DataFramed (DataCamp)

Understanding and interpreting data visualizations are one of the most important aspects of data literacy. When done well, data visualization ensures that stakeholders can quickly take away critical insights from data. Moreover, data visualization is often the best place to start when increasing organizational data literacy, as it’s often titled the “gateway drug” to more advanced data skills.

Andy Cotgreave, Senior Data Evangelist at Tableau Software and co-author of The Big Book of Dashboards, joins the show to break down data visualization and storytelling, drawing from his 15-year career in the data space. Andy has spoken for events like SXSW, Visualized, and Tableau’s conferences and has inspired thousands of people to develop their data skills.

In this episode, we discuss why data visualization skills are so essential, how data visualization increases organizational data literacy, the best practices for visual storytelling, and much more.

This episode of DataFramed is a part of DataCamp’s Data Literacy Month, where we raise awareness about Data Literacy throughout September through webinars, workshops, and resources featuring thought leaders and subject matter experts that can help you build your data literacy, as well as your organization’s. For more information, visit: https://www.datacamp.com/data-literacy-month/for-teams

To become a data-driven organization, it takes a major shift in mindset and culture, investments in technology and infrastructure, skills transformation, and clearly evangelizing the usefulness of using data to drive better decision-making.

With all of these levers to scale, many organizations get stuck early in their data transformation journey, not knowing what to prioritize and how. In this episode, Ganes Kesari joins the show to share the frameworks and processes that organizations can follow to become data-driven, measure their data maturity, and win stakeholder support across the organization.

Ganes is Co-Founder and Chief Decision Scientist at Gramener, which helps companies make data-driven decisions through powerful data stories and analytics. He is an expert in data, analytics, organizational strategy, and hands-on execution. Throughout his 20-year career, Ganes has become an internationally-renowned speaker and has been published in Forbes, Entrepreneur, and has become a thought leader in Data Science.

Throughout the episode, we talk about how organizations can scale their data maturity, how to build an effective data science roadmap, how to successfully navigate the skills and people components of data maturity, and much more.

Throughout data literacy month, we’ve shined a light on the importance of data literacy skills and how it impacts individuals and organizations. Equally as important is how to actually approach transformational data literacy programs and ensure they are successful.

In this final episode of Data Literacy Month, we are unpacking how CBRE is upskilling over 3,000 of its employees on data literacy skills through a relevant, high-value learning program.

Emily Hayward is the Data and Digital Change Manager at CBRE, a global leader in commercial real estate services and investment. Emily is a transformational leader with a track record of leading successful high-profile technology, data, and cultural transformations across both the public and private sectors through an ardent belief that change cannot be achieved without first winning people over.

Throughout the episode, we talk about Emily’s approach to building CBRE’s learning program, effective change management, why it’s critical to secure executive sponsorship, and much more.

Looking to build a data literacy program of your own? Check out DataCamp for Business: https://bit.ly/3r7BgsF

Taking inspiration from International Literacy Day on September 8, DataCamp is dedicating the whole month of September to raising awareness about Data Literacy.

Throughout the month, we are featuring thought leaders and subject matter experts in order to get you Data Literacy, and we can’t wait for you to hear the exceptional guests we have lined up for you right here on DataFramed.

Check out the full lineup of events.

Introducing the DataFramed Careers Series. Over the past year hosting the DataFramed podcast, we've had the incredible privilege of having biweekly conversations with data leaders at the forefront of the data revolution. This has led to fascinating conversations on the future of the modern data stack, the future of data skills, and how to build organizational data literacy. 

However, as the DataFramed podcast grows, we want to be able to provide the data science community across the spectrum from practitioners to leaders, with distilled insights that will help them manoeuvre their careers effectively. And we want to do that more often. 

This is why we’re excited to announce the launch of a four-day DataFramed Careers Series. Throughout next week, we will interview four different thought leaders and experts about what it takes to break into data science in 2022, best practices to stand out from the crowd, building a brand in data science, and more. Moreover, this episode series will mark DataFramed’s transition from biweekly to weekly.

Starting Monday the 30th of May, DataFramed will become a weekly podcast.

For next week’s DataFramed Careers Series, we’ll be covering the ins and outs of building a career in data, and the different aspects of standing out from the crowd during the job hunt. We’ll be hearing from Sadie St Lawrence, CEO and Founder of Women in Data on what it takes to launch a data career in 2022. Nick Singh, Co-author of Ace the Data Science Interview and 2nd time guest of DataFramed will join us to discuss what makes a great data science portfolio project. Khuyen Tran, Developer Advocate at Prefect on will outline how writing can accelerate a data career, and Jay Feng, CEO of Interview Query will join us to provide tips and frameworks on acing the data science interview.

For future DataFramed episodes, we’ll definitely still cover the different aspects of building a data-driven organization, cover the latest advancements in data science, building data careers, and more. So expect more varied guests, topics, and more specials series like this one in the future.

The healthcare industry presents a set of unique challenges for data science, including how to manage and work with sensitive patient information and accounting for the real-world impact of AI and machine learning on patient care and experience.

Curren Katz, Senior Director for Data Science & Project Management at Johnson & Johnson, believes that despite challenges like these, there are massive opportunities for data science and machine learning to increase care quality, drive business objectives, diagnose diseases earlier, and ultimately save countless lives around the world.

Curren has over 10 years of leadership experience across both the US and Europe and has led more than 20 successful data science product launches in the payer, provider, and pharmaceutical spaces. She also brings her background as a cognitive neuroscientist to data science, with research in neural networks, connectivity analysis, and more.

[Announcement] Join us for DataCamp Radar, our digital summit on June 23rd. During this summit, a variety of experts from different backgrounds will be discussing everything related to the future of careers in data. Whether you're recruiting for data roles or looking to build a career in data, there’s definitely something for you. Seats are limited, and registration is free, so secure your spot today on https://events.datacamp.com/radar/

Remarkable people walk among us. Some of us may be remarkable ourselves. But none of us start out remarkable. The journey to becoming a person that makes a difference in the world is never easy, as with any story that includes a hero, there are struggles, tests and moments of self-doubt. Remarkable people overcome these feats, and when they are in a position to, they give back. But what kind of mindset do these people have, how do they make decisions? What keeps them on their path towards becoming remarkable. 

Guy Kawasaki is the chief evangelist of Canva and the creator of Guy Kawasaki’s Remarkable People podcast. He is an executive fellow of the Haas School of Business (UC Berkeley), and adjunct professor of the University of New South Wales. He was the chief evangelist of Apple and a trustee of the Wikimedia Foundation. He has written Wise Guy, The Art of the Start 2.0, The Art of Social Media, Enchantment, and eleven other books. Kawasaki has a BA from Stanford University, an MBA from UCLA, and an honorary doctorate from Babson College.

In the episode, Richie and Guy explore the concept of being remarkable, growth, grit and grace, the importance of experiential learning, imposter syndrome, finding your passion, how to network and find remarkable people, dealing with failure, management and encouraging growth, work-life balance, measuring success through benevolent impact and much more. 

Links Mentioned in the Show:

• Think Remarkable by Guy Kawasaki
• Guy Kawasaki’s Remarkable People
• Connect with Guy on Linkedin
• Canva
• The Four Agreements: A Practical Guide to Personal Freedom by Don Miguel Ruiz
• How to Change: The Science of Getting from Where You Are to Where You Want to Be by Katy Milkman
• Related Episode: Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision Scientist
• Sign up to RADAR: The Analytics 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 toughest parts of any data project is experimentation, not just because you need to choose the right testing method to confirm the project’s effectiveness, but because you also need to make sure you are testing the right hypothesis and measuring the right KPIs to ensure you receive accurate results.

One of the most effective methods for data experimentation is A/B testing, and Anjali Mehra, Senior Director of Product Analytics, Data Science, Experimentation, and Instrumentation at DocuSign, is no stranger to how A/B testing can impact multiple parts of any organization. Throughout her career, she has also worked in marketing analytics and customer analytics at companies like Shutterfly, Wayfair, and Constant Contact.

Throughout the episode, we discuss DocuSign’s analytics goals, how A/B testing works, how to gamify data experimentation, how A/B testing helps with new initiative validation, examples of A/B testing with data projects, how organizations can get started with data experimentation, and much more.

In 2020, OpenAI launched GPT-3, a large language AI model that is demonstrating the potential to radically change how we interact with software, and open up a completely new paradigm for cognitive software applications.

Today’s episode features Sandra Kublik and Shubham Saboo, authors of GPT-3: Building Innovative NLP Products Using Large Language Models. We discuss what makes GPT-3 unique, transformative use-cases it has ushered in, the technology powering GPT-3, its risks and limitations, whether scaling models is the path to “Artificial General Intelligence”, and more.

Announcement

For the next seven days, DataCamp Premium and DataCamp for Teams are free. Gain free access by following going here. 

Ten years ago, Salesforce was trying to generate $1Bn of revenue in a quarter. Today, they create over $30Bn of revenue in year. Simultaneously, over the last decade we have seen huge advances in the world of data and data science.

In this episode, Laura Gent Felker, Director of Data Insights and Scalability at Salesforce, talks about her experience in building and leading data teams within the organization over the last ten years. Laura shares her insights on how to create a learning culture within a team, how to prioritize projects while accounting for long-term strategy, and the importance of setting aside time for innovation.

Laura also discusses how to ensure that the projects the team works on genuinely provide business value. She suggests creating a two-way street with executive leadership and understanding the collective value across a variety of stakeholders also citing that some of the best innovation she has seen come from her team is when they have had to solve high-priority short-term business problems. 

In addition, Laura shares a multi-layered approach to building a learning community within a data team. She explains that a culture of collaboration and trust is important in the direct data team, and the wider community within organizations. 

Laura also talks about the frameworks and mental models that can help develop business acumen. She highlights the importance of dedicating time to this area and being able to communicate insights effectively.

Throughout the episode, Laura's insights provide valuable guidance for both junior and experienced data professionals, consumers and leaders in creating a learning culture, prioritizing projects, and building a strong data community within organizations.

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.

Anjali Samani, Director of Data Science & Data Intelligence at Salesforce, joins the show to discuss what it takes to become a mature data organization and how to build an impactful, diverse data team. As a data leader with over 15 years of experience, Anjali is an expert at assessing and deriving maximum value out of data, implementing long-term and short-term strategies that directly enable positive business outcomes, and how you can do the same.

You will learn the hallmarks of a mature data organization, how to measure ROI on data initiatives, how Salesforce implements its data science function, and how you can utilize strong relationships to develop trust with internal stakeholders and your data team.

In pharmaceuticals, wrong decisions can not only cost a company revenue, but they can also cost people their lives. With stakes so high, it’s vital that pharmaceutical companies have robust systems and processes in place to accurately gather, analyze, and interpret data and turn it into actionable steps to solving health issues.

Suman Giri is the Global Head of Data Science of the Human Health Division at Merck, a biopharmaceutical research company that works to develop innovative health solutions for both people and animals. Suman joins the show today to share how Merck is using data to improve organizational decision-making, medical research outcomes, and how data science is transforming the pharmaceutical industry at scale. He also shares some of the biggest challenges facing the industry right now and what new trends are on the horizon.

Whether big or small, one of the biggest challenges organizations face when they want to work with data effectively is often lack of access to it. This is where building a data platform comes in. But building a data platform is no easy feat. It's not just about centralizing data in the data warehouse, it’s also about making sure that data is actionable, trustable and usable. So, how do you make sure your data platform is up to par?

Shuang Li is Group Product Manager at Box. With experience of building data, analytics, ML, and observability platform products for both external and internal customers, Shuang is always passionate about the insights, optimizations, and predictions that big data and AI/ML make possible. Throughout her career, she transitioned from academia to engineering, from engineering to product management, and then from an individual contributor to an emerging product executive.

In the episode, Adel and Shuang explore her career journey, including transitioning from academia to engineering and helping to work on Google Fiber, how to build a data platform, ingestion pipelines, processing pipelines, challenges and milestones in building a data platform, data observability and quality, developer experience, data democratization, future trends and a lot more. 

Links Mentioned in the Show:

• Box
• Connect with Shuang on Linkedin
• [Course] Understanding Modern Data Architecture
• Related Episode: Scaling Enterprise Analytics with Libby Duane Adams, Chief Advocacy Officer and Co-Founder of Alteryx

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

Creating a culture of continuous learning within analytics functions isn't just beneficial; it's essential. In the session, Russell Johnson, Chief Data Scientist at Marks & Spencer, Denisse Groenendaal-Lopez, Learning & Development Business Partner at Booking Group, and Mark Stern, VP of Business Intelligence & Analytics at BetMGM will address the importance of fostering a learning environment for driving success with analytics. They will provide insights on developing a culture where continuous learning, experimentation, and curiosity are the norms—and strategies leaders can adopt today to drive up excitement around analytics adoption & upskilling. 

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.

When you hear the term-digital first, you might think about tech, platforms and data.  But digital transformation succeeds when you put people first.

Gathering and analyzing data, then using it to provide the customer value and an unparalleled experience, is vital for an organization’s success.

Today’s guest, Bhavin Patel, Director o f Analytics and Innovation at J&J joins the show to share why people are the most important component to digital transformation.

Join us as we discuss:

• Why you need to put people first
• The importance of customer value and experience
• Why digital transformation is an ongoing process, not an end-state 

Find every episode of DataFramed on Apple, Spotify, and more. Find us on our website and join the conversation on LinkedIn.

Listening on a desktop and can’t see the links? Just search for DataFramed in your favorite podcast player.

By now, many of us are convinced that generative AI chatbots like ChatGPT are useful at work. However, many executives are rightfully worried about the risks from having business and customer conversations recorded by AI chatbot platforms. Some privacy and security-conscious organizations are going so far as to block these AI platforms completely. For organizations such as EY, a company that derives value from its intellectual property, leaders need to strike a balance between privacy and productivity. 

John Thompson runs the department for the ideation, design, development, implementation, & use of innovative Generative AI, Traditional AI, & Causal AI solutions, across all of EY's service lines, operating functions, geographies, & for EY's clients. His team has built the world's largest, secure, private LLM-based chat environment. John also runs the Marketing Sciences consultancy, advising clients on monetization strategies for data. He is the author of four books on data, including "Data for All' and "Causal Artificial Intelligence". Previously, he was the Global Head of AI at CSL Behring, an Adjunct Professor at Lake Forest Graduate School of Management, and an Executive Partner at Gartner.

In the episode, Richie and John explore the adoption of GenAI at EY, data privacy and security, GenAI use cases and productivity improvements, GenAI for decision making, causal AI and synthetic data, industry trends and predictions and much more. 

Links Mentioned in the Show:

• Azure OpenAI
• Causality by Judea Pearl
• [Course] AI Ethics
• Related Episode: Data & AI at Tesco with Venkat Raghavan, Director of Analytics and Science at Tesco
• Catch John talking about AI Maturity this September
• 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 most immediate needs to come out of the generative AI boom has been the need for guardrails and governmental regulation of AI technologies. Most of the work already completed in the AI space has been industry-led, with large organizations pushing AI forward to improve their efficiency as businesses and to create new avenues for revenue. This focus on industry and revenue can potentially create more inequality in the world, with companies not interested in the negative effects of AI being driven by profit, towards profit. To combat this, the UN has set up an AI Advisory Board, with members from different nationalities, backgrounds and expertises to ensure that AI is for all, and not just for profit. In this episode, we speak to two members of the board. 

