As AI continues to dominate industry conversations, the notion of AI readiness becomes a focal point for organizations. It's a multifaceted challenge that goes beyond technology, encompassing business processes and cultural shifts. For professionals, this means grappling with questions like: How do you choose the right AI projects that align with business goals? What skills and team structures are necessary to support AI initiatives? And how do you manage the change that comes with integrating AI into your operations? Venky Veeraraghavan is the Chief Product Officer at DataRobot. As CPO, Venky drives the definition and delivery of the DataRobot Enterprise AI Suite. Venky has twenty-five years of experience focusing on big data and AI as a product leader and technical consultant at top technology companies (Microsoft) and early-stage startups (Trilogy). In the episode, Richie and Venky Veeraraghavan explore AI readiness in organizations, the importance of aligning AI with business processes, the roles and skills needed for AI integration, the balance between building and buying AI solutions, the challenges of implementing AI-driven changes, and much more. Links Mentioned in the Show: DatarobotConnect with VenkySkill Track: Artificial Intelligence (AI) LeadershipRelated Episode: Aligning AI with Enterprise Strategy with Leon Gordon, CEO at Onyx DataAttend RADAR Skills 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

As AI continually changes how businesses operate, new questions emerge around ethics and privacy. Nowadays, algorithms can set prices and personalize offers, but how do companies ensure they’re doing this responsibly? What does it mean to be transparent with customers about data use, and how can businesses avoid unintended bias? Balancing innovation with trust is key, but achieving this balance isn’t always straightforward. Dr. Jose Mendoza is Academic Director and Clinical Associate Professor in Integrated Marketing at NYU, and was formerly an Associate Professor of Practice at The University of Arizona in Tucson, Arizona. His focus is on consumer pricing, digital retailing, intelligent retail stores, neuromarketing, big data, artificial intelligence, and machine learning. Previously, he taught marketing courses at Sacred Heart University and Western Michigan University. He is also an experienced senior global marketing executive with over 18 years of experience in global marketing alone and a career as an Engineer in Information Sciences. Dr. Mendoza is also a Doctoral Researcher in Strategic and Global pricing, Consumer Behavior, and Pricing Research methodologies. He had international roles in Latin America, Europe, and the USA with scope in over 50 countries.  In the episode, Richie and Jose explore AI-driven pricing, consumer perceptions and ethical pricing, the complexity of dynamic pricing models, explainable AI, data privacy and customer trust, legal and ethical guardrails, innovations in dynamic pricing and much more.  Links Mentioned in the Show: NYUConnect with JoseAmazon Dynamic Pricing Strategy in 2024Course: AI EthicsRelated Episode: The Future of Marketing Analytics with Cory Munchbach, CEO at BlueConicSign up to RADAR: Forward 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

Building a robust data infrastructure is crucial for any organization looking to leverage AI and data-driven insights. But as your data ecosystem grows, so do the challenges of managing, securing, and scaling it. How do you ensure that your data infrastructure not only meets today’s needs but is also prepared for the rapid changes in technology tomorrow? What strategies can you adopt to keep your organization agile, while ensuring that your data investments continue to deliver value and support business goals? Saad Siddiqui is a venture capitalist for Titanium Ventures. Titanium focus on enterprise technology investments, particularly focusing on next generation enterprise infrastructure and applications. In his career, Saad has deployed over $100M in venture capital in over a dozen companies. In previous roles as a corporate development executive, he has executed M&A transactions valued at over $7 billion in aggregate. Prior to Titanium Ventures he was in corporate development at Informatica and was a member of Cisco's venture investing and acquisitions team covering cloud, big data and virtualization.  In the episode, Richie and Saad explore the business impacts of data infrastructure, getting started with data infrastructure, the roles and teams you need to get started, scalability and future-proofing, implementation challenges, continuous education and flexibility, automation and modernization, trends in data infrastructure, and much more.  Links Mentioned in the Show: Titanium VenturesConnect with SaadCourse - Artificial Intelligence (AI) StrategyRelated Episode: How are Businesses Really Using AI? With Tathagat Varma, Global TechOps Leader at Walmart Global TechRewatch sessions from 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

