory. Seth’s knowledge and insight bridges the gap between data analytics and elite US sport. 

In the episode, Seth and Richie look into the intricate dynamics of elite basketball. Seth explores the challenges of attributing individual contributions in a sport where the outcome is significantly influenced by the complex interplay between players.

Drawing from his extensive experience in the field, Seth discusses the complexities of analyzing player performance, the nuances of determining why certain players get easier or harder shots, and the difficulty of attributing credit for defensive achievements to individual players.

Seth provides a comprehensive overview of the various roles within sports analytics, from data engineers to analysts, and highlights the importance of finding one's niche within these roles, particularly in the context of elite basketball.

Seth also shares his personal journey into basketball analytics, offering valuable insights and advice for those interested in pursuing a career in this field, stressing the importance of introspection and understanding the unique lifestyle associated with working for a sports team, while also offering industry-agnostic advice on how to approach analyzing and using data in any context.

In the realm of Applied Intelligence, Accenture leads the way in harnessing the power of data and AI to transform industries. From consumer products to life sciences, retail, and aerospace, Accenture's influence is far-reaching. But what drives the organization? How does it navigate the complex landscape of data modernization and transformation? And more importantly, how does it leverage technology not just as an enabler, but as a catalyst for innovation? 

Tracy Ring leads Accenture’s Applied Intelligence Products Category Group, in this role she has leadership across Consumer and Industrial Products, Automotive, Life Sciences, Retail and Aerospace and Defense. As the CDO and Global Generative AI lead for Life Sciences, she personally anchors the NA Applied Intelligence Life Sciences practice of more than 500 practitioners. Tracy has created solutions for Generative AI, Data led transformation, Artificial Intelligence, Data and Cloud Modernization, Analytics, and the organization and operating model strategies for next-generation adoption and AI fluency. 

In the episode, Tracy initially clarifies the difference between data modernization and data transformation, highlighting their distinct meanings and why the terms aren’t interchangeable. 

Tracy also emphasizes the importance of involving business end-users from the outset of data projects as well as advocating for a product-oriented approach to data.

The discussion also covers the topic of team diversity and inclusivity. Tracy shares practical advice on how to build diverse teams and create an environment that encourages curiosity and open dialogue. Tracy also shares her perspective on the future of work and the importance of fostering meaningful conversations in the workplace. She advocates for an attitude of infinite curiosity within teams.

In the context of life sciences, Tracy highlights the high stakes involved and underscores the need for responsible AI, data sharing, and data privacy. She also points out that the challenges in this field are more similar than dissimilar to those in other industries.

Tune in for a wealth of insights from a seasoned leader in the field of Applied Intelligence.

A lot of the times when we walk into a supermarket, we don't necessarily think about the impact data science had in getting these products on shelves. However, as you’ll learn in today's episode, it's safe to say there's a myriad of applications for data science in the FMCG industry. Whether be that supply chain use-cases that leverage time-series forecasting techniques, to computer vision use-cases for on-shelf optimization—the use-cases are endless here. So how can data scientists and data leaders maximize value in this space?

Enter Anastasia Zygmantovich. Anastasia is a Global Data Science Director at Reckitt, which is most known for products like Airwick, Lysol, Detol, and Durex. Throughout the episode, we discuss how data science can be used in the FMCG industry, how data leaders can hire impactful data teams in this space, why FMCG is a great place to work in for data scientists, some awesome use-cases she's worked on, how data scientists can best maximize their value in this space, what generative AI means for organizations, and a lot more.

When we think about video games like Call of Duty, Fifa, or Fortnite, our minds often turn to creative artists, software developers, designers, and producers. These are the people who make our favorite games a reality. But behind the scenes, data & AI actively shape our experience with our favorite video games. From the quality of video games, the accessibility of maps and worlds, even the go to market, data & AI play an impactul role in making or breaking the success of a video game.

Marie de Léséleuc is an accomplished game industry professional with over a decade of experience. Marie started her career as a data analyst, and has since risen through the ranks to a data leader in the gaming industry. She's worked at companies such as Ubisoft, Warner Brothers, and most recently at Eidos, the company most well known for games such as Guardians of the Galaxy and Tomb Raider.

Throughout the episode, we discuss how data science can be used in gaming, the unique challenges data teams face in gaming from really low data volumes to massive changes to production schedules and game vision. We also spoke about the difference between "AI" as we know it in data science, and AI in gaming, which informs how NPCs behave in a video game world—and a lot more.

