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: QlikQlik StaigeQlik Sense[Skill Track] AI FundamentalsRelated Episode: Adapting to the AI Era with Jason Feifer, Editor in Chief of Entrepreneur MagazineSign up to RADAR: The Analytics Edition

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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: AlteryxAlteryx SparkED Program[Course] Introduction to AlteryxRelated Episode: From Data Literacy to AI Literacy with Cindi Howson, Chief Data Strategy Officer at ThoughtSpotSign up to RADAR: The Analytics Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Generative AI is fantastic but has a major problem: sometimes it "hallucinates", meaning it makes things up. In a business product like a chatbot, this can be disastrous. Vector databases like Pinecone are one of the solutions to mitigating the problem. Vector databases are a key component to any AI application, as well as things like enterprise search and document search. They have become an essential tool for every business, and with the rise in interest in AI in the last couple of years, the space is moving quickly. In this episode, you'll find out how to make use of vector databases, and find out about the latest developments at Pinecone. Elan Dekel is the VP of Product at Pinecone, where he oversees the development of the Pinecone vector database. He was previously Product Lead for Core Data Serving at Google, where he led teams working on the indexing systems to serve data for Google search, YouTube search, and Google Maps. Before that, he was Founder and CEO of Medico, which was acquired by Everyday Health. In the episode, RIchie and Elan explore LLMs, hallucination in generative models, vector databases and the best use-cases for them, semantic search, business applications of vector databases and semantic search, the tech stack for AI applications, cost considerations when investing in AI projects, emerging roles within the AI space, the future of vector databases and AI, and much more.   Links Mentioned in the Show: Pinecone CanopyPinecone ServerlessLlamaIndexLangchain[Code Along] Semantic Search with PineconeRelated Episode: Expanding the Scope of Generative AI in the Enterprise with Bal Heroor, CEO and Principal at MactoresSign up to RADAR: The Analytics Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

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 HumanityAI for Sustainable Development GoalsThe Power of Crisis: How Three Threats – and Our Response – Will Change the World by Ian Bremmera href="https://www.weforum.org/agenda/2024/01/davos-2024-sam-altman-on-the-future-of-ai/" rel="noopener noreferrer"

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 KawasakiGuy Kawasaki’s Remarkable PeopleConnect with Guy on LinkedinCanvaThe Four Agreements: A Practical Guide to Personal Freedom by Don Miguel RuizHow to Change: The Science of Getting from Where You Are to Where You Want to Be by Katy MilkmanRelated Episode: Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision ScientistSign up to RADAR: The Analytics Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

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 democratizationAdding 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 managementAccelerating 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™ toolkitIntel Developer Clouds for Accelerated ComputingAWS Re:Invent[Course] Implementing AI Solutions in BusinessRelated Episode: Intel CTO Steve Orrin on How Governments Can Navigate the Data & AI RevolutionSign up to a href="https://www.datacamp.com/radar-analytics-edition"...

Snowflake has been foundational in the data space for years. In the mid-2010s, the platform was a major driver of moving data to the cloud. More recently, it's become apparent that combining data and AI in the cloud is key to accelerating innovation. Snowflake has been rapidly adding AI features to provide value to the modern data stack, but what’s really been going on under the hood? At the time of recording, Sridhar Ramaswamy was the SVP of AI at Snowflake, being appointed CEO at Snowflake in February 2024. Sridhar was formerly Co-Founder of Neeva, acquired in 2023 by Snowflake. Before founding Neeva, Ramaswamy oversaw Google's advertising products, including search, display, video advertising, analytics, shopping, payments, and travel. He joined Google in 2003 and was part of the growth of AdWords and Google's overall advertising business. He spent more than 15 years at Google, where he started as a software engineer and rose to SVP of Ads & Commerce.  In the episode, Richie and Sridhar explore Snowflake and its uses, how generative AI is changing the attitudes of leaders towards data, how NLP and AI have impacted enterprise business operations as well as new applications of AI in an enterprise environment, the challenges of enterprise search, the importance of data quality, management and the role of semantic layers in the effective use of AI, a look into Snowflakes products including Snowpilot and Cortex, the collaboration required for successful data and AI projects, advice for organizations looking to improve their data management 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 democratizationAdding 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 managementAccelerating 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: SnowflakeSnowflake acquires Neeva to accelerate search in the Data Cloud through generative AIUse AI in Seconds with Snowflake Cortex[Course] Introduction to SnowflakeRelated Episode: Why AI will Change Everything—with Former Snowflake CEO, Bob MugliaSign up to a...