Ian Bremmer is a political scientist who helps business leaders, policy makers, and the general public make sense of the world around them. He is president and founder of Eurasia Group, the world's leading political risk research and consulting firm, and GZERO Media, a company dedicated to providing intelligent and engaging coverage of international affairs.

Ian is credited with bringing the craft of political risk to financial markets, creating Wall Street's first global political risk index (GPRI), and for establishing political risk as an academic discipline. His definition of emerging markets— "those countries where politics matters at least as much as economics for market outcomes”—has become an industry standard. “G-Zero,” his term for a global power vacuum in which no country is willing and able to set the international agenda, is widely used by policymakers and thought leaders.

A prolific writer, Ian is the author of eleven books, including two New York Times bestsellers, “Us vs Them: The Failure of Globalism” which examines the rise of populism across the world, and his latest book “The Power of Crisis: How Three Threats—and Our Response—Will Change the World” which details a trio of looming global crises (health emergencies, climate change, and technological revolution) and outlines how governments, corporations, and concerned citizens can use these crises to create global prosperity and opportunity.

Jimena Viveros currently serves as the Chief of Staff and Head Legal Advisor to Justice Loretta Ortiz at the Mexican Supreme Court. Her prior roles include national leadership positions at the Federal Judicial Council, the Ministry of Security, and the Ministry of Finance, where she held the position of Director General. Jimena is a lawyer and AI expert, and possesses a broad and diverse international background. She is in the final stages of completing her Doctoral thesis, which focuses on the impact of AI and autonomous weapons on international peace and security law and policy, providing concrete propositions to achieve global governance from diverse legal perspectives. Her extensive work in AI and other legal domains has been widely published and recognized.

In the episode, Richie, Ian and Jimena cover what the UN's AI Advisory Body was set up for, the opportunities and risks of AI, how AI impacts global inequality, key principles of AI governance, the implementation of that governance, the future of AI in politics and global society, and much more. 

Links Mentioned in the Show:

• UN Interim Report: Governing AI for Humanity
• AI for Sustainable Development Goals
• The Power of Crisis: How Three Threats – and Our Response – Will Change the World by Ian Bremmer

In 2022, we saw significant developments in the field of data. From the emergence of generative AI to the growth of low-code data tools and AI assistants—these advancements signal an upcoming paradigm shift, where data-powered tools and machine learning systems will radically transform workflows across various professions.

2022 also saw digital transformation remain a major theme for organizations across industries as they sought to embrace new ways of working, reaching customers, and providing value. As 2023’s looming economic uncertainty puts pressure on organizations to maximize ROI from their investments, digital and data transformation will continue to be one of the key levers by which organizations can cut costs and scale value for their stakeholders.

So we’ve invited DataCamp’s co-founders, CEO Jonathan Cornelissen and COO Martijn Theuwissen to break down the top data trends they are seeing in the data space today, as well as their predictions for the future of the data industry.

Jonathan Cornelissen is the CEO and co-founder of DataCamp. As the CEO of DataCamp, he 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 PhD in financial econometrics, and was the original author of an R package for quantitative finance.

Martijn Theuwissen is the COO and co-founder of DataCamp. As the COO of DataCamp, he helps DataCamp’s enterprise clients on their data and digital transformation strategies, enabling them to make the most of DataCamp for Business’s offering, and helping them transform how their workforce uses data. 

Trust is the foundation of any relationship, whether it's between friends or in business. But what happens when the entity you're asked to trust isn't human, but AI? How do you ensure that the AI systems you're developing are not only effective but also trustworthy? In a world where AI is increasingly making decisions that impact our lives, how can we distinguish between systems that genuinely serve our interests and those that might exploit our data? 

Bruce Schneier is an internationally renowned security technologist, called a “security guru” by The Economist. He is the author of over one dozen books—including his latest, A Hacker’s Mind—as well as hundreds of articles, essays, and academic papers. His influential newsletter “Crypto-Gram” and his blog “Schneier on Security” are read by over 250,000 people. He has testified before Congress, is a frequent guest on television and radio, has served on several government committees, and is regularly quoted in the press. Schneier is a fellow at the Berkman Klein Center for Internet & Society at Harvard University; a Lecturer in Public Policy at the Harvard Kennedy School; a board member of the Electronic Frontier Foundation and AccessNow; and an Advisory Board Member of the Electronic Privacy Information Center and VerifiedVoting.org. He is the Chief of Security Architecture at Inrupt, Inc.

In the episode, Richie and Bruce explore the definition of trust, the difference between trust and trustworthiness, how AI mimics social trust, AI and deception, the need for public non-profit AI to counterbalance corporate AI, monopolies in tech, understanding the application and potential consequences of AI misuse, AI regulation, the positive potential of AI, why AI is a political issue and much more.

Links Mentioned in the Show:

• Schneier on Security
• Books by Bruce
• [Course] AI Ethics
• Related Episode: Building Trustworthy AI with Alexandra Ebert, Chief Trust Officer at MOSTLY AI
• Sign up to 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.

We are in a Generative AI hype cycle. Every executive looking at the potential generative AI today is probably thinking about how they can allocate their department's budget to building some AI use cases. However, many of these use cases won't make it into production.

In a similar vein, the hype around machine learning in the early 2010s led to lots of hype around the technology, but a lot of the value did not pan out. Four years ago, VentureBeat showed that 87% of data science projects did not make it into production. And in a lot of ways, things haven’t gotten much better. And if we don't learn why that is the case, generative AI could be destined to a similar fate. 

Eric Siegel, Ph.D., is a leading consultant and former Columbia University professor who helps companies deploy machine learning. He is the founder of the long-running Machine Learning Week conference series and its new sister, Generative AI World, the instructor of the acclaimed online course “Machine Learning Leadership and Practice – End-to-End Mastery,” executive editor of The Machine Learning Times, and a frequent keynote speaker. He wrote the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, as well as The AI Playbook: Mastering the Rare Art of Machine Learning Deployment. Eric’s interdisciplinary work bridges the stubborn technology/business gap. At Columbia, he won the Distinguished Faculty award when teaching graduate computer science courses in ML and AI. Later, he served as a business school professor at UVA Darden. Eric also publishes op-eds on analytics and social justice.

In the episode, Adel and Eric explore the reasons why machine learning projects don't make it into production, the BizML Framework or how to bring business stakeholders into the room when building machine learning use cases, the skill gap between business stakeholders and data practitioners, use cases of organizations have leveraged machine learning for operational improvements, what the previous machine learning hype cycle can teach us about generative AI and a lot more. 

Links Mentioned in the Show:

• The AI Playbook: Mastering the Rare Art of Machine Learning Deployment by Eric Siegel
• Generating ROI with AI
• BizML Cheat Sheet
• Gooder
• Survey: Machine Learning Projects Still Routinely Fail to Deploy
• [Skill Track] MLOps Fundamentals

AI features and products are the hottest area of software development. Creating high quality AI software is both essential and challenging for many businesses. In this episode, we look at retrieval augmented generation, an important technique for improving text generation quality in AI applications. Beyond technical measures, we look at the broader quality problem for AI applications. How do you ensure your AI applications are effective and secure? What steps should you take to integrate AI into your existing data governance frameworks? And how do you measure the success of these AI-driven solutions?

Theresa Parker is the Director of Product Management at Rocket Software. She has 25 years of experience as a technology executive with a focus on software development processes, consultancy, and business development. Her recent work in content management focuses on the use of AI and RAG to improve content discoverability.

Sudhi Balan is the Chief Technology Officer for AI & Cloud. He leads the AI and data teams for data modernization, driving AI adoption of Rocket's structured and unstructured data products. He also shapes AI strategy for Rocket’s infrastructure and app portfolio. He has earned patents for safe and scalable applications of transformational technology. Previously, he led digital transformation and hybrid cloud strategy for Rocket’s unstructured data business and was Senior Director of Product Development at ASG.

In the episode, Richie, Theresa, and Sudhi explore retrieval-augmented generation, its applications in customer support and loan processing, the importance of data governance and privacy, the role of testing and guardrails in AI, cost management strategies, and the potential of AI to transform customer experiences, and much more.

Links Mentioned in the Show:

• Rocket Software
• Connect with Theresa and Sudhi
• Course: Retrieval Augmented Generation (RAG) with LangChain
• Related Episode: Getting Generative AI Into Production with Lin Qiao, CEO and Co-Founder of Fireworks AI
• Rewatch sessions from RADAR: Forward 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

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.

Building machine learning systems with high predictive accuracy is inherently hard, and embedding these systems into great product experiences is doubly so. To build truly great machine learning products that reach millions of users, organizations need to marry great data science expertise, with strong attention to user experience, design thinking, and a deep consideration for the impacts of your prediction on users and stakeholders. So how do you do that?

Today’s guest is Sam Stone, Director of Product Management, Pricing & Data at Opendoor, a real-estate technology company that leverages machine learning to streamline the home buying and selling process. Sam played an integral part in developing AI/ML products related to home pricing including the Opendoor Valuation Model (OVM), market liquidity forecasting, portfolio optimization, and resale decision tooling. Prior to Opendoor, he was a co-founder and product manager at Ansaro, a SaaS startup using data science and machine learning to help companies improve hiring decisions. Sam holds degrees in Math and International Relations from Stanford and an MBA from Harvard.

Throughout the episode, we spoke about his principles for great ML product design, how to think about data collection for these types of products, how to package outputs from a model within a slick user interface, what interpretability means in the eyes of customers, how to be proactive about monitoring failure points, and much more.

Loyalty schemes are a hallmark of established retailers—not only do they build consumer trust, they are intelligent and constantly evolving, and Tesco’s Clubcard is the UK’s favorite retail loyalty program. The effects of these discounts are far-reaching, especially for families who rely on getting the best deals to make the most of their money. As Tesco’s tagline goes, every little helps. In turn, the identification and specific details of discounted products can have a profound impact on how consumers view the largest supermarket retailer in the United Kingdom, as well as the operational costs and profits that shareholders are concerned with. How do data and AI inform these offers, what goes into the enterprise-scale analytics that keeps Tesco’s Clubcard the UK’s favorite?

Venkat Raghavan is Director of Analytics and Science at Tesco. Venkat’s area of expertise is customer analytics, having been very heavily involved with the Tesco Clubcard loyalty program. Venkat also set up an analytics center of excellence to help break down data silos between teams. Previously, he was a Director of Analytics at Boston Consulting Group and Senior Director for Advanced Analytics & AI for Manthan and a Cross Industry Delivery Leader at Mu Sigma.

In the episode, Richie and Venkat explore Tesco’s use of data, the introduction of the clubcard scheme, Tesco’s data-driven innovations in online food retail, understanding customer behavior through loyalty programs and in-app interactions, improving customer experience at Tesco, operating a cohesive data intelligence platform that leverages multiple data sources, communication between data and business teams, pricing and cost management, the challenges of data science at scale, the future of data and much more. 

Links Mentioned in the Show:

• Tesco Clubcard
• McKinsey: State of Grocery Europe 2024
• [Course] Data Science for Business
• Related Episode: Scaling Enterprise Analytics with Libby Duane Adams, Chief Advocacy Officer and Co-Founder of Alteryx
• Sign up to RADAR: AI Edition

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Everything in the world has a price, including improving and scaling your data and AI functions. That means that at some point someone will question the ROI of your projects, and often, these projects will be looked at under the lens of monetization. But how do you ensure that what you’re working on is not only providing value to the business but also creating financial gain? What conditions need to be met to prove your project's success and turn value into cash?

Vin Vashishta is the author of ‘From Data to Profit’ (Wiley), the playbook for monetizing data and AI. He built V-Squared from client 1 to one of the oldest data and AI consulting firms. For the last eight years, he has been recognized as a data and AI thought leader. Vin is a LinkedIn Top Voice and Gartner Ambassador. His background spans over 25 years in strategy, leadership, software engineering, and applied machine learning.

Dr. Tiffany Perkins-Munn is on a mission to bring research, analytics, and data science to life. She earned her Ph.D. in Social-Personality Psychology with an interdisciplinary focus on Advanced Quantitative Methods. Her insights are the subject of countless lectures on psychology, statistics, and their real-world applications.

As the Head of Data and Analytics for the innovative CDAO organization at J.P. Morgan Chase, her knack involves unraveling complex business problems through operational enhancements, augmented financials, and intuitive recruiting. After over two decades in the industry, she consistently forges robust relationships across the corporate spectrum, becoming one of the Top 10 Finalists in the Merrill Lynch Global Markets Innovation Program.

In the episode, Richie, Vin, and Tiffany explore the challenges of monetizing data and AI projects, including how technical, organizational, and strategic factors affect your input, the importance of aligning technical and business objectives to keep outputs focused on core business goals, how to assess your organization's data and AI maturity, examples of high data maturity businesses, data security and compliance, quick wins in data transformation and infrastructure, why long-term vision and strategy matter, and much more.

Links Mentioned in the Show:

• Connect with Tiffany on Linkedin
• Connect with Vin on Linkedin
• Vin’s Website
• [Course] Data Governance Concepts 
• Related Episode: Scaling Enterprise Analytics with Libby Duane Adams, Chief Advocacy Officer and Co-Founder of Alteryx

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The consequences of data not being easily accessible within an organization are profound. Good decision-making often relies on good information, and with crucial insights locked behind closed doors, decision-makers may have to rely on incomplete information, stifling their ability to innovate through a lack of comprehensive data access or an inability to leverage data to its full potential. The ramifications of this are not merely operational – they extend to the core of an organization's ability to thrive in the data-driven era. However, democratizing access to data is only the first hurdle in driving a data led organization, employees need to feel confident in their ability to use data, try new tools and adopt new processes. But who is best to show us the benefits of accessing and utilizing data currently, and the cultural benefits it can bring. 

Lilac Schoenbeck is the Vice President of Strategic Initiatives at Rocket Software. Lilac has two decades of experience in enterprise software, data center technology and cloud, with wider experience in product marketing, pricing and packaging, corporate strategy, M&A integrations and product management. Lilac is passionate about delivering exceptional technology to IT teams that helps them drive value for their businesses. 

In the episode, Richie and Lilac explore data democratization and the importance of having widespread data capabilities across an organization, common data problems that data democratization can solve, tooling to facilitate better access and use of data, tool and process adoption, confidence with data, good data culture, processes to encourage good data usage and much more. 

Links mentioned in the show

• Rocket Software
• What Does Democratizing Data Mean? Unlocking the Power of Data Cultures
• Democratizing Data in Large Enterprises
• [Course] Introduction to Data Culture

The role of the data scientist is changing. Some organizations are splitting the role into more narrowly focused jobs, while others are broadening it. The latter approach, known as the Full Stack Data Scientist, is derived from the concept of a full stack software engineer, with this role often including software engineering tasks. In particular, one of the key functions of a full stack data scientist is to take machine learning models and get them into production inside software. So, what separates projects from production?

