Dataframed

It's been almost a year since ChatGPT was released, mainstreaming AI into the collective consciousness in the process. Since that moment, we've seen a really spirited debate emerge within the data & AI communities, and really public discourse at large. The focal point of this debate is whether AI is or will lead to existential risk for the human species at large.We've seen thinkers such as Elizier Yudkowski, Yuval Noah Harari, and others sound the alarm bell on how AI is as dangerous, if not more dangerous than nuclear weapons. We've also seen AI researchers and business leaders sign petitions and lobby government for strict regulation on AI. On the flip side, we've also seen luminaries within the field such as Andrew Ng and Yan Lecun, calling for, and not against, the proliferation of open-source AI. So how do we maneuver this debate, and where does the risk spectrum actually lie with AI? More importantly, how can we contextualize the risk of AI with other systemic risks humankind faces? Such as cli

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

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 alsothe executive sponsor fo

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 Mac

For the past few years, we've seen the importance of data literacy and why organizations must invest in a data-driven culture, mindset, and skillset. However, as generative AI tools like ChatGPT have risen to prominence in the past year, AI literacy has never been more important. But how do we begin to approach AI literacy? Is it an extension of data literacy, a complement, or a new paradigm altogether? How should you get started on your AI literacy ambitions? Cindi Howson is the Chief Data Strategy Officer at ThoughtSpot and host of The Data Chief podcast. Cindi is a data analytics, AI, and BI thought leader and an expert with a flair for bridging business needs with technology. As Chief Data Strategy Officer at ThoughtSpot, she advises top clients on data strategy and best practices to become data-driven, speaks internationally on top trends such as AI ethics, and influences ThoughtSpot’s product strategy.Cindi was previously a Gartner Research Vice President, the lead author for the data and analytics

With September and International Literacy Day (September 8th) upon us, we’re dedicating the entire month to cover the ins and outs of data & AI literacy. Make sure to sign up for the events we have in store, and to tune in for this month’s episodes.Data & AI Literacy MonthDataCamp Radar: Data & AI Literacy Edition

The mainstreaming of data & AI is fundamentally altering the way we work and operate. But with rising innovation, comes rising ambiguity and complexity. How can leaders effectively navigate the path ahead? How can leaders adopt data-driven decision-making and learn from their mistakes? How can leaders use data to look inward, and become what today’s guest describes as “meta-leaders”? Constance Dierickx is an internationally recognized expert in high-stakes decision-making who has advised leaders and delivered speeches in more than 20 countries. Founder and president of CD Consulting Group, her clients include Fortune 20 companies, private equity firms, and large not-for-profits around the globe. She is a contributor to Harvard Business Review, Forbes, Chief Executive, and others, and has taught strategic decision-making at Skolkovo Institute of Science and Technology in Moscow, Russia. In the episode, Richie and Constance delve into what meta-leadership is, the nuances of meta-leadership, the pi

Throughout history, small businesses have consistently played a pivotal role in the global economy, serving as its foundational backbone. As we navigate the digital age, the emergence of large corporations and rapid technological advancements present new challenges. Now, more than ever, it's imperative for small businesses to adapt, embracing a data-driven approach to remain competitive and sustainable. In this evolving landscape, we need champions dedicated to guiding these businesses, ensuring they harness the full potential of modern tools and insights to ensure a fair and varied marketplace of goods and services for all. Dr Kendra Vant, Executive General Manager of Data & AI Products at Xero, is an industry leader in building data-driven products that harness AI and machine learning to solve complex problems for the small-business economy. Working across Australia, Asia and the US, Kendra has led data and technology teams at companies such as Seek, Telstra, Deloitte and now Xero where she leads t