Databricks started out as a platform for using Spark, a big data analytics engine, but it's grown a lot since then. Databricks now allows users to leverage their data and AI projects in the same place, ensuring ease of use and consistency across operations. The Databricks platform is converging on the idea of data intelligence, but what does this mean, how will it help data teams and organizations, and where does AI fit in the picture? Ari is Databricks’ Head of Evangelism and "The Real Moneyball Guy" - the popular movie was partly based on his analytical innovations in Major League Baseball. He is a leading influencer in analytics, artificial intelligence, data science, and high-growth business innovation. Ari was previously the Global AI Evangelist at DataRobot, Nielsen’s regional VP of Analytics, Caltech Alumni of the Decade, President Emeritus of the worldwide Independent Oracle Users Group, on Intel’s AI Board of Advisors, Sports Illustrated Top Ten GM Candidate, an IBM Watson Celebrity Data Scientist, and on the Crain’s Chicago 40 Under 40. He's also written 5 books on analytics, databases, and baseball. Robin is the Field CTO at Databricks. She has consulted with hundreds of organizations on data strategy, data culture, and building diverse data teams. Robin has had an eclectic career path in technical and business functions with more than two decades in tech companies, including Microsoft and Databricks. She also has achieved multiple academic accomplishments from her juris doctorate to a masters in law to engineering leadership. From her first technical role as an entry-level consumer support engineer to her current role in the C-Suite, Robin supports creating an inclusive workplace and is the current co-chair of Women in Data Safety Committee. She was also recognized in 2023 as a Top 20 Women in Data and Tech, as well as DataIQ 100 Most Influential People in Data. In the episode, Richie, Ari, and Robin explore Databricks, the application of generative AI in improving services operations and providing data insights, data intelligence, and lakehouse technology, the wide-ranging applications of generative AI, how AI tools are changing data democratization, the challenges of data governance and management and how tools like Databricks can help, how jobs in data and AI are changing 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 democratizationAdding 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 managementAccelerating 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: DatabricksDelta Lakea href="https://mlflow.org/" rel="noopener...

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 democratizationAdding 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 managementAccelerating 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 AnalyticsThoughtSpot acquires Mode: Empowering data teams to bring Generative AI to BIEverybody 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 FundamentalsRelated Episode: The Future of Marketing Analytics with Cory Munchbach, CEO at...

Just as many of us have been using generative AI tools to make us more productive at work, so have bad actors. Generative AI makes it much easier to create fake yet convincing text and images that can be used to deceive and harm. We’ve already seen lots of high-profile attempts to leverage AI in phishing campaigns, and this is putting more pressure on cybersecurity teams to get ahead of the curve and combat these new forms of threats. However, AI is also helping those that work in cybersec to be more productive and better equip themselves to create new forms of defense and offense.  Brian Murphy is a founder, CEO, entrepreneur and investor. He founded and leads ReliaQuest, the force multiplier of security operations and one of the largest and fastest-growing companies in the global cybersecurity market. ReliaQuest increases visibility, reduces complexity, and manages risk with its cloud-native security operations platform, GreyMatter. Murphy grew ReliaQuest from a boot-strapped startup to a high-growth unicorn with a valuation of over $1 billion, more than 1,000 team members, and more than $350 million in growth equity with firms such as FTV Capital and KKR Growth.  In the full episode, Adel and Brian cover the evolution of cybersecurity tools, the challenges faced by cybersecurity teams, types of cyber threats, how generative AI can be used both defensively and offensively in cybersecurity, how generative AI tools are making cybersecurity professionals more productive, the evolving role of cybersecurity professionals, the security implications of deploying AI models, the regulatory landscape for AI in cybersecurity and much more.  Links Mentioned in the Show: ReliaQuestReliaQuest BlogIBM finds that ChatGPT can generate phishing emails nearly as convincing as a humanInformation Sharing and Analysis Centers (ISACs)[Course] Introduction to Data SecurityRelated episode: Data Security in the Age of AI with Bart Vandekerckhove, Co-founder at Raito 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 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 SiegelGenerating ROI with AIBizML Cheat SheetGooderSurvey: Machine Learning Projects Still Routinely Fail to Deploy[Skill Track] MLOps Fundamentals