Savin Goyal is the Co-Founder & CTO at Outerbounds. In addition to his work at Outerbounds, Savin is the creator of the open source machine learning management platform Metaflow. Previously Savin has worked as a Software Engineer at Netflix and LinkedIn.

In the episode, Richie and Savin explore the definition of production in data science, steps to move from internal projects to production, the lifecycle of a machine learning project, success stories in data science, challenges in quality control, Metaflow, scalability and robustness in production, AI and MLOps, advice for organizations and much more. 

Links Mentioned in the Show:

• Outerbounds
• Metaflow
• Connect with Savin on Linkedin
• [Course] Developing Machine Learning Models for Production
• Related Episode: Why ML Projects Fail, and How to Ensure Success with Eric Siegel, Founder of Machine Learning Week, Former Columbia Professor, and Bestselling Author
• Rewatch sessions from RADAR: AI Edition

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Lot’s of AI use-cases can start with big ideas and exciting possibilities, but turning those ideas into real results is where the challenge lies. How do you take a powerful model and make it work effectively in a specific business context? What steps are necessary to fine-tune and optimize your AI tools to deliver both performance and cost efficiency? And as AI continues to evolve, how do you stay ahead of the curve while ensuring that your solutions are scalable and sustainable? 

Lin Qiao is the CEO and Co-Founder of Fireworks AI. She previously worked at Meta as a Senior Director of Engineering and as head of Meta's PyTorch, served as a Tech Lead at Linkedin, and worked as a Researcher and Software Engineer at IBM. 

In the episode, Richie and Lin explore generative AI use cases, getting AI into products, foundational models, the effort required and benefits of fine-tuning models, trade-offs between models sizes, use cases for smaller models, cost-effective AI deployment, the infrastructure and team required for AI product development, metrics for AI success, open vs closed-source models, excitement for the future of AI development and much more. 

Links Mentioned in the Show:

• Fireworks.ai
• Hugging Face - Preference Tuning LLMs with Direct Preference Optimization Methods
• Connect with Lin
• Course - Artificial Intelligence (AI) Strategy
• Related Episode: Creating Custom LLMs with Vincent Granville, Founder, CEO & Chief Al Scientist at GenAltechLab.com
• Rewatch sessions from RADAR: AI Edition

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Imagine spending millions on data tools only to find you can’t trust the answers they provide. What if different teams define key metrics in different ways? Without a clear, unified approach, chaos reigns, and confidence erodes. What role do data governance and semantic layers play in helping you trust the AI tools you build and the insights you get from your data?

Sarah Levy is a seasoned executive with extensive experience in data science, artificial intelligence, and technology leadership. Currently serving as Co-Founder and CEO of Euno since January 2023, Sarah has previously held significant positions, including VP of Data Science and Data Analytics for Real Estate at Pagaya and CTO at Sight Diagnostics, where innovative advancements in blood testing were achieved. With a strong foundation in research and development from roles at Sight Diagnostics and Natural Intelligence, as well as a robust background in cyber security gained from tenure at the IDF, Sarah has consistently driven impactful decision-making and technological advancements throughout their career. Academic credentials include a Master's degree in Condensed Matter Physics from the Weizmann Institute of Science and a Bachelor's degree in Mathematics and Physics from The Hebrew University of Jerusalem.

In the episode, Richie and Sarah explore the challenges of data governance, the role of semantic layers in ensuring data trust, the emergence of analytics engineers, the integration of AI in data processes, and much more.

Links Mentioned in the Show:

• Euno
• Connect with Sarah
• Course: Responsible AI Data Management
• Related Episode: How Data Leaders Can Make Data Governance a Priority with Saurabh Gupta, Chief Strategy & Revenue Officer at The Modern Data Company
• Rewatch sessions from RADAR: Forward Edition

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In today's AI landscape, organizations are actively exploring how to seamlessly embed AI into their products, systems, processes, and workflows. The success of ChatGPT stands as a testament to this. Its success is not solely due to the performance of the underlying model; a significant part of its appeal lies in its human-centered user experience, particularly its chat interface. Beyond the foundational skills, infrastructure, and tools, it's clear that great design is a crucial ingredient in building memorable AI experiences.

How do you build human-centered AI experiences? What is the role of design in driving successful AI implementations? How can data leaders and practitioners adopt a design lens when building with AI?

Here to answer these questions is Haris Butt, Head of Product Design at ClickUp. ClickUp is a project management tool that's been making a big bet on AI, and Haris plays a key role in shaping how AI is embedded within the platform.

Throughout the episode, Adel & Haris spoke about the role of design in driving human-centered AI experiences, the iterative process of designing with large language models, how to design AI experiences that promote trust, how designing for AI differs from traditional software, whether good design will ultimately end up killing prompt engineering, and a lot more.

2022 was an incredible year for Generative AI. From text generation models like GPT-3 to the rising popularity of AI image generation tools, generative AI has rapidly evolved over the last few years in both its popularity and its use cases.

Martin Musiol joins the show this week to explore the business use cases of generative AI, and how it will continue to impact the way the society interacts with data. Martin is a Data Science Manager at IBM, as well as Co-Founder and an instructor at Generative AI, teaching people to develop their own AI that generates images, videos, music, text and other data. Martin has also been a keynote speaker at various events, such as Codemotion Milan. Having discovered his passion for AI in 2012, Martin has turned that passion into his expertise, becoming a thought leader in AI and machine learning space.

In this episode, we talk about the state of generative AI today, privacy and intellectual property concerns, the strongest use cases for generative AI, what the future holds, and much more.

With AI tools constantly evolving, the potential for innovation seems limitless. But with great potential comes significant costs, and the question of efficiency and scalability becomes crucial. How can you ensure that your AI models are not only pushing boundaries but also delivering results in a cost-effective way? What strategies can help reduce the financial burden of training and deploying models, while still driving meaningful business outcomes? 

Natalia Vassilieva is the VP & Field CTO of ML at Cerebras Systems. Natalia has a wealth of experience in research and development in natural language processing, computer vision, machine learning, and information retrieval. As Field CTO, she helps drive product adoption and customer engagement for Cerebras Systems' wafer-scale AI chips. Previously, Natalia was a Senior Research Manager at Hewlett Packard Labs, leading the Software and AI group. She also served as the head of HP Labs Russia leading research teams focused on developing algorithms and applications for text, image, and time-series analysis and modeling. Natalia has an academic background, having been a part-time Associate Professor at St. Petersburg State University and a lecturer at the Computer Science Center in St. Petersburg, Russia. She holds a PhD in Computer Science from St. Petersburg State University.

Andy Hock is the Senior VP, Product & Strategy at Cerebras Systems. Andy runs the product strategy and roadmap for Cerebras Systems, focusing on integrating AI research, hardware, and software to accelerate the development and deployment of AI models. He has 15 years of experience in product management, technical program management, and enterprise business development; over 20 years of experience in research, algorithm development, and data analysis for image processing; and  9 years of experience in applied machine learning and AI. Previously he was Product Management lead for Data and Analytics for Terra Bella at Google, where he led the development of machine learning-powered data products from satellite imagery. Earlier, he was Senior Director for Advanced Technology Programs at Skybox Imaging (which became Terra Bella following its acquisition by Google in 2014), and before that was a Senior Program Manager and Senior Scientist at Arete Associates. He has a Ph.D. in Geophysics and Space Physics from the University of California, Los Angeles.

In the episode, Richie, Natalia and Andy explore the dramatic recent progress in generative AI, cost and infrastructure challenges in AI, Cerebras’ custom AI chips and other hardware innovations, quantization in AI models, mixture of experts, RLHF, relevant AI use-cases, centralized vs decentralized AI compute, the future of AI and much more. 

Links Mentioned in the Show:

• Cerebras
• Cerebras Launches the World’s Fastest AI Inference
• Connect with Natalia and Andy
• Course: Implementing AI Solutions in Business
• Rewatch sessions from RADAR: AI Edition

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We don’t think about every decision we make. Some decisions are easy and intuitive, others can be riddled with doubt. In a business setting, decision-making is often crucial, and with that comes pressure to ensure we’re making the right decisions in the best way possible. We can often accompany decision-making with context, providing a narrative for how we might approach a decision, citing what data and insights have had significant input into our choices. But how do we approach storytelling and decision-making to breed success? There’s probably no better person to guide us through the ins and outs of decision-making than the co-author of Business Storytelling For Dummies.

Lori L. Silverman is the owner of Partners for Progress, a management consulting firm. As a business strategist, she has consulted with organizations in fifteen industries including financial services, insurance, manufacturing and petroleum companies, government entities, and professional associations. As a keynote speaker, Lori has positively impacted the lives of thousands of people. She has appeared on over fifty radio and television shows to speak about using stories in the workplace and is the co-author of Critical SHIFT and Stories Trainers Tell. 

She’s a pioneer in the business storytelling field, author of five books, and is known worldwide for her work in collaborative data-informed decision-making.

In the episode, Richie and Lori cover common problems in business decision-making, connecting decision-making to business processes, analytics and decision-making, integrating data practitioners and decision-makers, the role of data visualization and narrative storytelling, the SMARTER decision-making methodology, the importance of intuition, challenges faced when applying decision-making methodologies and much more. 

Links Mentioned in the Show

• Business Storytelling For Dummies by Karen Dietz and Lori Silverman
• Connect with Lori on Linkedin
• Level Up with Lori
• Books by Lori
• The SMARTER Framework for Data-Informed Decision Making
• Monetizing Data Through Informed, Collaborative Decision Making
• The Increasingly Vital Role of Business Storytelling in Leadership
• Pre-Suasion: A Revolutionary Way to Influence and Persuade by Robert Cialdini
• [Skill Track] Data Storytelling

One of the most annoying conversations about data that happens far too often is: “Can you do an analysis and answer this business problem for me?” “Sure, where’s the data?” “I don’t know. Probably in one of our databases.” At this point more time is spent hunting for data than actually analyzing it. Rather than grumbling about it, it would obviously be more productive to learn how to solve data discoverability issues. What’s the best way to properly document data sets? How can you avoid spending all your time maintaining dashboards that no one actually uses? 

Shinji Kim is the Founder & CEO of Select Star, an automated data discovery platform that helps you understand your data. Previously, she was the CEO of Concord Systems (concord.io), a NYC-based data infrastructure startup acquired by Akamai Technologies in 2016. She led building Akamai’s new IoT data platform for real-time messaging, log processing, and edge computing. Prior to Concord, Shinji was the first Product Manager hired at Yieldmo, where she led the Ad Format Lab, A/B testing, and yield optimization. Before Yieldmo, she was analyzing data and building enterprise applications at Deloitte Consulting, Facebook, Sun Microsystems, and Barclays Capital. Shinji studied Software Engineering at University of Waterloo and General Management at Stanford GSB. She advises early stage startups on product strategy, customer development, and company building.

In the episode, Richie and Shinji explore the importance of data governance, the utilization of data, data quality, challenges in data usage, why documentation matters, metadata and data lineage, improving collaboration between data and business teams, data governance trends to look forward to, and much more. 

Links Mentioned in the Show:

• Select Star
• Connect with Shinji
• [Course] Data Governance Concepts
• Related Episode: Making Data Governance Fun with Tiankai Feng, Data Strategy & Data Governance Lead at ThoughtWorks
• Rewatch sessions from RADAR: AI Edition

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One of the big use cases of generative AI is having small applications to solve specific tasks. These are known as AI agents or AI assistants. Since they’re small and narrow in scope, you probably want to create and use lots of them, which means you need to be able to create them cheaply and easily. I’m curious as to how you go about doing this from an organizational point of view. Who needs to be involved? What’s the workflow and what technology do you need?

Dmitry Shapiro is the CEO of MindStudio. He was previously the CTO at MySpace and a product manager at Google. Dmitry is also a serial entrepreneur, having founded the web-app development platform Koji, acquired by Linktree, and Veoh Networks, an early YouTube competitor. He has extensive experience in building and managing engineering, product, and AI teams.

In the episode, Richie and Dmitry explore generative AI applications, AI in SaaS, approaches to AI implementation, selecting processes for automation, changes in sales and marketing roles, MindStudio, AI governance and privacy concerns, cost management, the limitations and future of AI assistants, and much more.

Links Mentioned in the Show:

• MindStudio
• Connect with Dmitry
• [Webinar] Dmitry at RADAR: From Learning to Earning: Navigating the AI Job Landscape
• Related Episode: Designing AI Applications with Robb Wilson, Co-Founder & CEO at Onereach.ai
• Rewatch sessions from RADAR: AI Edition

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The data journey is a slow painstaking process. But knowing where to start and the areas to focus on can help any organization reach its goals faster.

Today’s guest, Vijay Yadav, Director of Quantitative Sciences & Head of Data Science at the Center for Mathematical Sciences at Merck, explains the 6 key elements of data strategy, complete with advice on how to navigate each.

Join us as we discuss:

• The different components of a data strategy
• Shifting mindset within the C-Suite
• Structuring the operating model
• Enabling people to work with data at scale
• Most effective tactics to kickstart a community around data science

Find every episode of DataFramed on Apple, Spotify, and more. Find us on our website and join the conversation on LinkedIn.

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Generative AI has made a mark everywhere, including BI platforms, but how can you combine AI and BI together? What effects can this have across organizations? With constituent aspects such as data quality, your AI strategy, and the specific use-case you’re trying to solve, it’s important to get the full picture and tread with intent. What are the subtleties that we need to get right in order for this marriage to work to its full potential?

Nick Magnuson is the Head of AI at Qlik, executing the organization’s AI strategy, solution development, and innovation. Prior to Qlik, Nick was the CEO of Big Squid, which was acquired by Qlik in 2021. Nick has previously held executive roles in customer success, product, and engineering in the field of machine learning and predictive analytics. As a practitioner in this field for over 20 years, Nick has published original research in these areas, as well as cognitive bias and other quantitative topics. He has also served as an advisor to other analytics platforms and start-ups. A long-time investment professional, Nick continues to hold his Chartered Financial Analyst designation and is a past member of the Chicago Quantitative Alliance and Society of Quantitative Analysts. 

In the episode, Richie and Nick explore what Qlik offers, including products like Sense and Staige, how Staige uses AI to enhance customer capabilities, use cases of generative AI, advice on data privacy and security when using AI, data quality and its effect on the success of AI tools, AI strategy and leadership, how data roles are changing and the emergence of new positions, and much more. 

Links Mentioned in the Show:

• Qlik
• Qlik Staige
• Qlik Sense
• [Skill Track] AI Fundamentals
• Related Episode: Adapting to the AI Era with Jason Feifer, Editor in Chief of Entrepreneur Magazine
• Sign up to RADAR: The Analytics Edition

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As data volumes grow and become ever-more complex, the role of the data analyst has never been more important. At the disposal of the modern data analyst, are tools that reduce time to insight, and increase collaboration. However, as the tools of a data analyst evolve, so do the skills. 

Today’s guest, Peter Fishman, Co-Founder at Mozart Data, speaks to this exact notion. 