Businesses are collecting more data than ever before. But is bigger always better? Many companies are starting to question whether massive datasets and complex infrastructure are truly delivering results or just adding unnecessary costs and complications. How can you make sure your data strategy is aligned with your actual needs? What if focusing on smaller, more manageable datasets could improve your efficiency and save resources, all while delivering the same insights? Ryan Boyd is the Co-Founder & VP, Marketing + DevRel at MotherDuck. Ryan started his career as a software engineer, but since has led DevRel teams for 15+ years at Google, Databricks and Neo4j, where he developed and executed numerous marketing and DevRel programs. Prior to MotherDuck, Ryan worked at Databricks and focussed the team on building an online community during the pandemic, helping to organize the content and experience for an online Data + AI Summit, establishing a regular cadence of video and blog content, launching the Databricks Beacons ambassador program, improving the time to an “aha” moment in the online trial and launching a University Alliance program to help professors teach the latest in data science, machine learning and data engineering. In the episode, Richie and Ryan explore data growth and computation, the data 1%, the small data movement, data storage and usage, the shift to local and hybrid computing, modern data tools, the challenges of big data, transactional vs analytical databases, SQL language enhancements, simple and ergonomic data solutions and much more.  Links Mentioned in the Show: MotherDuckThe Small Data ManifestoConnect with RyanSmall DataSF conferenceRelated Episode: Effective Data Engineering with Liya Aizenberg, Director of Data Engineering at AwayRewatch sessions from 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

Every organization today is exploring generative AI to drive value and push their business forward. But a common pitfall is that AI strategies often don’t align with business objectives, leading companies to chase flashy tools rather than focusing on what truly matters. How can you avoid these traps and ensure your AI efforts are not only innovative but also aligned with real business value?  Leon Gordon, is a leader in data analytics and AI. A current Microsoft Data Platform MVP based in the UK, founder of Onyx Data. During the last decade, he has helped organizations improve their business performance, use data more intelligently, and understand the implications of new technologies such as artificial intelligence and big data. Leon is an Executive Contributor to Brainz Magazine, a Thought Leader in Data Science for the Global AI Hub, chair for the Microsoft Power BI – UK community group and the DataDNA data visualization community as well as an international speaker and advisor. In the episode, Adel and Leon explore aligning AI with business strategy, building AI use-cases, enterprise AI-agents, AI and data governance, data-driven decision making, key skills for cross-functional teams, AI for automation and augmentation, privacy and AI and much more.  Links Mentioned in the Show: Onyx DataConnect with LeonLeon’s Linkedin Course - How to Build and Execute a Successful Data StrategySkill Track: AI Business FundamentalsRelated Episode: Generative AI in the Enterprise with Steve Holden, Senior Vice President and Head of Single-Family Analytics at Fannie MaeRewatch sessions from 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

Guardrails are not something we actively use in our day-to-day lives, they’re in place to keep us safe when we lack the control needed to keep us on course, and for that, they are essential. Navigating the complexities of decision-making in AI and data can be challenging, especially on a global scale when many are searching for any sort of competitive advantage. Every choice you make can have significant impacts, and having the right frameworks, ethics and guardrails in place are crucial. But how do you create systems that guide decisions without stifling creativity or flexibility? What practices can you employ to ensure your team consistently make better choices and flourish in the age of AI? Viktor Mayer-Schönberger is a distinguished Professor of Internet Governance and Regulation at the Oxford Internet Institute, University of Oxford. With a career spanning over decades, his research focuses on the role of information in a networked economy. He previously served on the faculty of Harvard’s Kennedy School of Government for ten years and has authored several influential books, including the award-winning “Delete: The Virtue of Forgetting in the Digital Age” and the international bestseller “Big Data.” Viktor founded Ikarus Software in 1986, where he developed Virus Utilities, Austria’s best-selling software product. He has been recognized as a Top-5 Software Entrepreneur in Austria and has served as a personal adviser to the Austrian Finance Minister on innovation policy. His work has garnered global attention, featuring in major outlets like the New York Times, BBC, and The Economist. Viktor is also a frequent public speaker and an advisor to governments, corporations, and NGOs on issues related to the information economy. In the episode, Richie and Viktor explore the definition of guardrails, characteristics of good guardrails, guardrails in business contexts, life-or-death decision-making, principles of effective guardrails, decision-making and cognitive bias, uncertainty in decision-making, designing guardrails, AI and the implementation of guardrails, and much more. Links Mentioned in the Show: Guardrails: Guiding Human Decisions in the Age of AI by Urs Gasser and Viktor Mayer-SchönbergerBook - The Checklist Manifesto by Atul GawandeConnect with ViktorCourse - AI EthicsRelated Episode: Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision ScientistRewatch sessions from RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business

Whether big or small, one of the biggest challenges organizations face when they want to work with data effectively is often lack of access to it. This is where building a data platform comes in. But building a data platform is no easy feat. It's not just about centralizing data in the data warehouse, it’s also about making sure that data is actionable, trustable and usable. So, how do you make sure your data platform is up to par? Shuang Li is Group Product Manager at Box. With experience of building data, analytics, ML, and observability platform products for both external and internal customers, Shuang is always passionate about the insights, optimizations, and predictions that big data and AI/ML make possible. Throughout her career, she transitioned from academia to engineering, from engineering to product management, and then from an individual contributor to an emerging product executive. In the episode, Adel and Shuang explore her career journey, including transitioning from academia to engineering and helping to work on Google Fiber, how to build a data platform, ingestion pipelines, processing pipelines, challenges and milestones in building a data platform, data observability and quality, developer experience, data democratization, future trends and a lot more.  Links Mentioned in the Show: BoxConnect with Shuang on Linkedin[Course] Understanding Modern Data ArchitectureRelated 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

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...