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 LinesJen’s TED Talk: The Future of WorkBrené Brown: Clear Is Kind. Unclear Is Unkind.What Is Psychological Safety?

2023 was a huge year for data and AI. Everyone who didn't live under a rock started using generative AI, and much was teased by companies like OpenAI, Microsoft, Google and Meta. We saw the millions of different use cases generative AI could be applied to, as well as the iterations we could expect from the AI space, such as connected multi-modal models, LLMs in mobile devices and formal legislation. But what has this meant for DataCamp? What will we do to facilitate learners and organizations around the world in staying ahead of the curve? In this special episode of DataFramed, we sit down with DataCamp Co-Founders Jo Cornelissen, Chief Executive Officer, and Martijn Theuwissen, Chief Operating Officer, to discuss their expectations for data & AI in 2024. In the episode, Richie, Jo and Martijn discuss generative AI's mainstream impact in 2023, the broad use cases of generative AI and skills required to utilize it effectively, trends in AI and software development, how the programming languages for data are evolving, new roles in data & AI, the job market and skill development in data science and their predictions for 2024. Links Mentioned in the Show: Free course - Become an AI DeveloperWebinar - Data & AI Trends & Predictions 2024 Courses: Artificial Intelligence (AI) StrategyGenerative AI for BusinessImplementing AI Solutions in BusinessAI Ethics

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 2010KDB+Q Query Language[Course] Quantitative Risk Management in PythonUnderstanding Value at Risk (VaR)

We’ve never been more aware of the word ‘hallucinate’ in a professional setting. Generative AI has taught us that we need to work in tandem with personal AI tools when we want accurate and reliable information. We’ve also seen the impacts of bias in AI systems, and why trusting outputs at face value can be a dangerous game, even for the largest tech organizations in the world. It seems we could be both very close and very far away from being able to fully trust AI in a work setting. To really find out what trustworthy AI is, and what causes us to lose trust in an AI system, we need to hear from someone who’s been at the forefront of the policy and tech around the issue.  Alexandra Ebert is an expert in data privacy and responsible AI. She works on public policy issues in the emerging field of synthetic data and ethical AI. Alexandra is on Forbes ‘30 Under 30’ list and has an upcoming course on DataCamp! In addition to her role as Chief Trust Officer at MOSTLY AI, Alexandra is the chair of the IEEE Synthetic Data IC expert group and the host of the Data Democratization podcast. In the episode, Richie and Alexandra explore the importance of trust in AI, what causes us to lose trust in AI systems and the impacts of a lack of trust, AI regulation and adoption, AI decision accuracy and fairness, privacy concerns in AI, handling sensitive data in AI systems, the benefits of synthetic data, explainability and transparency in AI, skills for using AI in a trustworthy fashion and much more.  Links Mentioned in the Show: MOSTLY.AIMicrosoft Research on AI FairnessUsing Synthetic Data for Machine Learning & AI in Python[Course] AI Ethics

Data used to be the exhaust of our work activities, until we started seeing the value it can provide. Today, data is a strategic asset, used to gain a competitive advantage and well guarded from those that might use it to harm others. With this change in attitude, how we access and safeguard our data has improved massively. However, data breaches are not a thing of the past, and with the advent of AI, many new techniques for maliciously accessing data are being created. With the extra importance of data security, it is always pertinent to iterate on how we keep our data safe, and how we manage who has access to it.  Bart Vandekerckhove is the co-founder and CEO at Raito. Raito is on a mission to bring back balance in data democratization and data security. Bart helps data teams save time on data access management, so they can focus on innovation. As the former PM Privacy at Collibra, Bart has seen first hand how slow data access management processes can harm progress.  In the full episode, Richie and Bart explore the importance of data access management, the roles involved in data access including senior management’s role in data access, data security and privacy tools, the impact of AI on data security, how culture feeds into data security, the challenges of a creating a good data access management culture, common mistakes organizations make, advice for improving data security and much more.  Links Mentioned in the Show: RaitoCapital One Data BreachOptus Data BreachIAMCourse: Introduction to Data Privacy