Join us as we discuss:

• Defining a data-driven organization & main challenges
• Breaking down the modern data stack & what it means
• What makes a great data analyst
• How data analysts can develop deep subject matter expertise in the areas they serve

Find every episode of DataFramed on Apple, Spotify, and more. Find us on our website and join the conversation on LinkedIn.

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Despite the critical role of analytics in guiding business decisions, organizations continue to face significant challenges in harnessing its full potential. As data sets expand and deadlines shrink, the urgency to scale analytics processes becomes paramount. What data leaders now need to focus on are essential strategies for analytics at scale, including fostering a culture of continuous learning, prioritizing data governance, and leveraging generative AI.

Libby Duane Adams is the Chief Advocacy Officer and co-founder of Alteryx. She is responsible for strengthening upskilling and reskilling efforts for Alteryx customers to enable a culture of analytics, scaling the presence of the Alteryx SparkED education program and furthering diversity and inclusion in the workplace. As the former Chief Customer Officer, Libby has helped many Fortune 100 executives to identify and seize market opportunities, outsmart their competitors, and drive more revenue from their current businesses using analytics. 

In the episode, Richie and Libby explore the differences between analytics and business intelligence, analytics as a team sport, the importance of speed in analytics, generative AI and its implications in analytics, the role of data quality and governance, Alteryx’s AI platform, data skills as a workplace necessity, using AI to automate documentation and insights, success stories and mistakes within analytics, and much more. 

Links Mentioned in the Show:

• Alteryx
• Alteryx SparkED Program
• [Course] Introduction to Alteryx
• Related Episode: From Data Literacy to AI Literacy with Cindi Howson, Chief Data Strategy Officer at ThoughtSpot
• Sign up to RADAR: The Analytics Edition

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Building a successful data engineering team involves more than just hiring skilled individuals—it requires fostering a culture of trust, collaboration, and continuous learning. But how do you start from scratch and create a team that not only meets technical demands but also drives business value? What key traits should you look for in your early hires, and how do you ensure your team’s projects align with the company’s goals?

Liya Aizenberg is Director of Data Engineering at Away and a seasoned data leader with over 22 years of experience spearheading innovation in scalable data engineering pipelines and distribution solutions. She has built successful data teams that integrate seamlessly with various business functions, serving as invaluable organizational partners. She focuses on promoting data-driven approaches to empower organizations to make proactive decisions based on timely and organized data, shifting from reactive to proactive business strategies. Additionally, as a passionate advocate for Women in Tech, she actively contributes to fostering diversity and inclusion in the technology industry.

In the episode, Adel and Liya explore the key attributes that forge an effective data engineering team, traits to look for in new hires, what technical skill sets set people up for success in a data engineering team, leveraging knowledge transfer between external experts and internal stakeholders, upskilling and career growth, aligning data engineering initiatives with business goals, measuring the ROI of data projects, working agile in data engineering, balancing innovation and practicality, future trends and much more. 

Links Mentioned in the Show:

• Away Travel
• Connect with Liya on Linkedin
• [Career Track] Data Engineer with Python
• Related Episode: Scaling Data Engineering in Retail with Mo Sabah, SVP of Engineering & Data at Thrive Market
• Sign up to RADAR: AI Edition

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As AI becomes more accessible, a growing question is: should machine learning experts always be the ones training models, or is there a better way to leverage other subject matter experts in the business who know the use-case best? What if getting started building AI apps required no coding skills? As businesses look to implement AI at scale, what part can no-code AI apps play in getting projects off the ground, and how feasible are smaller, tailored solutions for  department specific use-cases?

Birago Jones is the CEO at Pienso. Pienso is an AI platform that empowers subject matter experts in various enterprises, such as business analysts, to create and fine-tune AI models without coding skills. Prior to Pienso, Birago was a Venture Partner at Indicator Ventures and a Research Assistant at MIT Media Lab where he also founded the Media Lab Alumni Association.

Karthik Dinakar is a computer scientist specializing in machine learning, natural language processing, and human-computer interaction. He is the Chief Technology Officer and co-founder at Pienso. Prior to founding Pienso, Karthik held positions at Microsoft and Deutsche Bank. Karthik holds a doctoral degree from MIT in Machine Learning.

In the episode, Richie, Birago and Karthik explore why no-code AI apps are becoming more prominent, uses-cases of no-code AI apps, the steps involved in creating an LLM, the benefits of small tailored models, how no-code can impact workflows, cost in AI projects, AI interfaces and the rise of the chat interface, privacy and customization, excitement about the future of AI, and much more. 

Links Mentioned in the Show:

• Pienso
• Google Gemini for Business
• Connect with Birago and Karthik
• Andreesen Horowitz Report: Navigating the High Cost of AI Compute
• Course: Artificial Intelligence (AI) Strategy
• Related Episode: Designing AI Applications with Robb Wilson, Co-Founder & CEO at Onereach.ai
• Rewatch sessions from RADAR: AI Edition

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Data Analytics has played a major role in Chelsea’s journey to becoming the seventh most valuable football club in the world, Chelsea has won six league titles, eight FA Cups, five League Cups, and two Champions League titles.

Today, we are going behind the scenes at Chelsea FC to see how they use data analytics to analyze matches, inform tactical decision-making, and drive matchday success in one of the world’s top football leagues, just in time for the 2022 FIFA World Cup in Qatar!

Federico Bettuzzi is a Data Scientist at Chelsea FC. As a specialist in match analytics, Federico works with Chelsea’s first team to inform tactical decision making during matches. Federico joins the show to break down how he gathers and synthesizes data, how they develop match analyses for tactical reviews, how managers prioritize data analytics differently, how to balance long-term and short-term projects, and much more.

When many people talk about leading effective Data Science teams in large organizations, it’s easy for them to forget how much effort, intentionality, vision, and leadership are involved in the process.

Glenn Hofmann, Chief Analytics Officer at New York Life Insurance, is no stranger to that work. With over 20 years of global leadership experience in data, analytics, and AI that spans the US, Germany, and South Africa, Glenn knows firsthand what it takes to build an effective data science function within a large organization.

In this episode, we talk about how he built NeW York Life Insurance’s 50-person data science and AI function, how they utilize skillsets to offer different career paths for data scientists, building relationships across the organization, and so much more.

[Announcement] Join us for DataCamp Radar, our digital summit on June 23rd. During this summit, a variety of experts from different backgrounds will be discussing everything related to the future of careers in data. Whether you're recruiting for data roles or looking to build a career in data, there’s definitely something for you. Seats are limited, and registration is free, so secure your spot today on https://events.datacamp.com/radar/

As we look back at 2024, we're highlighting some of our favourite episodes of the year, and with 100 of them to choose from, it wasn't easy!

The four guests we'll be recapping with are:

• Lea Pica - A celebrity in the data storytelling and visualisation space. Richie and Lea cover the full picture of data presentation, how to understand your audience, how to leverage hollywood storytelling and more. Out December 19.
• Alex Banks - Founder of Sunday Signal. Adel and Alex cover Alex’s journey into AI and what led him to create Sunday Signal, the potential of AI, prompt engineering at its most basic level, chain of thought prompting, the future of LLMs and more. Out December 23.
• Don Chamberlin - The renowned co-inventor of SQL. Richie and Don explore the early development of SQL, how it became standardized, the future of SQL through NoSQL and SQL++ and more. Out December 26.
• Tom Tunguz - general Partner at Theory Ventures, a $235m VC firm. Richie and Tom explore trends in generative AI, cloud+local hybrid workflows, data security, the future of business intelligence and data analytics, AI in the corporate sector and more. Out December 30.

Your data project doesn't end once you have results. In order to have impact, you need to communicate those results to others. Presentations filled with endless tables and technical jargon can easily become tedious, leading your audience to lose interest or misunderstand your point.

Data storytelling provides a solution to this: by creating a narrative around your results you can increase engagement and understanding from your audience. This is an art, and there are so many factors that contribute to visualizing data and creating a compelling story, it can be overwhelming. However, with the right approach, creating data stories can become second nature. In this special episode of DataFramed, we join forces with the Present Beyond Measure podcast to glean the best data presentation practices from one of the leading voices in the space.

Lea Pica host of the Founder and Host of the Present Beyond Measure podcast and is a seasoned digital analytics practitioner, social media marketer and blogger with over 11 years of experience building search marketing and digital analytics practices for companies like Scholastic, Victoria’s Secret and Prudential.

Present Beyond Measure’s mission is to bring their teachings to the digital marketing and web analytics communities, and empower anyone responsible for presenting data to an audience.

In the full episode, Richie and Lea cover the full picture of data presentation, how to understand your audience, leverage hollywood storytelling, data storyboarding and visualization, the use of imagery in presentations, cognitive load management, the use of throughlines in presentations, how to improve your speaking and engagement skills, data visualization techniques in business setting and much more. 

Links Mentioned in the Show:

• Present Beyond Measure
• Lea’s Book
• Connect with Lea on Linkedin
• Hollywood Storytelling
• [Course] Data Storytelling Concepts

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Everyone has seen the reach and impact of generative AI, and with countless use-cases across a variety of fields, the question is often not "can we do things with AI?", but rather "what should we do with AI?". What are the key areas where generative AI has had a profound impact already? Which economies, industries, and businesses have taken full advantage of the abilities of GenAI already? It takes a lot of wisdom and experience within the data & AI space to distill high-level insights from such a rapidly changing world, but, luckily we have one of the best people in the world to quiz on the current landscape and future of AI. 

Bernard Marr is an internationally best-selling business author, keynote speaker and strategic advisor to companies and governments. He advises many of the world’s best-known organizations such as Amazon, Google, Microsoft, IBM, Toyota, and more.

LinkedIn has recently ranked Bernard as one of the top 5 business influencers in the world. He has authored 19 best-selling books, including his new book Generative AI in Practice: 100+ Amazing Ways Generative Artificial Intelligence is Changing Business and Society. Every day Bernard actively engages his over 4 million social media followers. He is one of the world’s most highly respected experts when it comes to future trends, strategy, business performance, digital transformation and the intelligent use of data and AI in business.

In the episode, Richie and Bernard explore how AI will impact society through the augmentation of jobs, the importance of developing skills that won’t be easily replaced by AI, how generative AI is revolutionizing creative fields already, how AI will impact education, AI’s role in coding and software development, use cases of generative AI in business, how personalization is set to improve through AI, concerns and ethical considerations surrounding AI, why we should be optimistic about the future of AI, and much more. 

Links Mentioned in the Show:

• Bernard’s book: Generative AI in Practice
• Bernard’s Website, Twitter and Linkedin
• [Skill Track] AI Business Fundamentals
• Related Episode: Adapting to the AI Era with Jason Feifer, Editor in Chief of Entrepreneur Magazine

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Today marks the second episode in our DataFramed Careers Series. In this series, we will interview a diverse range of thought leaders and experts on the different aspects of landing a data role in 2022.

In the first episode of the series, Sadie discussed at great length the importance of having a solid data science portfolio to land a role in data. But what makes a great data science portfolio?

Nick Singh, co-author of Acing the Data Science Interview, joins the show to share everything you need to know to create high-quality, thorough portfolio projects.

Throughout the episode, we discuss

• How portfolio projects build experience
• Who should be focusing on portfolio projects
• The different types of portfolio projects
• Biggest pitfalls when creating portfolio projects
• How to get noticed with your portfolio projects
• Concrete examples of great portfolio projects 

[Announcement] Join us for DataCamp Radar, our digital summit on June 23rd. During this summit, a variety of experts from different backgrounds will be discussing everything related to the future of careers in data. Whether you're recruiting for data roles or looking to build a career in data, there’s definitely something for you. Seats are limited, and registration is free, so secure your spot today on https://events.datacamp.com/radar/

From the dawn of humanity, decisions, both big and small, have shaped our trajectory. Decisions have built civilizations, forged alliances, and even charted the course of our very evolution. And now, as data & AI become more widespread, the potential upside for better decision making is massive. Yet, like any technology, the true value of data & AI is realized by how we wield it. 

We're often drawn to the allure of the latest tools and techniques, but it's crucial to remember that these tools are only as effective as the decisions we make with them. ChatGPT is only as good as the prompt you decide to feed it and what you decide to do with the output. A dashboard is only as good as the decisions that it influences. Even a data science team is only as effective as the value they deliver to the organization. 

So in this vast landscape of data and AI, how can we master the art of better decision making? How can we bridge data & AI with better decision intelligence?

​​Cassie Kozyrkov founded the field of Decision Intelligence at Google where, until recently, she served as Chief Decision Scientist, advising leadership on decision process, AI strategy, and building data-driven organizations. Upon leaving Google, Cassie started her own company of which she is the CEO, Data Scientific. In almost 10 years at the company, Cassie personally trained over 20,000 Googlers in data-driven decision-making and AI and has helped over 500 projects implement decision intelligence best practices. Cassie also previously served in Google's Office of the CTO as Chief Data Scientist, and the rest of her 20 years of experience was split between consulting, data science, lecturing, and academia. 

Cassie is a top keynote speaker and a beloved personality in the data leadership community, followed by over half a million tech professionals. If you've ever went on a reading spree about AI, statistics, or decision-making, chances are you've encountered her writing, which has reached millions of readers. 

In the episode Cassie and Richie explore misconceptions around data science, stereotypes associated with being a data scientist, what the reality of working in data science is, advice for those starting their career in data science, and the challenges of being a data ‘jack-of-all-trades’. 

Cassie also shares what decision-science and decision intelligence are, what questions to ask future employers in any data science interview, the importance of collaboration between decision-makers and domain experts, the differences between data science models and their real-world implementations, the pros and cons of generative AI in data science, and much more. 

Links mentioned in the Show:

• Data scientist: The sexiest job of the 22nd century
• The Netflix Prize
• AI Products: Kitchen Analogy
• Type one, Two & Three Errors in Statistics
• Course: Data-Driven Decision Making for Business
• Radar: Data & AI Literacy...

We’re improving DataFramed, and we need your help! We want to hear what you have to say about the show, and how we can make it more enjoyable for you—find out more here.

Understanding where the data you use comes from, how to use it responsibly, and how to maximize its value has become essential. But as data sources multiply, so do the complexities around data privacy, customization, and ownership. How can companies capture and leverage the right data to create meaningful customer experiences while respecting privacy? And as data drives more personalized interactions, what steps can businesses take to protect sensitive information and navigate the increasingly complex regulatory picture? 

Jonathan Bloch is CEO at Exchange Data International (EDI) and a seasoned businessman with 40 years experience in information provision. He started work in the newsletter industry and ran the US subsidiary of a UK public company before joining its main board as head of its publishing division. He has been a director and/or chair of several companies and is currently a non executive director of an FCA registered investment bank. In 1994 he founded Exchange Data International (EDI) a London based financial data provider. EDI now has over 450 clients across three continents and is based in the UK, USA, India and Morocco employing 500 people.

Scott Voigt is CEO and co-founder at Fullstory. Scott has enjoyed helping early-stage software businesses grow since the mid 90s, when he helped launch and take public nFront—one of the world's first Internet banking service providers. Prior to co-founding Fullstory, Voigt led marketing at Silverpop before the company was acquired by IBM. Previously, he worked at Noro-Moseley Partners, the Southeast's largest Venture firm, and also served as COO at Innuvo, which was acquired by Google. Scott teamed up with two former Innuvo colleagues, and the group developed the earliest iterations of Fullstory to understand how an existing product was performing. It was quickly apparent that this new platform provided the greatest value—and the rest is history.