As companies scale and become more successful, new horizons open, but with them come unexpected challenges. The influx of revenue and expansion of operations often reveal hidden complexities that can hinder efficiency and inflate costs. In this tricky situation, data teams can find themselves entangled in a web of obstacles that slow down their ability to innovate and respond to ever-changing business needs. Enter cloud analytics—a transformative solution that promises to break down barriers and unleash potential. By migrating analytics to the cloud, organizations can navigate the growing pains of success, cutting costs, enhancing flexibility, and empowering data teams to work with agility and precision. John Knieriemen is the Regional Business Lead for North America at Exasol, the market-leading high-performance analytics database. Prior to joining Exasol, he served as Vice President and General Manager at Teradata during an 11-year tenure with the company. John is responsible for strategically scaling Exaso

Generative AI is here to stay—even in the 8 months since the public release of ChatGPT, there are an abundance of AI tools to help make us more productive at work and ease the stress of planning and execution of our daily lives among other things. Already, many of us are wondering what is to come in the next 8 months, the next year, and the next decade of AI’s evolution. In the grand scheme of things, this really is just the beginning. But what should we expect in this Cambrian explosion of technology? What are the use cases being developed behind the scenes? What do we need to be mindful of when training the next generations of AI? Can we combine multiple LLMs to get better results?Bal Heroor is CEO and Principal at Mactores and has led over 150 business transformations driven by analytics and cutting-edge technology. His team at Mactores are researching and building AI, AR/VR, and Quantum computing solutions for business to gain a competitive advantage. Bal is also the Co-Founder of Aedeon—the first hy

'Software is eating the world’ is a truism coined by Mark Andreesen, General Partner at Andreesen Horowitz. This was especially evident during the shift from analog mediums to digital at the turn of the century. Software companies have essentially usurped and replaced their non-digital predecessors. Amazon was the largest bookseller, Netflix was the largest movie "rental" service, Spotify or Apple were the largest music providers.Today, AI is starting to eat the world. However, we are still at the early start of the AI revolution, with AI set to become embedded in almost every piece of software we interact with. An AI ecosystem that touches every aspect of our lives is what today’s guest describes as ‘Ambient AI’. But what can we expect from this ramp up to Ambient AI? How will it change the way we work? What do we need to be mindful of as we develop this technology?Daniel Jeffries is the Managing Director of the AI Infrastructure Alliance and former CIO at Stability AI, the company responsible for Stable Dif

In a time when AI is evolving at breakneck speeds, taking a step back and gaining a bird's-eye view of the evolving AI ecosystem is paramount to understanding where the field is headed.With this bird's-eye view come a series of questions. Which trends will dominate generative AI in the foreseeable future? What are the truly transformative use-cases that will reshape our business landscape? What does the skills economy look like in an age of hyper intelligence?Enter Joanne Chen, General Partner at Foundation Capital. Joanne invests in early-stage AI-first B2B applications and data platforms that are the building blocks of the automated enterprise. She has shared her learnings as a featured speaker at conferences, including CES, SXSW, WebSummit, and has spoken about the impact of AI on society in her TED talk titled "Confessions of an AI Investor." Joanne began her career as an engineer at Cisco Systems and later co-founded a mobile gaming company. She also spent many years working on Wall Street at Jefferies &

Spreadsheets have been the unsung heroes of the data world for many decades now. Yet, despite their ubiquity and importance, they've seen little disruption or evolution. The grid of cells we interact with today isn't far removed from the ones our predecessors used in the 1980s.However, the winds of change have started to blow. As we stand on the cusp of a new era in data and AI, the humble spreadsheet is poised for transformation. The coming changes could redefine how we interact with data, derive insights, and how we make decisions. The implications are vast given the popularity and dependence we have on spreadsheets, and the potential impacts could ripple through every corner of the professional world. Hjalmar Gislason is the founder and CEO of GRID, with their main product being a smart spreadsheet with an interactive data visualization layer and integrated AI assistance. Hjalmar previously served as VP of Product Management at Qlik. He was the founder and CEO of DataMarket, founded in 2008 and sold t