We learned so much about generative AI and its impact for people and organizations in 2023, we must anticipate many more innovations in the data and AI space 2024. One of the best places to look for this information is through the wisdom of those that spend their time with the Fortune 1000 leaders that are helping shape data and AI practices. Wavestone’s annual Data and AI Executive Leadership Survey is a great way to gain insight into thoughts in current practices, as well as understand what to expect from business leaders and organizations in the near future. In this episode, we speak to the author of the survey.  Randy Bean is a start-up business founder, CEO, industry thought leader, author, and speaker in the field of data-driven business leadership.  He serves as Innovation Fellow, Data Strategy for Paris-based consultancy Wavestone. Randy is the creator of the Data and AI Leadership Executive Survey discussed in today's episode. He is the author of the bestselling "Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI", and a current contributor to Forbes, Harvard Business Review, and MIT Sloan Management Review.   In the episode, Richie and Randy explore the 2024 Data and AI Leadership Executive Survey, the impact of generative AI in 2023 and what to expect from it in 2024, the state of generative AI implementation in organizations, healthcare and AI, including examples of generative AI outperforming human doctors, the evolving responsibilities of CDOs, the increasing importance of data-driven decision-making in organizations, the barriers to becoming data-driven, insights on data skills and the generational shift towards more data-savvy business leaders, as well as much more.  Links Mentioned in the Show: Data and AI Leadership Executive SurveyRandy’s Articles in ForbesAlly FinancialResponsible AI InstituteCourse: Implementing AI Solutions in Business

Regardless of profession, the work we do leaves behind a trace of actions that help us achieve our goals. This is especially true for those that work with data. For large enterprises where there are seemingly countless processes happening at any one time, keeping track of these processes is crucial. Given the scale of these processes, one small efficiency gain can leads to a staggering amount of time and money saved. Process mining is a data-driven approach to process analysis that uses event logs to extract process-related information. It can separate inferred facts, from exact truths, and uncover what really happens in a variety of operations.  Wil van der Aalst is a full professor at RWTH Aachen University, leading the Process and Data Science (PADS) group. He is also the Chief Scientist at Celonis, part-time affiliated with the Fraunhofer FIT, and a member of the Board of Governors of Tilburg University.  His research interests include process mining, Petri nets, business process management, workflow management, process modeling, and process analysis. Wil van der Aalst has published over 275 journal papers, 35 books (as author or editor), 630 refereed conference/workshop publications, and 85 book chapters. Cong Yu leads the CeloAI group at Celonis focusing on bringing advanced AI technologies to EMS products, building up capabilities for their knowledge platform, and ultimately helping enterprises in reducing process inefficiencies and achieving operational excellence. Previously, Cong was Principal (Research) Scientist / Research Director at Google Research NYC from September 2010 to July 2022, leading the NYSD/Beacon Research Group, and also taught at NYU Courant Institute of Mathematical Sciences.  In the episode, Wil, Cong, and Richie explore process mining and its development over the past 25 years, the differences between process mining and ML, AI, and data mining, popular use cases of process mining, adoption from large enterprises like BMW, HP, and Dell, the requirements for an effective process mining system, the role of predictive analytics and data engineering in process mining, how to scale process mining systems, prospects within the field and much more. Links Mentioned in the Show: CelonisGartner’s Magic Quadrant for Process MiningPM4PyProcess Query Language (PQL)[Couse] Business Process Analytics in R