Imagine making parenting choices not just based on instinct and through the lived experiences of others, but instead using data-driven techniques garnered through a career in data and economics. 

Emily Fair Oster is a Professor of Economics and International and Public Affairs at Brown University. Her work is unique, blending economics, health, and research in new ways. In her books "Expecting Better," "The Family Firm," and "Cribsheet," she's shown how data can help guide us through pregnancy and parenting.

In the episode, Emily shows how she used her knowledge of data and economics when she was pregnant, and how this way of thinking can change how we make decisions.

We look at the tension between what we feel and what the data tells us when we're making parenting choices, and why many of us lean on personal experiences. Emily tells us why it's important to use quality data when making decisions and how to make sense of all the information out there.

Emily talks about the ins and outs of using data to make parenting decisions, discussing the big milestones in a child's life, the role of sleep, and how these can impact a person's future as well as the nuance in applying data-driven decision-making to your parenting. 

Emily also touches on how having two working parents and traditional gender roles can shape how we parent.

Finally, Emily gives some helpful tips on finding and understanding good-quality data. This will help you make better decisions as a parent. Tune in for a thought-provoking look at parenting, data, and economics.

Although many have been cognizant of AI’s value in recent months, the further back we look, the more exclusive this group of people becomes. In our latest AI-series episodes of DataFramed, we gain insight from an expert who has been part of the industry for 40 years.

Joaquin Marques, Founder and Principal Data Scientist at Kanayma LLC has been working in AI since 1983. With experience at major tech companies like IBM, Verizon, and Oracle, Joaquin's knowledge of AI is vast. Today, he leads an AI consultancy, Kanayma, where he creates innovative AI products.

Throughout the episode, Joaquin shares his insights on AI's development over the years, its current state, and future possibilities. Joaquin also shares the exciting projects they've worked on at Kanayma as well as what to consider when building AI products, and how ChatGPT is making chatbots better.

Joaquin goes beyond providing insight into the space, encouraging listeners to think about the practical consequences of implementing AI, with Joaquin sharing the finer technical details of many of the solutions he’s helped build. Joaquin also shares many of the thought processes that have helped him move forward when building AI products, providing context on many practical applications of AI, both from his past and the bleeding edge of today.  

The discussion examines the complexities of artificial intelligence, from the perspective of someone that has been focused on this technology for more than most. Tune in for guidance on how to build AI into your own company's products.

With the advances in AI products and the explosion of ChatGPT in recent months, it is becoming easier to imagine a world where AI and humans work seamlessly together—revolutionizing how we solve complex problems and transform our daily lives. This is especially the case for data professionals.

In this episode of our AI series, we speak to Sarah Schlobohm, Head of AI at Kubrick Group. Dr. Schlobohm leads the training of the next generation of machine learning engineers. With a background in finance and consulting, Sarah has a deep understanding of the intersection between business strategy, data science, and AI. Prior to her work in finance, Sarah became a chartered accountant, where she honed her skills in financial analysis and strategy. Sarah worked for one of the world's largest banks, where she used data science to fight financial crime, making significant contributions to the industry's efforts to combat money laundering and other illicit activities. Sarah shares her extensive knowledge on incorporating AI within data teams for maximum impact, covering a wide array of AI-related topics, including upskilling, productivity, and communication, to help data professionals understand how to integrate generative AI effectively in their daily work.

Throughout the episode, Sarah explores the challenges and risks of AI integration, touching on the balance between privacy and utility. She highlights the risks data teams can avoid when using AI products and how to approach using AI products the right way. She also covers how different roles within a data team might make use of generative AI, as well as how it might effect coding ability going forward.

Sarah also shares use cases for those in non-data teams, such as marketing, while also highlighting what to consider when using outputs from GPT models. Sarah shares the impact chatbots might have on education calling attention to the power of AI tutors in schools.

Sarah encourages people to start using AI now, considering the barrier to entry is so low, and how that might not be the case going forward. From automating mundane tasks to enabling human-AI collaboration that makes work more enjoyable, Sarah underscores the transformative power of AI in shaping the future of humanity.

Whether you're an AI enthusiast, data professional, or someoone with an interest in either this episode will provide you with a deeper understanding of the practical aspects of AI implementation.