Podcasts about thoughtspot

Embark on an insightful exploration of the hospitality sector, powered by data-driven analysis. Cindi Howson and Hyatt's data trailblazer, Ray Boyle (Vice President, Data and Analytics), are charting a course through Hyatt's data innovation. Witness how Hyatt's four-pillar data strategy is revolutionizing everything from employee empowerment to guest personalization and operational efficiency. Discover how Hyatt is democratizing data with self-service tools and pioneering an AI-powered frontier to redefine the very essence of hospitality!Key Moments: Data as an Asset (08:26): Ray emphasizes the importance of shifting the organizational mindset to view data not as a cost center, but as a critical asset. He discusses how data should be cared for, invested in, and stored like any other valuable asset, with the expectation of generating value for the business.  Hyatt's Data Strategy Pillars (13:00): Ray outlines the four key pillars that form the foundation of Hyatt's data strategy. These pillars include cultivating people and building a data-driven culture, personalizing the guest and customer experience in a high-trust environment, operating with excellence by ensuring operational efficiency and information consistency, and growing with intent by integrating new businesses and data flows.  Key Milestones in Hyatt's Data Transformation (16:42): Ray details the significant milestones in Hyatt's data transformation journey. These include clarifying the data strategy, establishing the data and AI operating model, building data governance capabilities, modernizing the data platform and infrastructure, expanding data assets, and releasing new services like personalization and forecasting.  Data Democratization and Data Fluency (23:00): Ray explains Hyatt's strong emphasis on self-service analytics to empower users across the organization. He discusses the importance of data accessibility, trustworthiness, and usability, as well as the potential of generative AI to further democratize data access and insights.  This includes building a data community to facilitate knowledge sharing and learning, as well as providing tooling and guidance to business organizations to effectively roll out analytics within their domains.  AI's Impact and Collaboration (31:35): Ray explores the transformative impact of AI on businesses and its role in fostering tighter collaboration between business and technology teams. He discusses how AI is driving the need for reimagined workflows and how it's changing the way data is used and delivered across the organization.Key Quotes:“ThoughtSpot has been a key partner of ours on that journey. We just roll the data into the cloud, and we're working to publish our assets, sales, finance, loyalty, revenue, search, and marketing into that infrastructure so that there's just a growing base of information that everybody can use in the self-service context.” - Raymond Boyle"Velocity is something you build over time. It's how I think about the operating model around data, ensuring everyone plays their role and develops the necessary skills. To me, velocity increases as you establish the operating model and you have the business, technology, and data organizations, along with governance and security, all participating effectively. - Raymond Boyle"When you think about the business outcomes and how people are beginning to consider AI's potential in that transformation, I believe AI is becoming a more significant factor every quarter." - Raymond BoyleMentionsThe Four V's of Big Data, Including VelocityDalva, By Jim HarrisonMinnesota Timberwolves' SuccessGuest Bio Ray Boyle (current Vice President, Data and Analytics at Hyatt) has enjoyed a distinguished career spanning several industries and roles across consulting, software, analytics, and data leadership. His notable roles include leading strategic planning, research, and analytics for Walmart's Sam's Club division; serving as Vice President of Walmart Global Customer Insights and Analytics; Vice President of Walmart's Global Data and Analytics Platform; Vice President leading FICO's global retail and CPG practice; and Executive Vice President heading IRI's Global Shopper Analytics and Services team.Since 2019, Ray has served as Vice President, Data and Analytics at Hyatt. Aligned with Hyatt's purpose — to care for people so they can be their best — his ambition is to elevate and scale that care through data-driven decisions and automation that benefit guests, customers, owners, and colleagues.Guest Bio Ray Boyle (current Vice President, Data and Analytics at Hyatt) has enjoyed a distinguished career spanning several industries and roles across consulting, software, analytics, and data leadership. His notable roles include leading strategic planning, research, and analytics for Walmart's Sam's Club division; serving as Vice President of Walmart Global Customer Insights and Analytics; Vice President of Walmart's Global Data and Analytics Platform; Vice President leading FICO's global retail and CPG practice; and Executive Vice President heading IRI's Global Shopper Analytics and Services team.Since 2019, Ray has served as Vice President, Data and Analytics at Hyatt. Aligned with Hyatt's purpose — to care for people so they can be their best — his ambition is to elevate and scale that care through data-driven decisions and automation that benefit guests, customers, owners, and colleagues. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