There are a few caveats to using generative AI tools, those caveats have led to a few tips that have quickly become second nature to those that use LLMs like ChatGPT. The main one being: have the domain knowledge to validate the output in order to avoid hallucinations. Hallucinations are one of the weak spots for LLMs due to the nature of the way they are built, as they are trained to correlate data in order to predict what might come next in an incomplete sequence. Does this mean that we’ll always have to be wary of the output of AI products, with the expectation that there is no intelligent decision-making going on under the hood? Far from it. Causal AI is bound by reason—rather than looking at correlation, these exciting systems are able to focus on the underlying causal mechanisms and relationships. As the AI field rapidly evolves, Causal AI is an area of research that is likely to have a huge impact on a huge number of industries and problems.  Paul Hünermund is an Assistant Professor of Strategy and Innovation at Copenhagen Business School. In his research, Dr. Hünermund studies how firms can leverage new technologies in the space of machine learning and artificial intelligence such as Causal AI for value creation and competitive advantage. His work explores the potential for biases in organizational decision-making and ways for managers to counter them. It thereby sheds light on the origins of effective business strategies in markets characterized by a high degree of technological competition and the resulting implications for economic growth and environmental sustainability.  His work has been published in The Journal of Management Studies, the Econometrics Journal, Research Policy, Journal of Product Innovation Management, International Journal of Industrial Organization, MIT Sloan Management Review, and Harvard Business Review, among others.  In the full episode, Richie and Paul explore Causal AI, its differences when compared to other forms of AI, use cases of Causal AI in fields like drug development, marketing, manufacturing, and defense. They also discuss how Causal AI contributes to better decision-making, the role of domain experts in getting accurate results, what happens in the early stages of Causal AI adoption, exciting new developments within the Causal AI space and much more.  Links Mentioned in the Show: Causal Data Science in BusinessCausal AI by causaLensIntro to Causal AI Using the DoWhy Library in PythonLesson: Inference (causal) models

Over the past year, we’ve seen a full hype cycle of hysteria and discourse surrounding generative AI. It almost seems difficult to think back to a time when no one had used ChatGPT. We are in the midst of the fourth industrial revolution, and technology is moving rapidly. Better performing and more capable models are being released at a stunning rate, and with the growing presence of multimodal AI, can we expect another whirlwind year that vastly changes the state of play within AI again? Who might be able to provide insight into what is to come in 2024? Craig S. Smith is an American journalist, former executive of The New York Times, and host of the podcast Eye on AI. Until January 2000, he wrote for The Wall Street Journal, most notably covering the rise of the religious movement Falun Gong in China. He has reported for the Times from more than 40 countries and has covered several conflicts, including the 2001 invasion of Afghanistan, the 2003 war in Iraq, and the 2006 Israeli-Lebanese war. He retired from the Times in 2018 and now writes about artificial intelligence for the Times and other publications. He was a special Government employee for the National Security Commission on Artificial Intelligence until the commission's end in October 2021.  In the episode, Richie and Craig explore the 2023 advancements in generative AI, such as GPT-4, and the evolving roles of companies like Anthropic and Meta, practical AI applications for research and image generation, challenges in large language models, the promising future of world models and AI agents, the societal impacts of AI, the issue of misinformation, computational constraints, and the importance of AI literacy in the job market, the transformative potential of AI in various sectors and much more.  Links Mentioned in the Show: Eye on AIWayveAnthropicCohereMidjourneyYann Lecun

There's often a debate in technology ethics on whether technology is neutral or not. On one hand, critics have rightfully pointed out examples of technology exacerbating the climate crisis, amplifying bias as we've seen in our recent episode with Dr. Joy Buolamwini, or contributing to the spread of misinformation and disinformation. Conversely, we cannot deny the many wonderful things technology has given us, from better healthcare outcomes, to the ability to communicate wherever we are in the world, or to elevate the quality of life of everyone on the planet. It is this duality, that today's guest, Marga Hoek, points to as to why technology is neutral, and why it is in our hands to use it for good. Marga Hoek is a true visionary on sustainable business, capital, and technology and a successful business leader. As a three-time CEO, Board Member, Chair, and Founder of Business for Good, she applies her vision on how business can be a true force for good in practice. As a bestselling and multi-award-winning author, member of Thinkers50, and one of the most in-demand speakers on sustainable business and ESG investment, Marga Hoek has inspired many companies and leaders worldwide. She is also appreciated as a global voice for G20 and G7 Intergovernmental forums, international climate meetings and COPs, and many other prestigious global conferences.  In the episode, Adel and Marga explore the fourth industrial revolution and the eight technologies that combine to build it, the ethical application of technology and how it can be the biggest lever to combating climate change and building a sustainable society, how data and AI enable real-time information sharing leading to better early warning systems related to the environment, use cases of tech for good initiatives, how collaboration can bridge the gap in investment for sustainable business ventures and a lot more.  Links Mentioned In the Show: Tech for GoodAzure FarmBeatsCapgemini in the Mojave DesertReDeTec 3D PrintingFramlab 3D Printed Homes for the Unsheltered

