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 SecurityBooks by Bruce[Course] AI EthicsRelated Episode: Building Trustworthy AI with Alexandra Ebert, Chief Trust Officer at MOSTLY AISign up to RADAR: AI 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

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 LinkedinConnect with Vin on LinkedinVin’s Website[Course] Data Governance Concepts 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 appEmpower your business with world-class data and AI skills with DataCamp for business

As we close out our focus on how the venture capital industry identifies and decides which future companies to fund, it might be easy to fall into the trap of thinking that the latest methods for discovering future unicorns are ubiquitous among all VCs. However, many VCs still work ‘the old way,’ using data to back up human assumptions. But what happens when a data engineer pivots to VC? What does a data-driven, data-first approach look like, and how does it compare to the incumbent processes? Dr. Andre Retterath is a Partner in Earlybird’s Munich Office, focussing on enterprise software with a particular interest in developer, data and productivity tools, alongside AI-centric products and robotics. Before transitioning into VC in 2017, he gained more than 5 years of experience as a process automation and predictive maintenance engineer at ThyssenKrupp and further insights as a management consultant at GE North America. Andre also has his own VC, AI & data newsletter, Data-Driven VC. In the episode, Richie and Andre explore the concept of data-driven venture capital, the challenges of traditional VC and why digitization has had a huge impact on the industry, the data-driven VC process, the use of modern data and AI technologies in identifying potentially successful projects, the human element in VC, the challenges and opportunities of early-stage investments, the importance of early identification of these ventures, cultural and organizational indicators and much more.  Links Mentioned in the Show: Data-Driven VCEarlybird VCAleph AlphaPareto PrincipleRelated Episode: Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision ScientistSign up to RADAR: AI Edition 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

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 WebsiteSequoia CapitalStanford University Related Episode: Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision ScientistSign up to RADAR: AI 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

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 BusinessRelated Episode: Inside Algorithmic Trading with Anthony Markham, Vice President, Quantitative Developer at Deutsche BankSign up to RADAR: AI Edition New to DataCamp? Learn on the go using thea href="https://www.datacamp.com/mobile" rel="noopener noreferrer"...

With seemingly every organization wanting to enhance their AI capabilities, questions arise about who should be in charge of these initiatives. At the moment, it’s likely a CTO, CIO, or CDO, or a mixture of the three. The gold standard is to have someone in the C-suite whose sole focus is their AI projects: the Chief AI Officer. This role is so new that it's not yet widely understood. In this episode, we explore what the CAIO job entails. Philipp Herzig is the Chief AI Officer at SAP. He’s held a variety of roles within SAP, most recently SVP Head of Cross Product Engineering & Experience, however his experience covers intelligent enterprise & cross-architecture, head of engineering for cloud-native apps, a software development manager, and product owner.  In the full episode, Richie and Philipp explore what his day-to-day responsibilities are as a CAIO, the holistic approach to cross-team collaboration, non-technical interdepartmental work, AI strategy and implementation, challenges and success metrics, how to approach high-value AI use cases, insights into current AI developments and the importance of continuous learning, the exciting future of AI and much more. 

Links Mentioned in the Show: SAP’s AI CoPilot JouleSAP[Course] Implementing AI Solutions in BusinessRelated Episode: How Walmart Leverages Data & AI with Swati Kirti, Sr Director of Data Science at WalmartRewatch sessions from 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

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 LanguageDon’s Book: SQL++ for SQL Users: A TutorialSystem R: Relational approach to database managementSQL CoursesSQL Articles, Tutorials and Code-AlongsRelated Episode: Scaling Enterprise Analytics with Libby Duane Adams, Chief Advocacy Officer and Co-Founder of AlteryxRewatch sessions from 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

Last year saw the proliferation of countless AI tools and initiatives, many companies looked to find ways where AI could be leveraged to reduce operational costs and pressure wherever possible. 2023 was a year of experimentation for anyone trying to harness AI, but we can’t walk forever. To keep up with the rapidly changing landscape in business, last year’s experiments with AI need to find their feet and allow us to run. But how do we know which initiatives are worth fully investing in? Will your company culture impede the change management that is necessary to fully adopt AI? Sanjay Srivastava is the Chief Digital Strategist at Genpact. He works exclusively with Genpact’s senior client executives and ecosystem technology leaders to mobilize digital transformation at the intersection of cutting-edge technology, data strategy, operating models, and process design. In his previous role as Chief Digital Officer at Genpact, Sanjay built out the company’s offerings in artificial intelligence, data and analytics, automation, and digital technology services. He leads Genpact’s artificial-intelligence-enabled platform that delivers industry-leading governance, integration, and orchestration capabilities across digital transformations. Before joining Genpact, Sanjay was a Silicon Valley serial entrepreneur and built four high-tech startups, each of which was successfully acquired by Akamai, BMC, FIS, and Genpact, respectively. Sanjay also held operating leadership roles at Hewlett Packard, Akamai, and SunGard (now FIS), where he oversaw product management, global sales, engineering, and services businesses. In the episode, Sanjay and Richie cover the shift from experimentation to production seen in the AI space over the past 12 months, the importance of corporate culture in the adoption of AI in a business environment, how AI automation is revolutionizing business processes at GENPACT, how change management contributes to how we leverage AI tools at work, adapting skill development pathways to make the most out of AI, how AI implementation changes depending on the size of your organization, future opportunities for AI to change industries and much more.  Links Mentioned in the Show: Genpact[Course] Implementing AI Solutions in BusinessArticle: AI adoption accelerates as enterprise PoCs show productivity gainsRelated Episode: How Generative AI is Changing Business and Society with Bernard Marr, AI Advisor, Best-Selling Author, and FuturistRewatch sessions from 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