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

Effective data management has become a cornerstone of success in our digital era. It involves not just collecting and storing information but also organizing, securing, and leveraging data to drive progress and innovation. Many organizations turn to tools like Snowflake for advanced data warehousing capabilities. However, while Snowflake enhances data storage and access, it's not a complete solution for all data management challenges. To address this, tools like Capital One’s Slingshot can be used alongside Snowflake, helping to optimize costs and refine data management strategies. Salim Syed is a VP, Head of engineering for Capital One Slingshot product. He led Capital One’s data warehouse migration to AWS and is a specialist in deploying Snowflake to a large enterprise. Salim’s expertise lies in developing Big Data (Lake) and Data Warehouse strategy on the public cloud. He leads an organization of more than 100 data engineers, support engineers, DBAs and full stack developers in driving enterprise data lake, data warehouse, data management and visualization platform services. Salim has more than 25 years of experience in the data ecosystem. His career started in data engineering where he built data pipelines and then moved into maintenance and administration of large database servers using multi-tier replication architecture in various remote locations. He then worked at CodeRye as a database architect and at 3M Health Information Systems as an enterprise data architect. Salim has been at Capital One for the past six years. In this episode, Adel and Salim explore cloud data management and the evolution of Slingshot into a major multi-tenant SaaS platform, the shift from on-premise to cloud-based data governance, the role of centralized tooling, strategies for effective cloud data management, including data governance, cost optimization, and waste reduction as well as insights into navigating the complexities of data infrastructure, security, and scalability in the modern digital era. Links Mentioned in the Show: Capital One SlingshotSnowflakeCourse: Introduction to Data WarehousingCourse: Introduction to Snowflake

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

Cindi was previously a Gartner Research Vice President, the lead author for the data and analytics maturity model and analytics and BI Magic Quadrant, and a popular keynote speaker. She introduced new research in data and AI for good, NLP/BI Search, and augmented analytics, bringing both BI bake-offs and innovation panels to Gartner globally. She’s frequently quoted in MIT, Harvard Business Review, and Information Week. She is rated a top 12 influencer in big data and analytics by Analytics Insight, Onalytca, Solutions Review, and Humans of Data.

In the episode, Cindi and Adel discuss how generative AI accelerates an organization’s data literacy, how leaders can think beyond data literacy and start to think about AI literacy, the importance of responsible use of AI, how to best communicate the value of AI within your organization, what generative AI means for data teams, AI use-cases in the data space, the psychological barriers blocking AI adoption, and much more. 

Links Mentioned in the Show: The Data Chief Podcast  ThoughtSpot Sage  BloombergGPT  Radar: Data & AI Literacy Course: AI Ethics  Course: Generative AI Concepts Course: Implementing AI Solutions in Business 

About 10 years ago, Thomas Davenport & DJ Patil published the article "Data Scientist: The Sexiest Job of the 21st Century" in the Harvard Business Review. In this piece, they described the bourgeoning role of the data scientist and what it will mean for organizations and individuals in the coming decade. As time has passed, data science has become increasingly institutionalized. Once seen as a luxury, it is now deemed a necessity in every modern boardroom. Moreover as technologies like AI and systems like ChatGPT keep astonishing us with their capabilities in handling data science tasks, it raises a pertinent question: Is Data Science Still the Sexiest Job of the 21st Century? In this episode, we invited Thomas Davenport on the show to share his perspective on where data science & AI are at today, and where they are headed. Thomas Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, the co-founder of the International Institute for Analytics, a Fellow of the MIT Initiative for the Digital Economy, and a Senior Advisor to Deloitte Analytics. He has written or edited twenty books and over 250 print or digital articles for Harvard Business Review (HBR), Sloan Management Review, the Financial Times, and many other publications. One of HBR’s most frequently published authors, Thomas has been at the forefront of the Process Innovation, Knowledge Management, and Analytics and Big Data movements. He pioneered the concept of “competing on analytics” with his 2006 Harvard Business Review article and his 2007 book by the same name. Since then, he has continued to provide cutting-edge insights on how companies can use analytics and big data to their advantage, and then on artificial intelligence. Throughout the episode, we discuss how data science has changed since he first published his article, how it has become more institutionalized, how data leaders can drive value with data science, the importance of data culture, his views on AI and where he thinks its going, and a lot more. Links from the Show: Working with AI by Thomas Davenport The AI Advantage: How to Put the Artificial Intelligence Revolution to Work by Thomas Davenport Harvard Business Review New Vantage Partners CCC Intelligent Solutions Radar AI