We are all guilty of getting excited about shiny new toys in whatever guise they present themselves to us. For many of us, lots of the recent shiny new toys have been ways of utilizing AI to update and iterate on the ways that we work. Leadership teams have been looking for ways that their organizations can incorporate AI solutions into their products, regardless of whether they might be the most valuable use of the company's time. A company that fails to incorporate new tools and technology will stagnate and fail altogether right? A failure to adapt to the new state of play will surely stop the company from becoming a high performer? Or will it? What sets apart high-performing organizations from their non high-performing counterparts? It’s not shiny new toys. It’s culture. Counter to conventional wisdom, the norms and beliefs of an organization, and not the technology and tools it uses, is what drives its performance. Andrew McAfee is a Principal Research Scientist at the MIT Sloan School of Management, co-founder and co-director of MIT’s Initiative on the Digital Economy, and the inaugural Visiting Fellow at the Technology and Society organization at Google. He studies how technological progress changes the world. His book, The Geek Way, reveals a new way to get big things done. His previous books include More from Less and, with Erik Brynjolfsson, The Second Machine Age. McAfee has written for publications including Foreign Affairs, Harvard Business Review, The Economist, The Wall Street Journal, and The New York Times. He's talked about his work on CNN and 60 Minutes, at the World Economic Forum, TED, and the Aspen Ideas Festival, with Tom Friedman and Fareed Zakaria, and in front of many international and domestic audiences. He’s also advised many of the world’s largest corporations and organizations ranging from the IMF to the Boston Red Sox to the US Intelligence Community. Throughout the episode, Adel and Andrew explore the four cultural norms of the Geek way, the evolutionary biological underpinnings of the traits high performing organizations exhibit, case studies in adapting organizational culture, the role of data in driving high performance teams, useful frameworks leaders can adopt to build high performing organizations, and a lot more. Link mentioned in the show: The Geek Way: The Radical Mindset That Drives Extraordinary Results by Andrew McAfeeThe Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Andrew McAfee and Erik BrynjolfssonThe Planning FallacyAnnie DukeSteven PinkerAdam Grant

Poor data engineering is like building a shaky foundation for a house—it leads to unreliable information, wasted time and money, and even legal problems, making everything less dependable and more troublesome in our digital world. In the retail industry specifically, data engineering is particularly important for managing and analyzing large volumes of sales, inventory, and customer data, enabling better demand forecasting, inventory optimization, and personalized customer experiences. It helps retailers make informed decisions, streamline operations, and remain competitive in a rapidly evolving market. Insight and frameworks learned from data engineering practices can be applied to a multitude of people and problems, and in turn, learning from someone who has been at the forefront of data engineering is invaluable.   Mohammad Sabah is SVP of Engineering and Data at Thrive Market, and was appointed to this role in 2018. He joined the company from The Honest Company where he served as VP of Engineering & Chief Data Scientist. Sabah joined The Honest Company following its acquisition of Insnap, which he co-founded in 2015. Over the course of his career, Sabah has held various data science and engineering roles at companies including Facebook, Workday, Netflix, and Yahoo! In the episode, Richie and Mo explore the importance of using AI to identify patterns and proactively address common errors, the use of tools like dbt and SODA for data pipeline abstraction and stakeholder involvement in data quality, data governance and data quality as foundations for strong data engineering, validation layers at each step of the data pipeline to ensure data quality, collaboration between data analysts and data engineers for holistic problem-solving and reusability of patterns, ownership mentality in data engineering and much more.  Links from the show: PagerDutyDomoOpsGeneCareer Track: Data Engineer