In the episode, Richie, Jonathan and Scott explore first-party vs third-party data, protecting corporate data, behavioral data, personalization, data sourcing strategies, platforms for storage and sourcing, data privacy, synthetic data, regulations and compliance, the future of data collection and storage, and much more. 

Links Mentioned in the Show:

• Fullstory
• Exchange Data International
• Connect with Jonathan and Scott
• Course: Understanding GDPR
• Related Episode: How Data and AI are Changing Data Management with Jamie Lerner, CEO, President, and Chairman at Quantum
• Sign up to RADAR: Forward Edition

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While leading a mature data science function is a challenge in its own right, building one from scratch at an organization can be just as, if not even more, difficult. As a data leader, you need to balance short-term goals with a long-term vision, translate technical expertise into business value, and develop strong communication skills and an internalized understanding of a business's values and goals in order to earn trust with key stakeholders and build the right team.

Elettra Damaggio is no stranger to this process. Elettra is the Director for Global Data Science at StoneX, an institutional-grade financial services network that connects clients to the global markets ecosystem. Elettra has over 10 years of experience in machine learning, AI, and various roles within digital transformation and digital business growth.

In this episode, she shares how data leaders can balance short-term wins with long-term goals, how to earn trust with stakeholders, major challenges when launching a data science function, and advice she has for new and aspiring data practitioners.

Speedily adopting new technologies can give your business a competitive advantage, but with so much happening in the world of generative AI, it's difficult to know what to adopt. In this episode, Richie chats to two venture capitalists to get their view on the global AI landscape, where we are in the AI hype cycle, and how to adopt AI tech. Beyond this, we explore Rocketship.vc's use of data and algorithms to make investment decisions in early-stage startups. If our previous episode’s deep dive into 2024’s data & AI trends with VC Tom Tunguz got you excited about how investors are looking at the market at the moment, then this episode is sure to do the same. This time, we have twice the insight, thanks to our two guests.

Madhu Shalini Iyer is a Managing Partner at Rocketship.vc, a Silicon Valley based fund investing globally. She was the Chief Data Officer of Gojek and helped grow the business into a $10 billion unicorn. In addition to being a board member, she started the Singapore office and played an active role in the strategy, new business development, and ‘data as a competitive advantage’. Prior to Gojek, Madhu was part of the founding team of Intuit’s Quickbooks Lending Platform. As the data science leader at Intuit, Madhu helped grow the platform to $300 million and holds 2 patents in the areas of user data augmented algorithms for financial inclusion. Madhu was also the Chief Data Officer for Ethoslending. There she built the underwriting platform and was responsible for all b2c revenue, resulting in $65 million gross market value per month. Madhu was further responsible for building and running the marketing team. Prior, Madhu was a partner at a $150m private equity fund, Stem Financial, in Hong Kong. She started her career as a senior data scientist with a leading think tank in Menlo Park, CA.

Sailesh Ramakrishnan is also a Managing Partner at Rocketship.vc. Prior to Rocketship.vc, Sailesh was CTO and co-founder of LocBox (acquired by Square), a startup focussed on marketing for local businesses. Sailesh worked with Anand and Venky at their previous startup Kosmix, and continued on to Walmart as a Director of Engineering at @WalmartLabs. Before jumping into the startup world, Sailesh worked as a Computer Scientist at NASA Ames Research Center. Sailesh earned his Bachelors degree in Civil Engineering from IIT Madras, his Masters degree in Construction Management from Virginia Tech and another Master degree in Intelligent Systems from University of Pittsburgh. He was a Ph.D. candidate in Artificial Intelligence at the University of Michigan.

In the episode, Richie, Madhu and Sailesh explore the generative AI revolution, categorizing generative AI tools, the impact of genAI across industries, investment philosophy and data-driven decision-making, the challenges and opportunities when investing in AI, future trends and predictions, regulatory and ethical considerations of AI, and much more. 

Links Mentioned in the Show:

• Rocketship.vc
• [Course] Implementing AI Solutions in Business
• Related Episode: Inside Algorithmic Trading with Anthony Markham, Vice President, Quantitative Developer at Deutsche Bank
• Sign up to RADAR: AI Edition

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AI is becoming a key tool in industries far beyond just tech. From automating tasks in the movie industry to revolutionizing drug development in life sciences, AI is transforming how we work. But with this growth comes important questions: How is AI really impacting jobs? Are we just increasing efficiency, or are we replacing human roles? And how can companies effectively store and leverage the vast amounts of data being generated every day to gain a competitive advantage?

Jamie Lerner is the President and CEO of Quantum, a company specializing in data storage, management, and protection. Since taking the helm in 2018, Lerner has steered Quantum towards innovative solutions for video and unstructured data. His leadership has been marked by strategic acquisitions and product launches that have significantly enhanced the company's market position. Before joining Quantum, Jamie worked at Cisco, Seagate, CITTIO, XUMA, and Platinum Technology. At Quantum, Lerner has been instrumental in shifting the company's focus towards data storage, management, and protection for video and unstructured data, driving innovation and strategic acquisitions to enhance its market position.

In the episode, Richie and jamie explore AI in subtitling, translation, and the movie industry at large, AI in sports, AI in business and scientific research, AI ethics, infrastructure and data management, video and image data in business, challenges of working with AI in video, excitement vs fear in AI and much more. 

Links Mentioned in the Show:

• Quantum
• Connect with Jamie
• Career Track: Data Engineer in Python
• Related Episode: Seeing the Data Layer Through Spatial Computing with Cathy Hackl and Irena Cronin
• Rewatch sessions from RADAR: AI Edition

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Over the past 199 episodes of DataFramed, we’ve heard from people at the forefront of data and AI, and over the past year we’ve constantly looked ahead to the future AI might bring. But all of the technologies and ways of working we’ve witnessed have been built on foundations that were laid decades ago. For our 200th episode, we’re bringing you a special guest and taking a walk down memory lane—to the creation and development of one of the most popular programming languages in the world.

Don Chamberlin is renowned as the co-inventor of SQL (Structured Query Language), the predominant database language globally, which he developed with Raymond Boyce in the mid-1970s. Chamberlin's professional career began at IBM Research in Yorktown Heights, New York, following a summer internship there during his academic years. His work on IBM's System R project led to the first SQL implementation and significantly advanced IBM’s relational database technology. His contributions were recognized when he was made an IBM Fellow in 2003 and later a Fellow of the Computer History Museum in 2009 for his pioneering work on SQL and database architectures. Chamberlin also contributed to the development of XQuery, an XML query language, as part of the W3C, which became a W3C Recommendation in January 2007. Additionally, he holds fellowships with ACM and IEEE and is a member of the National Academy of Engineering.

In the episode, Richie and Don explore his early career at IBM and the development of his interest in databases alongside Ray Boyce, the database task group (DBTG), the transition to relational databases and the early development of SQL, the commercialization and adoption of SQL, how it became standardized, how it evolved and spread via open source, the future of SQL through NoSQL and SQL++ and much more. 

Links Mentioned in the Show:

• The first-ever journal paper on SQL. SEQUEL: A Structured English Query Language
• Don’s Book: SQL++ for SQL Users: A Tutorial
• System R: Relational approach to database management
• SQL Courses
• SQL Articles, Tutorials and Code-Alongs
• Related Episode: Scaling Enterprise Analytics with Libby Duane Adams, Chief Advocacy Officer and Co-Founder of Alteryx
• Rewatch sessions from RADAR: The Analytics Edition

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In January 2024, six activists were identified by British Police in London, suspected of planning to disrupt the London Stock Exchange through a lock-in. In an attempt to prevent the building from opening for trading. Despite the foiled attempt, the strategy for this protest was inherently flawed. Trading no longer requires a busy exchange with raucous shouting and phone calls to facilitate the flow of investment around the world. Nowadays, machines can trade at a fraction of a second, ingesting huge amounts of real-time data to execute finely tuned-trading strategies. But who programs these trading machines, how do we assess risk when trading at such a high volume and in such short periods of time?

Anthony Markham is Vice President, Quantitative Developer at Deutsche Bank. With a background in Aerospace and Software Engineering, Anthony has experience in Data Science, facial recognition research, tertiary education, and Quantitative Finance, developing mostly in Python, Julia, and C++. When not working, Anthony enjoys working on personal projects, flying aircraft, and playing sports.

In the episode, Richie and Anthony cover what algorithmic trading is, the use of machine learning techniques in trading strategies, the challenges of handling large datasets with low latency, risk management in algorithmic trading, data analysis techniques for handling time series data, the challenges of deep neural networks in trading, the diverse roles and skills of those who work in algorithmic trading and much more. 

Links Mentioned in the Show:

• Flash crash of 2010
• KDB+
• Q Query Language
• [Course] Quantitative Risk Management in Python
• Understanding Value at Risk (VaR)

One of the biggest surprises of the generative AI revolution over the past 2 years lies in the counter-intuitiveness of its most successful use cases. Counter to most predictions made about AI years ago, AI-assisted coding, specifically AI-assisted data work, has been surprisingly one of the biggest killer apps of generative AI tools and copilots. However, what happens when we take this notion even further? How will analytics workflows look like when generative AI tools can also assist us in problem-solving? What type of analytics use cases can we expect to operationalize, and what tools can we expect to work with when AI systems can provide scalable qualitative data instead of relying on imperfect quantitative proxies? Today’s guest calls this future “weird”. 

Benn Stancil is the Field CTO at ThoughtSpot. He joined ThoughtSpot in 2023 as part of its acquisition of Mode, where he was a Co-Founder and CTO. While at Mode, Benn held roles leading Mode’s data, product, marketing, and executive teams. He regularly writes about data and technology at benn.substack.com. Prior to founding Mode, Benn worked on analytics teams at Microsoft and Yammer.

Throughout the episode, Benn and Adel talk about the nature of AI-assisted analytics workflows, the potential for generative AI in assisting problem-solving, how he imagines analytics workflows to look in the future, and a lot more. 

About the AI and the Modern Data Stack DataFramed Series

This week we’re releasing 4 episodes focused on how AI is changing the modern data stack and the analytics profession at large. The modern data stack is often an ambiguous and all-encompassing term, so we intentionally wanted to cover the impact of AI on the modern data stack from different angles. Here’s what you can expect:

• Why the Future of AI in Data will be Weird with Benn Stancil, CTO at Mode & Field CTO at ThoughtSpot — Covering how AI will change analytics workflows and tools 
• How Databricks is Transforming Data Warehousing and AI with Ari Kaplan, Head Evangelist & Robin Sutara, Field CTO at Databricks — Covering Databricks, data intelligence and how AI tools are changing data democratization
• Adding AI to the Data Warehouse with Sridhar Ramaswamy, CEO at Snowflake — Covering Snowflake and its uses, how generative AI is changing the attitudes of leaders towards data, and how to improve your data management
• Accelerating AI Workflows with Nuri Cankaya, VP of AI Marketing & La Tiffaney Santucci, AI Marketing Director at Intel — Covering AI’s impact on marketing analytics, how AI is being integrated into existing products, and the democratization of AI

Links Mentioned in the Show:

• Mode Analytics
• ThoughtSpot acquires Mode: Empowering data teams to bring Generative AI to BI
• Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are
• [Course] Generative AI for Business
• [Skill Track] SQL Fundamentals
• Related Episode: The Future of Marketing Analytics with Cory Munchbach, CEO at...

The insurance industry thrives on data from utilizing data and analytics to determine policy rates for customers to working with relevant partners in the industry to improve their products and services, data is embedded in everything that insurance companies do.

But insurance companies also have a number of hurdles to overcome, whether it’s transitioning legacy data into new processes and technology, balancing new projects and models with ever-changing regulatory standards, and balancing the ethical considerations of how to best utilize data without resulting in unintended consequences for the end user.

That’s why we’ve brought Rob Reynolds onto the show. Rob is the VP and Chief Data & Analytics Officer at W. R. Berkley, a multinational insurance holding company specializing in property and casualty insurance. Rob brings over two decades of experience in Data Science, IT, and technology leadership, with a particular expertise in building departments and establishing highly functioning teams, especially in highly dynamic environments.

In this episode, we talk in-depth about how insurance companies utilize data, the most important skills for anyone looking for data science jobs in the insurance industry, why the need for thoughtful criticism is growing in data science, and how an expertise in communication will put you ahead of the pack.

We’re improving DataFramed, and we need your help! We want to hear what you have to say about the show, and how we can make it more enjoyable for you—find out more here.

With the EU AI Act coming into effect, the AI industry faces a pivotal moment. This regulation is a landmark step for AI governance and challenges data and AI teams to rethink their approach to AI development and deployment. How will this legislation influence the way AI systems are built and used? What are the key compliance requirements that organizations need to be aware of? And how can companies balance regulatory obligations with the drive for innovation and growth?

Dan Nechita led the technical negotiations for the EU Artificial Intelligence Act on behalf of the European Parliament. For the 2019-2024 mandate, besides artificial intelligence, he focused on digital regulation, security and defense, and the transatlantic partnership as Head of Cabinet for Dragos Tudorache, MEP. Previously, he was a State Counselor for the Romanian Prime Minister with a mandate on e-governance, digitalization, and cybersecurity. He worked at the World Security Institute (the Global Zero nuclear disarmament initiative); at the Brookings Institution Center of Executive Education; as a graduate teaching assistant at the George Washington University; at the ABC News Political Unit; and as a research assistant at the Arnold A. Saltzman Institute of War and Peace at Columbia. He is an expert project evaluator for the European Commission and a member of expert AI working groups with the World Economic Forum and the United Nations. Dan is a graduate of the George Washington University (M.A.) and Columbia University in the City of New York (B.A.).

In the episode, Adel and Dan explore the EU AI Act's significance, risk classification frameworks, organizational compliance strategies, the intersection with existing regulations, AI literacy requirements, and the future of AI legislation, and much more.

Links Mentioned in the Show:

• The EU AI Act
• Connect with Dan
• Course: Understanding the EU AI Act
• Related Episode: Guardrails for the Future of AI with Viktor Mayer-Schönberger, Professor of Internet Governance and Regulation at the University of Oxford
• Rewatch sessions from RADAR: Forward Edition

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It's been a year since ChatGPT burst onto the scene. It has given many of us a sense of the power and potential that LLMs hold in revolutionizing the global economy. But the power that generative AI brings also comes with inherent risks that need to be mitigated. 

For those working in AI, the task at hand is monumental: to chart a safe and ethical course for the deployment and use of artificial intelligence. This isn't just a challenge; it's potentially one of the most important collective efforts of this decade. The stakes are high, involving not just technical and business considerations, but ethical and societal ones as well.

How do we ensure that AI systems are designed responsibly? How do we mitigate risks such as bias, privacy violations, and the potential for misuse? How do we assemble the right multidisciplinary mindset and expertise for addressing AI safety? 