There aren’t many retail giants like Walmart. In fact, there are none. The multinational generates 650bn in revenue, (including 50bn in eCommerce)—the highest revenue of any retailer globally. With over 10,000 stores worldwide and a constantly evolving product line, Walmart’s data & AI function has a lot to contend with when it comes to customer experience, demand forecasting, supply chain optimization and where to use AI effectively. So how do they do it? What can we learn from one of the most successful and well-known organizations on the planet? Swati Kirti is a Senior Director of Data Science, leading the AI/ML charter for Walmart Global Tech’s international business in Canada, Mexico, Central America, Chile, China, and South Africa. She is responsible for building AI/ML models and products to enable automation and data-driven decisions, powering superior customer experience and realizing value for omnichannel international businesses across e-commerce, stores, supply chain, and merchandising. In the episode, Swati and Richie explore the role of data and AI at Walmart, how the data and AI teams operate under Swati’s supervision, how Walmart improves customer experience through the use of data, supply chain optimization, demand forecasting, retail-specific data challenges, scaling AI solutions, innovation in retail through AI and much more.  Links Mentioned in the Show: Article - Walmart’s Generative AI search puts more time back in customers' handsWalmart Global Tech[Course] Implementing AI Solutions in BusinessRelated Episode: How Generative AI is Changing Business and Society with Bernard Marr, AI Advisor, Best-Selling Author, and FuturistRewatch sessions from 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

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 PracticeBernard’s Website, Twitter and Linkedin[Skill Track] AI Business FundamentalsRelated Episode: Adapting to the AI Era with Jason Feifer, Editor in Chief of Entrepreneur Magazine 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 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

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

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"

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...

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 SilvermanConnect with Lori on LinkedinLevel Up with LoriBooks by LoriThe SMARTER Framework for Data-Informed Decision MakingMonetizing Data Through Informed, Collaborative Decision MakingThe Increasingly Vital Role of Business Storytelling in LeadershipPre-Suasion: A Revolutionary Way to Influence and Persuade by Robert Cialdini[Skill Track] Data Storytelling

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)

Cookies were invented to help online shoppers, simply as an identifier so that online carts weren’t lost to the ether. Marketers quickly saw the power of using cookies for more than just maintaining session states, and moved to use them as part of their targeted advertising. Before we knew it, our online habits were being tracked, without our clear consent. The unregulated cookie-boom lasted until 2018 with the advent of GDPR and the CCPA. Since then marketers have been evolving their practices, looking for alternatives to cookie-tracking that will perform comparatively, and with the cookie being phased out in 2024, technologies like fingerprinting and new privacy-centric marketing strategies will play a huge role in how products meet users in the future.  Cory Munchbach has spent her career on the cutting edge of marketing technology and brings years working with Fortune 500 clients from various industries to BlueConic. Prior to BluConic, she was an analyst at Forrester Research where she covered business and consumer technology trends and the fast-moving marketing tech landscape. A sought-after speaker and industry voice, Cory’s work has been featured in Financial Times, Forbes, Raconteur, AdExchanger, The Drum, Venture Beat, Wired, AdAge, and Adweek. A life-long Bostonian, Cory has a bachelor’s degree in political science from Boston College and spends a considerable amount of her non-work hours on various volunteer and philanthropic initiatives in the greater Boston community.  In the episode, Richie and Cory cover successful marketing strategies and their use of data, the types of data used in marketing, how data is leveraged during different stages of the customer life cycle, the impact of privacy laws on data collection and marketing strategies, tips on how to use customer data while protecting privacy and adhering to regulations, the importance of data skills in marketing, the future of marketing analytics and much more. Links Mentioned in the Show: BlueConicMattel CreationsGoogle: Prepare for third-party cookie restrictionsData Clean Rooms[Course] Marketing Analytics for Business

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

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 MeasureLea’s BookConnect with Lea on LinkedinHollywood Storytelling[Course] Data Storytelling Concepts

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