Data and AI are advancing at an unprecedented rate—and while the jury is still out on achieving superintelligent AI systems, the idea of artificial intelligence that can understand and learn anything—an “artificial general intelligence”—is becoming more likely. What does the rise of AI mean for the future of software and work as we know it? How will AI help reinvent most of the ways we interact with the digital and physical world?Bob Muglia is a data technology investor and business executive, former CEO of Snowflake, and past president of Microsoft's Server and Tools Division. As a leader in data & AI, Bob focuses on how innovation and ethical values can merge to shape the data economy's future in the era of AI. He serves as a board director for emerging companies that seek to maximize the power of data to help solve some of the world's most challenging problems.In the episode, Richie and Bob explore the current era of AI and what it means for the future of software. Throughout the episode, they discuss

Today's government agencies face unprecedented complexities, and when thinking about the role of government in driving positive change for society at large, data & AI stand out as key levers to empower government agencies to do more with less. However, the road to government data & AI transformation is fraught with risk, and is full with opportunity. So how can government data leaders succeed in their transformation endeavors? Steve Orrin is Intel’s Federal Chief Technology Officer. He leads Public Sector Solution Architecture, Strategy, and Technology Engagements and has held technology leadership positions at Intel where he has led cybersecurity programs, products, and strategy. Steve was previously CSO for Sarvega, CTO of Sanctum, CTO and co-founder of LockStar, and CTO at SynData Technologies. He was named one of InfoWorld's Top 25 CTO's, received Executive Mosaic’s Top CTO Executives Award, is a Washington Exec Top Chief Technology Officers to Watch in 2023, was the Vice-Chair of the NSITC/I

Every year we become increasingly aware of the urgency of the climate crisis, and with that, the need to usher in renewable energies and scale their adoption has never been more important. However, as we look at the ways to scale the adoption of renewable energy, data stands out as a key lever to accelerate a greener future. Today’s guest is Jean-Pierre Pélicier, CDO at ENGIE. ENGIE is one of the largest energy producers in the world and definitely one of the largest in Europe. They operate in more than 48 countries and have committed to becoming carbon neutral by 2045. Data plays a crucial part in these plans.In the episode, Jean-Pierre shares his unique perspective on how data is not just transforming the renewable energy industry but also redefining the way we approach the climate crisis. From harnessing the power of data to optimize energy production and distribution to leveraging advanced analytics to predict and mitigate environmental impacts, Jean-Pierre highlights the ways data continues to be an

About 10 years ago, Thomas Davenport & DJ Patil published the article "Data Scientist: The Sexiest Job of the 21st Century" in the Harvard Business Review. In this piece, they described the bourgeoning role of the data scientist and what it will mean for organizations and individuals in the coming decade. As time has passed, data science has become increasingly institutionalized. Once seen as a luxury, it is now deemed a necessity in every modern boardroom. Moreover as technologies like AI and systems like ChatGPT keep astonishing us with their capabilities in handling data science tasks, it raises a pertinent question: Is Data Science Still the Sexiest Job of the 21st Century?In this episode, we invited Thomas Davenport on the show to share his perspective on where data science & AI are at today, and where they are headed. Thomas Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, the co-founder of the International Institute for Analytics,

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 B

In the realm of Applied Intelligence, Accenture leads the way in harnessing the power of data and AI to transform industries. From consumer products to life sciences, retail, and aerospace, Accenture's influence is far-reaching. But what drives the organization? How does it navigate the complex landscape of data modernization and transformation? And more importantly, how does it leverage technology not just as an enabler, but as a catalyst for innovation? Tracy Ring leads Accenture’s Applied Intelligence Products Category Group, in this role she has leadership across Consumer and Industrial Products, Automotive, Life Sciences, Retail and Aerospace and Defense. As the CDO and Global Generative AI lead for Life Sciences, she personally anchors the NA Applied Intelligence Life Sciences practice of more than 500 practitioners. Tracy has created solutions for Generative AI, Data led transformation, Artificial Intelligence, Data and Cloud Modernization, Analytics, and the organization and operating model strat

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 me