Reid Blackman, Ph.D., is the author of “Ethical Machines” (Harvard Business Review Press), creator and host of the podcast “Ethical Machines,” and Founder and CEO of Virtue, a digital ethical risk consultancy. He is also an advisor to the Canadian government on their federal AI regulations, was a founding member of EY’s AI Advisory Board, and a Senior Advisor to the Deloitte AI Institute. His work, which includes advising and speaking to organizations including AWS, US Bank, the FBI, NASA, and the World Economic Forum, has been profiled by The Wall Street Journal, the BBC, and Forbes. His written work appears in The Harvard Business Review and The New York Times. Prior to founding Virtue, Reid was a professor of philosophy at Colgate University and UNC-Chapel Hill.

In the episode, Reid and Richie discuss the dominant concerns in AI ethics, from biased AI and privacy violations to the challenges introduced by generative AI, such as manipulative agents and IP issues. They delve into the existential threats posed by AI, including shifts in the job market and disinformation. Reid also shares examples where unethical AI has led to AI projects being scrapped, the difficulty in mitigating bias, preemptive measures for ethical AI and much more. 

Links mentioned in the show:

• Ethical Machines by Reid Blackman
• Virtue Ethics Consultancy
• Amazon’s Scrapped AI Recruiting Tool
• NIST AI Risk Management Framework
• Course: AI Ethics
• DataCamp Radar: Data & AI Literacy

Driving impact with analytics goes beyond numbers and graphs; it's about telling a story that resonates. In this session, Brent Dykes, author of "Effective Data Storytelling" & the Founder & Chief Data Storyteller at AnalyticsHero, Lea Pica, author of "Present Beyond Measure" & the Founder at Story-driven by Data, and Andy Cotgreave, co-author of "The Big Book of Dashboards" and Senior Data Evangelist at Tableau, will unveil how to transform data into compelling narratives. 

They shed light on the art of blending analytics with storytelling, a key to making data-driven insights both understandable and influential within any organization.

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

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The convergence of AI and business intelligence is creating new opportunities for innovation. As AI becomes more embedded in BI tools, the challenge lies in fostering a data-driven culture within organizations. How can professionals bridge the gap between intuition and data-driven decision-making? What strategies can be employed to cultivate a culture where data is at the forefront of business decisions? And how can AI tools be leveraged to make data insights more accessible to all employees?

Omri Kohl is the CEO and co-founder of Pyramid Analytics, the Trusted Analytics Platform built for the enterprise. He leads Pyramid’s strategy and operations through a fast-growing data and analytics market. Kohl brings a deep understanding of analytics and AI technologies, valuable management experience, and a natural ability to challenge conventional thinking. Since Kohl founded Pyramid in 2009, it has achieved significant market success and customer growth. Kohl is a highly experienced entrepreneur with a proven track record developing and managing fast-growth companies.

In the episode, Richie and Omri explore the evolution of BI with AI, the importance of data-driven culture, the role of generative BI in democratizing insights, the balance between intuition and data, and much more.

Links Mentioned in the Show:

• Pyramid Analytics
• Connect with Omri
• Pyramid Analytics GenBI Demo
• Course: Introduction to Data Culture
• Related Episode: Self-Service Business Intelligence with Sameer Al-Sakran, CEO at Metabase
• Rewatch sessions from RADAR: Forward Edition

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Throughout the middle east, efforts are underway to build smart cities from the ground up.

But to create a modern, intelligently-designed city, you first need to lay a solid foundation.

And the strongest foundation you can build a smart city upon is data.

In today’s episode, we speak with Kaveh Vessali, Digital, Data & AI Leader, PwC Middle East, about the intersection between data and public policy and the many exciting insights he’s gained from his role delivering smart cities and data transformation projects within the public sector in the middle east. 

Join us as we discuss:

• The important role data plays in shaping public policy 
• What goes into designing a smart city
• The change management skills vital for successful digital transformation 
• Data ethics and the importance of transparency  

Find every episode of DataFramed on Apple, Spotify, and more. Find us on our website and join the conversation on LinkedIn. 

Listening on a desktop and can’t see the links? Just search for DataFramed in your favorite podcast player. 

In almost every industry, the rate of innovation is increasing, and this is great for consumers around the globe. However, with constant innovation and continual disruption of the status quo, where to innovate next becomes much harder to identify. If your industry hasn’t been disrupted yet, it’s next on the list. So, in order to deal with uncertainty, a new culture is needed, and there’s a clear group of companies that constantly deal with uncertainty and innovation—VC’s. 

Ilya A. Strebulaev is the David S. Lobel Professor of Private Equity and Professor of Finance at the Stanford Graduate School of Business, and a Research Associate at the National Bureau of Economic Research. He is an expert in corporate finance, venture capital, innovation financing, and financial decision-making. He is the founder and director of the Stanford GSB Venture Capital Initiative.

In the episode, Richie and Ilya explore the venture mindset, the importance of embracing unknowns, how VC’s deal with unpredictability, how our education affects our decision-making ability, practical examples from Ilya’s teaching experiences at Stanford, adapting to market changes and continual innovation, venture mindset principles and much more. 

Links Mentioned in the Show:

• Ilya’s Website
• Sequoia Capital
• Stanford University 
• Related Episode: Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision Scientist
• Sign up to RADAR: AI Edition

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In programming, collaboration and experimentation can be very stressful, since sharing code and making it visible to others can be tedious, time-consuming, and nerve-wracking.Tools like Power BI are changing that entirely, by opening up new ways to collaborate between team members, add layers of customized and complex security to the data teams are working with, and making data much more accessible across organizations.

Ginger Grant joins the show to talk about how organizations can utilize Power BI, Dax, and M to their fullest potential and create new opportunities for experimentation, innovation, and collaboration.

Ginger is the Principal Consultant at the Desert Isle Group, working as an expert in advanced analytic solutions, including machine learning, data warehousing, ETL, reporting and cube development, Power BI, Excel Automation, Data Visualization and training. In addition to her consultant work, she is also a blogger at and global keynote speaker on developments and trends in data. Microsoft has also recognized her technical contributions by awarding her a MVP in Data Platform.

In this episode, we talk about what Power BI is, the common mistakes organizations make when implementing Power BI, advanced use cases, and much more.

Generative AI's transformative power underscores the critical need for high-quality data. In this session, Barr Moses, CEO of Monte Carlo Data, Prukalpa Sankar, Cofounder at Atlan, and George Fraser, CEO at Fivetran, discuss the nuances of scaling data quality for generative AI applications, highlighting the unique challenges and considerations that come into play. Throughout the session, they share best practices for data and AI leaders to navigate these challenges, ensuring that governance remains a focal point even amid the AI hype cycle.

Links Mentioned in the Show:

• Rewatch Session from RADAR: AI Edition

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From data science to software engineering, Large Language Models (LLMs) have emerged as pivotal tools in shaping the future of programming. In this session, Michele Catasta, VP of AI at Replit, Jordan Tigani, CEO at Motherduck, and Ryan J. Salva, VP of Product at GitHub, will explore practical applications of LLMs in coding workflows, how to best approach integrating AI into the workflows of data teams, what the future holds for AI-assisted coding, and a lot more.

Links Mentioned in the Show:

• Rewatch Session from RADAR: AI Edition

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We have had many guests on the show to discuss how different industries leverage data science to transform the way they do business, but arguably one of the most important applications of data science is in space research and technology.

Justin Fletcher joins the show to talk about how the US Space Force is using deep learning with telescope data to monitor satellites, potentially lethal space debris, and identify and prevent catastrophic collisions. Justin is responsible for artificial intelligence and autonomy technology development within the Space Domain Awareness Delta of the United States Space Force Space Systems Command. With over a decade of experience spanning space domain awareness, high performance computing, and air combat effectiveness, Justin is a recognized leader in defense applications of artificial intelligence and autonomy.

In this episode, we talk about how the US Space Force utilizes deep learning, how the US Space Force publishes its research and data to find high-quality peer review, the must-have skills aspiring practitioners need in order to pursue a career in Defense, and much more.

You've just invested in licenses for your favorite analytics tool, but now what? In this session, Laura Gent Felker, GTM Analytics Lead at MongoDB, Tiffany Perkins-Munn, Managing Director & Head of Data & Analytics at JPMC and Omar Khawaja, CDAO & Global Head Data & Analytics at Givaudan will explore best practices when it comes to scaling analytics adoption within the wider organization. They will discuss how to approach change management when it comes to driving analytics adoption, the role of data leaders in driving a culture change around analytics tooling, and a lot more. 

One of the prerequisites for being able to do great data analyses is that the data is well structured and clean and high quality. For individual projects, this is often annoying to get right. On a corporate level, it’s often a huge blocker to productivity. And then there’s healthcare data. When you consider all the healthcare records across the USA, or any other country for that matter, there are so many data formats created by so many different organizations, it’s frankly a horrendous mess. This is a big problem because there’s a treasure trove of data that researchers and analysts can’t make use of to answer questions about which medical interventions work or not. Bad data is holding back progress on improving everyone’s health.

Terry Myerson is the CEO and Co-Founder of Truveta. Truveta enables scientifically rigorous research on more than 18% of the clinical care in the U.S. from a growing collective of more than 30 health systems. Previously, Terry enjoyed a 21-year career at Microsoft. As Executive Vice President, he led the development of Windows, Surface, Xbox, and the early days of Office 365, while serving on the Senior Leadership Team of the company. Prior to Microsoft, he co-founded Intersé, one of the earliest Internet companies, which Microsoft acquired in 1997.​

In the episode, Richie and Terry explore the current state of health records, challenges when working with health records, data challenges including privacy and accessibility, data silos and fragmentation, AI and NLP for fragmented data, regulatory grade AI, ongoing data integration efforts in healthcare, the future of healthcare and much more. 

Links Mentioned in the Show:

• Truveta
• Connect with Terry
• HIPAA
• Course - Introduction to Data Privacy
• Related Episode: Using AI to Improve Data Quality in Healthcare
• Rewatch sessions from RADAR: AI Edition

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When working with data, it’s easy for us to think about it as a mechanistic process, where data comes in and products come out. But as we’ve explored throughout the show, succeeding in data, whether you’re a data leader looking to build a data culture, a data scientist ascending the ranks, or even a policy maker looking to have an impact with data, the human side is crucial.

At the heart of the “human side” is empathy— whether it’s for your stakeholders if you’re a data scientist developing a dashboard for them, empathy for your workforce if you’re a data or learning leader, or empathy for the planet and your citizens if you’re a policy maker. 

So how can we all practice better empathy? Specifically, can we all practice better data empathy? Luckily, empathy is a muscle that can be built. It’s not a “you have it, or you don’t” type of skill. So how can individuals and organizations utilize data empathy to improve how they work with data and the success rate of their projects?  

Enter Phil Harvey, an Industrial Metaverse Architect in the Industrial Metaverse Core group at Microsoft. He is an expert in Data & AI Technical and Business Strategy & Philosophy. Harvey is also co-author of the book Data: A Guide to Humans, which explores the concept of Data Empathy, and how it can power better use of data through better communication and understanding of stakeholders in the value chain of data. 

The rise of AI agents in the workplace is transforming how businesses operate, tackling repetitive tasks and freeing up human employees for more creative endeavors. But what does this mean for the future of work, and how can professionals leverage these tools effectively? As AI agents become more sophisticated, capable of reasoning and decision-making, how do you ensure they align with your business goals? What are the implications for data privacy and security, and how do you manage the transition to a more automated workforce while maintaining human oversight?

Surojit Chatterjee is the founder and CEO of Ema. Previously, he guided Coinbase through a successful 2021 IPO as its Chief Product Officer and scaled Google Mobile Ads and Google Shopping into multi-billion dollar businesses as the VP and Head of Product. Surojit holds 40 US patents and has an MBA from MIT, MS in Computer Science from SUNY at Buffalo, and B. Tech from IIT Kharagpur.

In the episode, Richie and Surojit explore the transformative role of AI agents in automating repetitive business tasks, enhancing creativity and innovation, improving customer support, and redefining workplace efficiency. They discuss the potential of AI employees, data privacy concerns, and the future of AI-driven business processes, and much more.

Links Mentioned in the Show:

• Ema
• Connect with Surojit
• Skill Track: Artificial Intelligence (AI) Leadership
• Related Episode: How Generative AI is Changing Leadership with Christie Smith, Founder of the Humanity Institute and Kelly Monahan, Managing Director, Research Institute
• Attend RADAR Skills Edition

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As AI continues to dominate industry conversations, the notion of AI readiness becomes a focal point for organizations. It's a multifaceted challenge that goes beyond technology, encompassing business processes and cultural shifts. For professionals, this means grappling with questions like: How do you choose the right AI projects that align with business goals? What skills and team structures are necessary to support AI initiatives? And how do you manage the change that comes with integrating AI into your operations?

Venky Veeraraghavan is the Chief Product Officer at DataRobot. As CPO, Venky drives the definition and delivery of the DataRobot Enterprise AI Suite. Venky has twenty-five years of experience focusing on big data and AI as a product leader and technical consultant at top technology companies (Microsoft) and early-stage startups (Trilogy).

In the episode, Richie and Venky Veeraraghavan explore AI readiness in organizations, the importance of aligning AI with business processes, the roles and skills needed for AI integration, the balance between building and buying AI solutions, the challenges of implementing AI-driven changes, and much more.

Links Mentioned in the Show:

• Datarobot
• Connect with Venky
• Skill Track: Artificial Intelligence (AI) Leadership
• Related Episode: Aligning AI with Enterprise Strategy with Leon Gordon, CEO at Onyx Data
• Attend RADAR Skills Edition

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As we look back at 2024, we're highlighting some of our favourite episodes of the year, and with 100 of them to choose from, it wasn't easy!

The four guests we'll be recapping with are:

• Lea Pica - A celebrity in the data storytelling and visualisation space. Richie and Lea cover the full picture of data presentation, how to understand your audience, how to leverage hollywood storytelling and more. Out December 19.
• Alex Banks - Founder of Sunday Signal. Adel and Alex cover Alex’s journey into AI and what led him to create Sunday Signal, the potential of AI, prompt engineering at its most basic level, chain of thought prompting, the future of LLMs and more. Out December 23.
• Don Chamberlin - The renowned co-inventor of SQL. Richie and Don explore the early development of SQL, how it became standardized, the future of SQL through NoSQL and SQL++ and more. Out December 26.
• Tom Tunguz - general Partner at Theory Ventures, a $235m VC firm. Richie and Tom explore trends in generative AI, cloud+local hybrid workflows, data security, the future of business intelligence and data analytics, AI in the corporate sector and more. Out December 30.

Since the launch of ChatGPT, one of the trending terms outside of ChatGPT itself has been prompt engineering. This act of carefully crafting your instructions is treated as alchemy by some and science by others. So what makes an effective prompt?

Alex Banks has been building and scaling AI products since 2021. He writes Sunday Signal, a newsletter offering a blend of AI advancements and broader thought-provoking insights. His expertise extends to social media platforms on X/Twitter and LinkedIn, where he educates a diverse audience on leveraging AI to enhance productivity and transform daily life.

In the episode, Alex and Adel cover Alex’s journey into AI and what led him to create Sunday Signal, the potential of AI, prompt engineering at its most basic level, strategies for better prompting, chain of thought prompting, prompt engineering as a skill and career path, building your own AI tools rather than using consumer AI products, AI literacy, the future of LLMs and much more. 