In 2015 an MIT Researcher set out to build a mirror that would augment their face to look like those of their idols. The execution of this went well, until it came to testing. When the researcher came to use the mirror, no face was detected. The researcher was not detected in the mirror, until that is, she put on a white mask, at which point, the mirror worked as expected.  Three years later, a paper named ‘Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification’ was published by the same researcher. Its release started a wider conversation about bias within AI-based facial recognition systems, and about bias within AI in general. Work to fight against algorithmic bias, or ‘The Coded-Gaze’, has been ongoing since. But who spearheaded this work and highlighted these issues to the AI and tech community?  Dr. Joy Buolamwini is an AI researcher, artist, and advocate. In 2023, she is one of Time’s top 100 most influential people in AI. Joy founded the Algorithmic Justice League to create a world with more equitable and accountable technology. Her TED Featured Talk on algorithmic bias has over 1.5 million views and in 2020 Netflix released the documentary ‘Coded Bias’ following Joy’s research into the flaws of facial recognition systems. Her MIT thesis methodology uncovered large racial and gender bias in AI services from companies like Microsoft, IBM, and Amazon. Her research has been covered in over 40 countries, and as a renowned international speaker she has championed the need for algorithmic justice at the World Economic Forum and the United Nations. She serves on the Global Tech Panel convened by the vice president of European Commission to advise world leaders and technology executives on ways to reduce the harms of A.I. As a creative science communicator, she has written op-eds on the impact of artificial intelligence for publications like TIME Magazine and New York Times. Her spoken word visual audit "AI, Ain't I A Woman?" which shows AI failures on the faces of iconic women like Oprah Winfrey, Michelle Obama, and Serena Williams as well as the Coded Gaze short have been part of exhibitions ranging from the Museum of Fine Arts, Boston to the Barbican Centre, UK. A Rhodes Scholar and Fulbright Fellow, Joy has been named to notable lists including Bloomberg 50, Tech Review 35 under 35, , Forbes Top 50 Women in Tech (youngest), and Forbes 30 under 30. She holds two masters degrees from Oxford University and MIT; and a bachelor's degree in Computer Science from the Georgia Institute of Technology. Fortune Magazine named her to their 2019 list of world's greatest leaders describing her as "the conscience of the A.I. Revolution." In the episode, Richie and Joy discuss her journey into AI, the ethics of AI, the inception of Joy’s interest in AI bias, the Aspire Mirror and Gender Shades projects, The Algorithmic Justice League, consequences of biased facial recognition systems, highlights from Joy’s book (Unmasking AI), challenges in AI research such as misleading datasets and the importance of context, balancing working in AI and data while being an artist, and much more.  Links mentioned in the show: Unmasking AI by Joy BuolamwiniAlgorithmic Justice LeagueGender Shades ProjectThe Coded Gaze

I think it's safe to say that we are in the peak of the hype cycle with generative AI. Almost every week now, we see new startups with exciting new GenAI use-cases and products. However, exciting doesn't necessarily translate to useful. And now more than ever, it's important for leaders, whether at incumbents or startups, to adapt and drive value with generative AI and focus on useful use-cases. So how can they adapt well to these tectonic changes? Jason Feifer is the editor in chief of Entrepreneur magazine and host of the podcast Problem Solvers. Outside of Entrepreneur, he is the author of the book Build For Tomorrow, which helps readers find new opportunities in times of change, and co-hosts the podcast Help Wanted, where he helps solve listeners' work problems. He also writes a newsletter called One Thing Better, which each week gives you one better way to build a career or company you love. In the episode, Jason and Adel explore AI’s role in entrepreneurship, use cases and applications of AI, the effectiveness of certain AI tools, AI’s impact on established business models, frameworks for navigating change, advice for leaders and individuals on using AI in their work and much more.  Links Mentioned in the Show: Build for Tomorrow by Jason FeiferOne Thing Better NewsletterHeyGenBurger King Accepting Credit Cards in the 90s[COURSE] Implementing AI Solutions in Business