Links Mentioned in the Show:

• [Alex’s Free Course on DataCamp] Understanding Prompt Engineering
• Sunday Signal
• Principles by Ray Dalio: Life and Work
• Related Episode: [DataFramed AI Series #1] ChatGPT and the OpenAI Developer Ecosystem
• Rewatch sessions from RADAR: The Analytics Edition

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Perhaps the biggest complaint about generative AI is hallucination. If the text you want to generate involves facts, for example, a chatbot that answers questions, then hallucination is a problem. The solution to this is to make use of a technique called retrieval augmented generation, where you store facts in a vector database and retrieve the most appropriate ones to send to the large language model to help it give accurate responses. So, what goes into building vector databases and how do they improve LLM performance so much?

Ram Sriharsha is currently the CTO at Pinecone. Before this role, he was the Director of Engineering at Pinecone and previously served as Vice President of Engineering at Splunk. He also worked as a Product Manager at Databricks. With a long history in the software development industry, Ram has held positions as an architect, lead product developer, and senior software engineer at various companies. Ram is also a long time contributor to Apache Spark. 

In the episode, Richie and Ram explore common use-cases for vector databases, RAG in chatbots, steps to create a chatbot, static vs dynamic data, testing chatbot success, handling dynamic data, choosing language models, knowledge graphs, implementing vector databases, innovations in vector data bases, the future of LLMs and much more. 

Links Mentioned in the Show:

• Pinecone
• Webinar - Charting the Path: What the Future Holds for Generative AI
• Course - Vector Databases for Embeddings with Pinecone
• Related Episode: The Power of Vector Databases and Semantic Search with Elan Dekel, VP of Product at Pinecone
• Rewatch sessions from RADAR: AI Edition

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There’s been a lot of pressure to add AI to almost every digital tool and service recently, and two years into the AI hype cycle, we’re seeing two types of problems. The first is organizations that haven’t done much yet with AI because they don’t know where to start. The second is organizations that rushed into AI and failed because they didn’t know what they were doing. Both are symptoms of the same problem: not having an AI strategy and not understanding how to tactically implement AI. There’s a lot to consider around choosing the right project and putting processes and skilled talent in place, not to mention worrying about costs and return on investment.

Tathagat Varma is the Global TechOps Leader at Walmart Global Tech. Tathagat is responsible for leading strategic business initiatives, enterprise agile transformation, technical learning and enablement, strategic technical initiatives, startup ecosystem engagement, and internal events across Walmart Global Tech. He also provides support to horizontal technical and internal innovation programs in the company. Starting as a Computer Scientist with DRDO, and with an overall experience of 27 years, Tathagat has played significant technical and leadership roles in establishing and growing organizations like NerdWallet, ChinaSoft International, McAfee, Huawei, Network General, NetScout System, [24]7 Innovations Labs and Yahoo!, and played key engineering roles at Siemens and Philips.

In the episode, Richie and Tathagat explore failures in AI adoption, the role of leadership in AI adoption, AI strategy and business objective alignment, investment and timeline for AI projects, identifying starter AI projects, skills for AI success, building a culture of AI adoption, the potential of AI and much more. 

Links Mentioned in the Show:

• Walmart Global Tech
• Connect with Tathagat
• [Course] Data Governance Concepts
• Related Episode: How Walmart Leverages Data & AI with Swati Kirti, Sr Director of Data Science at Walmart
• Rewatch sessions from RADAR: AI Edition

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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.

Generative AI has had a wide range of uses, but some of its strongest use cases are in coding and programming. One of the companies that has been leading the way in AI-assisted programming has been GitHub with GitHub CoPilot. Many software engineering teams now have tools like CoPilot embedded into their workflows, but what does this mean for the future of programming?

Kyle Daigle is the COO of GitHub, leading the strategic initiatives, operations, and innovation of the world's largest platform for software development and collaboration. With over 10 years of experience at GitHub, Kyle has a deep understanding of the needs and challenges of developers and the ecosystem they work in.

In the episode, Adel and Kyle explore Kyle’s journey into development and AI, how he became the COO at GitHub, GitHub’s approach to AI, the impact of CoPilot on software development, how AI tools are adopted by software developers, the future of programming and AI’s role within it, the risks and challenges associated with the adoption of AI coding tools, the broader implications tools like CoPilot might have and much more. 

Links Mentioned in the Show:

• GitHub CoPilot
• Kyle on GitHub
• [Code Along] Pair Programming with GitHub Copilot
• [Course] GitHub Concepts
• Related Episode: What to Expect from AI in 2024 with Craig S. Smith, Host of the Eye on A.I Podcast
• Rewatch sessions from RADAR: The Analytics Edition

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We’ve heard so much about the value and capabilities of generative AI over the past year, and we’ve all become accustomed to the chat interfaces of our preferred models. One of the main concerns many of us have had has been privacy. Is OpenAI keeping the data and information I give to ChatGPT secure? One of the touted solutions to this problem is running LLMs locally on your own machine, but with the hardware cost that comes with it, running LLMs locally has not been possible for many of us. That might now be starting to change.

Nuri Canyaka is VP of AI Marketing at Intel. Prior to Intel, Nuri spent 16 years at Microsoft, starting out as a Technical Evangelist, and leaving the organization as the Senior Director of Product Marketing. He ran the GTM team that helped generate adoption of GPT in Microsoft Azure products.

La Tiffaney Santucci is Intel’s AI Marketing Director, specializing in their Edge and Client products. La Tiffaney has spent over a decade at Intel, focussing on partnerships with Dell, Google Amazon and Microsoft. 

In the episode, Richie, Nuri and La Tiffaney explore AI’s impact on marketing analytics, the adoptions of AI in the enterprise, how AI is being integrated into existing products, the workflow for implementing AI into business processes and the challenges that come with it, the importance of edge AI for instant decision-making in uses-cases like self-driving cars, the emergence of AI engineering as a distinct field of work, the democratization of AI, what the state of AGI might look like in the near future and much more. 

About the AI and the Modern Data Stack DataFramed Series

This week we’re releasing 4 episodes focused on how AI is changing the modern data stack and the analytics profession at large. The modern data stack is often an ambiguous and all-encompassing term, so we intentionally wanted to cover the impact of AI on the modern data stack from different angles. Here’s what you can expect:

• Why the Future of AI in Data will be Weird with Benn Stancil, CTO at Mode & Field CTO at ThoughtSpot — Covering how AI will change analytics workflows and tools 
• How Databricks is Transforming Data Warehousing and AI with Ari Kaplan, Head Evangelist & Robin Sutara, Field CTO at Databricks — Covering Databricks, data intelligence and how AI tools are changing data democratization
• Adding AI to the Data Warehouse with Sridhar Ramaswamy, CEO at Snowflake — Covering Snowflake and its uses, how generative AI is changing the attitudes of leaders towards data, and how to improve your data management
• Accelerating AI Workflows with Nuri Cankaya, VP of AI Marketing & La Tiffaney Santucci, AI Marketing Director at Intel — Covering AI’s impact on marketing analytics, how AI is being integrated into existing products, and the democratization of AI

Links Mentioned in the Show:

• Intel OpenVINO™ toolkit
• Intel Developer Clouds for Accelerated Computing
• AWS Re:Invent
• [Course] Implementing AI Solutions in Business
• Related Episode: Intel CTO Steve Orrin on How Governments Can Navigate the Data & AI Revolution
• Sign up to

With the increasing rate at which new data tools and platforms are being created, the modern data stack risks becoming just another buzzword data leaders use when talking about how they solve problems.

Alongside the arrival of new data tools is the need for leaders to see beyond just the modern data stack and think deeply about how their data work can align with business outcomes, otherwise, they risk falling behind trying to create value from innovative, but irrelevant technology.

In this episode, Yali Sassoon joins the show to explore what the modern data stack really means, how to rethink the modern data stack in terms of value creation, data collection versus data creation, and the right way businesses should approach data ingestion, and much more.

Yali is the Co-Founder and Chief Strategy Officer at Snowplow Analytics, a behavioral data platform that empowers data teams to solve complex data challenges. Yali is an expert in data with a background in both strategy and operations consulting teaching companies how to use data properly to evolve their operations and improve their results.

In data science, the push for unbiased machine learning models is evident. So much effort is made into ensuring the products we create are done thoughtfully and correctly, but are we investing the same effort in ensuring our teams, the very architects of these models, are diverse and inclusive? Bias in data can lead to skewed results, and similarly, a lack of diversity in teams can result in narrow perspectives. As we prioritize building diversity and inclusion into our data, it's equally crucial to embed these principles within our teams. So, who is best equipped to guide us in integrating DEI from a data perspective?

Tracy Daniels is the Chief Data Officer for Truist Financial Corporation. She leads the team responsible for Truist’s enterprise data capabilities, including strategy, governance, data platform delivery, client, master & reference data, and the centers of excellence for business intelligence visualization and artificial intelligence & machine learning. She is also

the executive sponsor for Truist’s Enterprise Technology & Operations Diversity Council. Daniels joined Truist in 2018. She has more than 25 years of banking and technology experience leading high performing technology portfolio, development, infrastructure and global operations organizations. Tracy enjoys participating in civic and philanthropic endeavors including serving on the Georgia State University Foundation Board of Trustees. She has been recognized as a National 2013 WOC STEM Rising Star award recipient, the 2017 Working Mother magazine Mother of the Year recipient, and a 2021 Women In Technology (WIT) Women of the Year in STEAM finalist.

In the episode Tracy and Richie discuss Truist's approach to Diversity, Equity, and Inclusion (DEI) and its alignment with the company's purpose and values, the distinction between diversity and inclusion, the positive outcomes of implementing DEI correctly, the importance of not missing opportunities both externally with customers and internally with talent, the significance of aligning diversity programs with business metrics and hiring to promote DEI, considerations for job advertisements that appeal to a diverse audience, and much more. 

Links mentioned in the show:

• McKinsey on Diversity and Inclusion
• Brookings Piece on Mitigating Bias in Data
• Algorithmic Justice League
• European Legislation on Data and Diversity
• Course: AI Ethics
• Radar: Data & AI Literacy Edition

Many times, data scientists can fall into the trap of resume-driven development. As in, learning the shiniest, most advanced technique available to them in an attempt to solve a business problem. However, this is not what a learning mindset should look like for data teams.

As it turns out, taking a step back and focusing on the fundamentals and step-by-step iteration can be the key to growing as a data scientist, because when data teams develop a strong understanding of the problems and solutions lying underneath the surface, they will be able to wield their tools with complete mastery.

Ella Hilal joins the show to share why operating from an always-learning mindset will open up the path to a true mastery and innovation for data teams. Ella is the VP of Data Science and Engineering for Commercial and Service Lines at Shopify, a global commerce leader that helps businesses of all size grow, market, and manage their retail operations. Recognized as a leading woman in Data science, Internet of things and Machine Learning, Ella has over 15 years of experience spanning multiple countries, and is an advocate for responsible innovation, women in tech, and STEM.

In this episode, we talk about the biggest mistakes data scientists make when solving business problems, how to create cohesion between data teams and the broader organization, how to be an effective data leader that prioritizes their team’s growth, and how developing an always-learning mindset based on iteration, experimentation, and deep understanding of the problems needing to be solved can accelerate the growth of data teams.

As AI continually changes how businesses operate, new questions emerge around ethics and privacy. Nowadays, algorithms can set prices and personalize offers, but how do companies ensure they’re doing this responsibly? What does it mean to be transparent with customers about data use, and how can businesses avoid unintended bias? Balancing innovation with trust is key, but achieving this balance isn’t always straightforward.

Dr. Jose Mendoza is Academic Director and Clinical Associate Professor in Integrated Marketing at NYU, and was formerly an Associate Professor of Practice at The University of Arizona in Tucson, Arizona. His focus is on consumer pricing, digital retailing, intelligent retail stores, neuromarketing, big data, artificial intelligence, and machine learning. Previously, he taught marketing courses at Sacred Heart University and Western Michigan University. He is also an experienced senior global marketing executive with over 18 years of experience in global marketing alone and a career as an Engineer in Information Sciences. Dr. Mendoza is also a Doctoral Researcher in Strategic and Global pricing, Consumer Behavior, and Pricing Research methodologies. He had international roles in Latin America, Europe, and the USA with scope in over 50 countries. 

In the episode, Richie and Jose explore AI-driven pricing, consumer perceptions and ethical pricing, the complexity of dynamic pricing models, explainable AI, data privacy and customer trust, legal and ethical guardrails, innovations in dynamic pricing and much more. 

Links Mentioned in the Show:

• NYU
• Connect with Jose
• Amazon Dynamic Pricing Strategy in 2024
• Course: AI Ethics
• Related Episode: The Future of Marketing Analytics with Cory Munchbach, CEO at BlueConic
• Sign up to RADAR: Forward Edition

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Businesses are collecting more data than ever before. But is bigger always better? Many companies are starting to question whether massive datasets and complex infrastructure are truly delivering results or just adding unnecessary costs and complications. How can you make sure your data strategy is aligned with your actual needs? What if focusing on smaller, more manageable datasets could improve your efficiency and save resources, all while delivering the same insights?

Ryan Boyd is the Co-Founder & VP, Marketing + DevRel at MotherDuck. Ryan started his career as a software engineer, but since has led DevRel teams for 15+ years at Google, Databricks and Neo4j, where he developed and executed numerous marketing and DevRel programs. Prior to MotherDuck, Ryan worked at Databricks and focussed the team on building an online community during the pandemic, helping to organize the content and experience for an online Data + AI Summit, establishing a regular cadence of video and blog content, launching the Databricks Beacons ambassador program, improving the time to an “aha” moment in the online trial and launching a University Alliance program to help professors teach the latest in data science, machine learning and data engineering.

In the episode, Richie and Ryan explore data growth and computation, the data 1%, the small data movement, data storage and usage, the shift to local and hybrid computing, modern data tools, the challenges of big data, transactional vs analytical databases, SQL language enhancements, simple and ergonomic data solutions and much more. 

Links Mentioned in the Show:

• MotherDuck
• The Small Data Manifesto
• Connect with Ryan
• Small DataSF conference
• Related Episode: Effective Data Engineering with Liya Aizenberg, Director of Data Engineering at Away
• Rewatch sessions from RADAR: AI Edition

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• Empower your business with world-class data and AI skills with DataCamp for business

Doing sales better is perhaps the most direct route to making more revenue, so it should be a priority for every business. B2B sales is often very complex, with a mix of emails and video calls and prospects interacting with your website and social content. And you often have multiple people making decisions about a purchase. All this generates a massive data—or, more accurately, a mess of data—which very few sales teams manage to harness effectively. How can sales teams can make use of data, software, and AI to clean up this mess, work more effectively, and most of all, crush those quarterly targets? 

Ellie Fields is the Chief Product and Engineering Officer at Salesloft leading Product Management, Engineering, and Design. Ellie previously led development teams at Tableau responsible for product strategy and engineering for collaboration and mobile portfolio. Ellie also launched and led Tableau Public.

In the episode Richie and Ellie explore the digital transformation of sales, how sales technology helps buyers and sellers, metrics for sales success, activity vs outcome metrics, predictive forecasting, AI, customizing sales processes, revenue orchestration, how data impacts sales and management, future trends in sales, and much more. 

Links Mentioned in the Show:

• Salesloft
• Connect with Ellie
• Forrester Research
• Course - Understanding the EU AI Act
• Related Episode: Data & AI at Tesco with Venkat Raghavan, Director of Analytics and Science at Tesco
• Rewatch sessions from RADAR: AI Edition

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Mastering the technical side of data and AI is one thing, but communicating those insights effectively is a whole different challenge. How do you make sure your data is understood, acted upon, and influences decisions? It’s not just about presenting the right numbers—it’s about framing them in a way that resonates with different audiences. But how do you tailor your communication to different stakeholders and ensure your message cuts through? What strategies can you use to make your insights truly impactful?

Wes Kao is an entrepreneur, marketer, coach, and advisor who writes at newsletter.weskao.com. She is co-founder of Maven, an edtech company that raised $25M from First Round and Andreessen Horowitz. Previously, she co-founded the altMBA with bestselling author Seth Godin.

In the episode, Richie and Wes explore communication skills, tailoring to your audience, persuasion vs information, feedback and behavioral change, intellectual honesty, judgement and analytical thinking, management and ownership, dealing with mistakes, conflict management, career advice for data practitioners and much more. 

Links Mentioned in the Show:

• Wes’ Website
• Connect with Wes
• 10,000 Hours Concept by Malcolm Gladwell
• Course: Data Communication Concepts
• Related Episode: Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision Scientist
• Sign up to RADAR: Forward 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

We’re improving DataFramed, and we need your help! We want to hear what you have to say about the show, and how we can make it more enjoyable for you—find out more here.

The modern leader faces unprecedented challenges, from managing a multi-generational workforce to integrating AI into daily operations. How can leaders cultivate a human-centric approach that fosters trust and innovation? What role does vulnerability play in effective leadership, and how can it coexist with the need for bold decision-making? As professionals strive to lead with authenticity, what strategies can help leaders raise the tide for all boats?

Dana Maor is the global co-head for the McKinsey People & Organizational Performance Practice and is a member of its Knowledge Council. As a senior partner, she works with leaders globally to transform their organizations and themselves and serves as co-dean of multiple McKinsey leadership programs.

In the episode, Adel and Dana explore the complexities of modern leadership, the importance of human-centric leadership, balancing empathy with performance, navigating imposter syndrome, and the evolving role of leaders in the age of AI.

Links Mentioned in the Show:

• The Journey to Leadership by Dana Maor
• McKinsey & Company - Organizational Health Index
• Skill Track: Artificial Intelligence (AI) Leadership
• Related Episode: How Data can Enable Effective Leadership with Dr. Constance Dierickx, The Decision Doctor
• Rewatch sessions from RADAR: Forward 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

Databases are ubiquitous, and you don’t need to be a data practitioner to know that all data everywhere is stored in a database—or is it? While the majority of data around the world lives in a database, the data that helps run the heart of our operating systems—the core functions of our computers— is not stored in the same place as everywhere else. This is due to database storage sitting ‘above’ the operating system, requiring the OS to run before the databases can be used. But what if the OS was built ‘on top’ of a database? What difference could this fundamental change make to how we use computers?

Mike Stonebraker is a distinguished computer scientist known for his foundational work in database systems, he is also currently CTO & Co-Founder At DBOS. His extensive career includes significant contributions through academic prototypes and commercial startups, leading to the creation of several pivotal relational database companies such as Ingres Corporation, Illustra, Paradigm4, StreamBase Systems, Tamr, Vertica, and VoltDB. Stonebraker's role as chief technical officer at Informix and his influential research earned him the prestigious 2014 Turing Award.

Stonebraker's professional journey spans two major phases: initially at the University of California, Berkeley, focusing on relational database management systems like Ingres and Postgres, and later, from 2001 at the Massachusetts Institute of Technology (MIT), where he pioneered advanced data management techniques including C-Store, H-Store, SciDB, and DBOS. He remains a professor emeritus at UC Berkeley and continues to influence as an adjunct professor at MIT’s Computer Science and Artificial Intelligence Laboratory. Stonebraker is also recognized for his editorial work on the book "Readings in Database Systems."

In the episode, Richie and Mike explore the the success of PostgreSQL, the evolution of SQL databases, the shift towards cloud computing and what that means in practice when migrating to the cloud, the impact of disaggregated storage, software and serverless trends, the role of databases in facilitating new data and AI trends, DBOS and it’s advantages for security, and much more. 

Links Mentioned in the Show:

• DBOS
• Paper: What Goes Around Comes Around
• [Course] Understanding Cloud Computing
• Related Episode: Scaling Enterprise Analytics with Libby Duane Adams, Chief Advocacy Officer and Co-Founder of Alteryx
• Rewatch sessions from RADAR: The Analytics Edition

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• Empower your business with world-class data and AI skills with DataCamp for business

Data Literacy is increasingly becoming a skill that every role needs to have, regardless of whether their role a data-oriented or not. No one knows this better than Jordan Morrow, who is known as the Godfather of Data Literacy.

Jordan is the VP and Head of Data Analytics at Brainstorm, Inc., and is the author of Be Data Literate: The Skills Everyone Needs to Succeed.Jordan has been a fierce advocate for data literacy throughout his career, including helping the United Nations understand and utilize data literacy effectively.

Throughout the episode, we define data literacy, why organizations need data literacy in order to use data properly and drive business impact, how to increase organizational data literacy, and more.

This episode of DataFramed is a part of DataCamp’s Data Literacy Month, where we raise awareness for Data Literacy throughout the month of September through webinars, workshops, and resources featuring thought leaders and subject matter experts that can help you build your data literacy, as well as your organization’s. For more information, visit: https://www.datacamp.com/data-literacy-month/for-teams

Driving trust with data is essential to succeeding with analytics. However, time and time again, data quality remains an issue for most organizations today. In this session, Esther Munyi, Chief Data Officer at Sasfin, Amy Grace, Director, Military Engines Digital Strategy at Pratt & Whitney, Stefaan Verhulst, Chief Research & Development Officer, Director of Data Program at NYU Governance Lab, and Malarvizhi Veerappan, Program Manager and Senior Data Scientist at the World Bank will focus on strategies for improving data quality, fostering a culture of trust around data, and balancing robust governance with the need for accessible, high-quality data.

Data quality can make or break any data initiative or product. If you aren’t able to collect data that is accurate, or you have data sets that have varying structures, or are filled with typos and other issues caused by human error, then the chances drop drastically that your data models will be accurate, or even helpful.

When it comes to healthcare, data quality can be an absolute nightmare. With so many different data sources, high data churn rates, and a lack of standardization in many different healthcare categories, it can seem impossible to make quality healthcare more easily accessible to people when they need it.

Ribbon Health seeks to change that by using AI to improve the quality of healthcare data and create a data platform with actionable provider information including insurance coverage, prices, and performance.

Today’s guests are Nate Fox, the CTO, Co-Founder, and President of Ribbon Health, and Sunna Jo, a former pediatrician who is now a data scientist at Ribbon Health.

Throughout the episode, we talk about why data quality in healthcare is messy, why having context around data is necessary to interpret and utilize it properly, how healthcare providers are improving their services because of platforms like Ribbon Health, how to tackle common data cleaning problems, and much more

Diversity in both skillset and experience are at the core of high-impact data teams, but how can you take your data team’s impact to the next level with subject matter expertise, attention to user experience, and mentorship?

Today’s guest, Dan Kellet, Chief Data Officer at Capital One UK, joins us to discuss how he scaled Capital One’s data team. Throughout the episode, we discuss:

• The hallmarks of a high-impact data team
• The importance of skills and background diversity when building great data teams
• The importance of UX skills when developing data products
• The specific challenges of leading data teams in financial services

Arianna Huffington, co-founder of The Huffington Post, woke up in a pool of blood nursing a broken cheekbone after collapsing at her desk in 2007. Various stresses and pressures in her life had manifested themself into an episode of extreme mental exhaustion. This event was the catalyst for her to write a book on well-being as well as start the behavioral-change company Thrive Global. Many of us have, or will, experience burnout at some point. The build-up of stress, negative emotions, and internal tension may not result in the same shocking scene Huffington found herself in, but its effects are serious and permeate not just through our profession but into our home life as well. Stress and burnout are especially prevalent in working environments where there is an emphasis on urgency, and with the constant advancements we’ve seen in the data & AI sphere in the past year, leaders and practitioners working in the data space will need to know how to recognize the symptoms of burnout and create workplace cultures that prevent burnout in the first place.

Jen Fisher is Deloitte’s human sustainability leader. Previously, Fisher served as Deloitte’s first-ever chief well-being officer. She’s also a TEDx speaker, coauthor of the book, Work Better Together: How to Cultivate Strong Relationships to Maximize Well-Being and Boost Bottom Lines, editor-at-large for Thrive Global, and host of the “WorkWell” podcast series.

In the episode, Jen and Adel cover Jen’s own personal experience with burnout, the role of a Chief Wellbeing Officer, the impact of work on our overall well-being, the patterns that lead to burnout, defining well-being in the workplace, technology’s impact on our well-being, psychological safety in the workplace, how managers and leaders can looking after themselves and their teams, the future of human sustainability in the workplace and much more. 

Links Mentioned in the Show:

• Work Better Together: How to Cultivate Strong Relationships to Maximize Well-Being and Boost Bottom Lines
• Jen’s TED Talk: The Future of Work
• Brené Brown: Clear Is Kind. Unclear Is Unkind.
• What Is Psychological Safety?

AI is not just about writing code; it's about improving the entire software development process. From generating documentation to automating code reviews, AI tools are becoming indispensable. But how do you ensure the quality of AI-generated code? What strategies can you employ to maintain high standards while leveraging AI's capabilities? These are the questions developers must consider as they incorporate AI into their workflows.

Eran Yahav is an associate professor at the Computer Science Department at the Technion – Israel Institute of Technology and co-founder and CTO of Tabnine (formerly Codota). Prior to that, he was a research staff member at the IBM T.J. Watson Research Center in New York (2004-2010). He received his Ph.D. from Tel Aviv University (2005) and his B.Sc. from the Technion in 1996. His research interests include program analysis, program synthesis, and program verification. Eran is a recipient of the prestigious Alon Fellowship for Outstanding Young Researchers, the Andre Deloro Career Advancement Chair in Engineering, the 2020 Robin Milner Young Researcher Award (POPL talk here), the ERC Consolidator Grant as well as multiple best paper awards at various conferences.

In the episode, Richie and Eran explore AI's role in software development, the balance between AI assistance and manual coding, the impact of generative AI on code review and documentation, the evolution of developer tools, and the future of AI-driven workflows, and much more.

Links Mentioned in the Show:

• Tabnine
• Connect with Eran
• Course: Working with the OpenAI API
• Related Episode: Getting Generative AI Into Production with Lin Qiao, CEO and Co-Founder of Fireworks AI
• Rewatch sessions from RADAR: Forward Edition

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As organizations of all sizes continuously look to drive value out of data, the modern data stack has emerged as a clear solution for getting insights into the hands of the organization. With the rapid pace of innovation not slowing down, the tools within the modern data stack have enabled data teams to drive faster insights, collaborate at scale, and democratize data knowledge. However, are tools just enough to drive business value with data? 

In the first of our four RADAR 2023 sessions, we look at the key drivers of value within the modern data stack through the minds of Yali Sassoon and Barr Moses. 

Yali Sassoon is the Co-Founder and Chief Strategy Officer at Snowplow Analytics, a behavioral data platform that empowers data teams to solve complex data challenges. At Snowplow, Yali gets to combine his love of building things with his fascination of the ways in which people use data to reason.

Barr Moses is CEO & Co-Founder of Monte Carlo. Previously, she was VP Customer Operations at customer success company Gainsight, where she helped scale the company 10x in revenue and, among other functions, built the data/analytics team. 

Listen in as Yali and Barr outline how data leaders can drive value creation with data in 2023.

From May 8-11, discover expert insights from four industry leaders from OpenAI, Accenture, Kubrick Group, and Kanayma LLC on how to navigate the era of AI.

One of the biggest challenges facing the adoption of machine learning and AI in Data Science is understanding, interpreting, and explaining models and their outcomes to produce higher certainty, accountability, and fairness.

Serg Masis is a Climate & Agronomic Data Scientist at Syngenta and the author of the book, Interpretable Machine Learning with Python. For the last two decades, Serg has been at the confluence of the internet, application development, and analytics. Serg is a true polymath. Before his current role, he co-founded a search engine startup incubated by Harvard Innovation Labs, was the proud owner of a Bubble Tea shop, and more.

Throughout the episode, Serg spoke about the different challenges affecting model interpretability in machine learning, how bias can produce harmful outcomes in machine learning systems, the different types of technical and non-technical solutions to tackling bias, the future of machine learning interpretability, and much more.

Building and managing AI products comes with its own set of unique challenges. Especially when they are under intense scrutiny like mobile and home assistants have dealt with in recent years. From dealing with the unpredictable nature of machine learning models to ensuring that your product is both ethical and user-friendly, the path to success isn’t always clear. But how do you navigate these complexities and still deliver a product that meets business goals? What key steps can you take to align AI innovation with measurable outcomes and long-term success?

Marily Nika is one of the world's leading thinkers on product management for artificial intelligence. At Google, she manages the generative AI product features for Google Assistant. Marily also founded AI Product Academy, where she runs a BootCamp on AI product management, and she teaches the subject on Maven. Previously, Marily was an AI Product Lead in Meta's Reality Labs, and the AI Product Lead for Google Glass. She is also an Executive Fellow at Harvard Business School.

In the episode, Richie and Marily explore the unique challenges of AI product management, experimentation, ethical considerations in AI product management, collaboration, skills needed to succeed in AI product development, the career path to work in AI as a Product Manager, key metrics for AI products and much more. 

Links Mentioned in the Show:

• Komo AI
• Connect with Marily
• Marily’s Course: AI Product Management Bootcamp with Certification
• Skill Track: AI Business Fundamentals
• Related Episode: Building Human-Centered AI Experiences with Haris Butt, Head of Product Design at ClickUp
• Rewatch sessions from RADAR: AI Edition

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It’s no secret that data science jobs are on the rise; but data skills across the board are rising — leading to what today’s guest calls “hybrid jobs.”

This will require a paradigm shift in how we think about jobs and skills.  

Today’s guest, Matt Sigelman, President of The Burning Glass Institute & Chairman of Emsi Burning Glass, talks about the difficulties of connecting companies with top talent, the hybridization of many positions, and how to position yourself in the ever-changing market. 

Join us as we discuss:

• The methodology of using data science on the labor market
• The demand for data skills & how they’re evolving
• Blending skills to get ahead in the job market & the rise of subskills
• How educational institutions can prepare students for hybridization 
• Advice to the audience on how to structure their approach to skill acquisition 

Find every episode of DataFramed on Apple, Spotify, and more. Find us on our website and join the conversation on LinkedIn.

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