In this episode of The Effortless Podcast, host Amit Prakash sits down with Professor Debdeep Jena, a leading expert in semiconductors, superconductors, and quantum devices at Cornell University. They explore the fascinating world of quantum computing, from its early 20th-century origins to its transformative potential in modern technology.Professor Jena delves into key concepts of quantum physics and quantum computing, shedding light on quantum systems, qubits, and the challenges and promises of quantum hardware. With decades of experience in semiconductor research, he explains how quantum computing could revolutionize industries, from computational speed to energy efficiency.In this conversation, they discuss:The birth of quantum mechanics and its evolution into quantum computingThe role of qubits and superposition in quantum devicesHow quantum computing is tackling complex problems beyond classical computingCurrent advancements in quantum hardware and the roadblocks still aheadProfessor Jena's perspective on the future of quantum technology and its potential impact on industries like AI, communications, and beyondThis episode is a must-watch for anyone curious about the future of quantum technology and its applications in modern science and industry. Professor Jena provides unique insights into how quantum systems are poised to transform computing, energy efficiency, and even artificial intelligence. Whether you're a tech enthusiast, a student of physics, or a professional exploring the frontier of quantum technology, this conversation is packed with invaluable knowledge.Key Topics & Timestamps:00:00 – Introduction to Quantum Mechanics, Entanglement, and the Role of Information in Physics05:00 – Classical Computation vs. Quantum Computation: Understanding the Basics of Classical and Quantum Bits12:00 – The Role of Information Erasure and Its Link to Energy Loss in Classical Computing18:00 – Superposition and Entanglement: The Basis of Quantum Computation25:00 – Bell's Theorem and the EPR Paradox: Understanding Quantum Nonlocality32:00 – Quantum Measurement and the Challenge of Formulating the Right Questions in Quantum Computation40:00 – Shor's Algorithm and the Promise of Quantum Speedup for Prime Factorization45:00 – Practical Quantum Computing: Grover's Algorithm and the Search Problem52:00 – The Need for Quantum Error Correction and the Problem of Decoherence in Quantum Systems58:00 – Superconducting Qubits: The Technology Behind Quantum Hardware1:05:00 – The Challenges of Packing More Qubits: Coherence Time and Integration of Quantum Systems1:12:00 – Temperature and Cooling Requirements for Superconducting Qubits1:20:00 – Advances in Quantum Error Correction and Strategies for Scaling Quantum Devices1:28:00 – Future Directions for Quantum Computing: Materials Science, Algorithms, and Hardware Innovations1:35:00 – Schrödinger's Cat: Exploring Quantum Superposition in a Philosophical Context1:45:00 – The Double-Slit Experiment: Quantum Interference and the Nature of Quantum Systems1:50:00 – The Future of Quantum Computing: Overcoming Challenges and Expanding Practical Applications2:00:00 – Concluding Thoughts on the Impact of Quantum Mechanics on Modern Technology and the Future of ComputingHosts:Amit Prakash: Co-founder and CTO at ThoughtSpot, former engineer at Google and Microsoft, and expert in distributed systems and machine learning.Guest:Professor Debdeep Jena: David E. Burr Professor of Engineering at Cornell University, expert in semiconductors, superconductors, and quantum devices.Follow the Hosts and Guest:Amit Prakash: LinkedIn | XDebdeep Jena: LinkedInHave questions or thoughts on AI? Drop us a mail at effortlesspodcasthq@gmail.comDon't forget to like, comment, and subscribe for more insightful conversations on the future of technology and innovation!

Prepare to see banking in a new light! Cindi Howson and Macquarie Bank's data trailblazer, Ashwin Sinha (Chief Data Officer), go deep into the AI revolution transforming financial services. Discover how one of Australia's most dynamic financial institutions, Macquarie Bank, is wielding the disruptive force of generative AI, not just for efficiency, but to combat high-stakes threats like fraud. Plus, discover the remarkable evolution of the data analyst from report-generator to AI-powered strategic powerhouse!Key Moments: Drivers of Digital Transformation (04:36): Ashwin outlines the key factors driving a digital transformation and early cloud adoption, emphasizing customer obsession, improving turnaround times, and ensuring technology reliability.  Leveraging Dual Cloud Providers (12:25): Ashwin discusses Macquarie Bank's use of AWS for infrastructure and core applications and Google Cloud (GCP) for its digital and data stack, including AI capabilities.  The Power of Gen AI in Analytics (14:16): Ashwin explores the role of generative AI in enhancing productivity for data analysts, particularly through prompt engineering and tools like ThoughtSpot.  Empowering Analysts Through Evolution (16:56): Ashwin details Macquarie Bank's successful strategy for evolving the data analyst role by proactively introducing self-service analytics, emphasizing upskilling, and enabling analysts to concentrate on higher-impact activitiesCombating Data Risk and Fraud Prevention (26:04): Ashwin discusses the increasing threat of scams and fraud and details Macquarie's two-pronged approach: educating customers and employing AI and machine learning to detect and prevent fraudulent activities.  Importance of Prompt Engineering (32:57): Ashwin stresses the significance of prompt engineering as a general-purpose technology that can drive productivity across various business functions, not just within technical roles.  Key Quotes:"There is always a big backlog in most organizations, which you cannot get done just because you do not have enough capacity. You cannot prioritize them. You cannot execute fast enough. And so, what prompt engineering and GenAI broadly does is take away the low-value tasks that you could just use AI and machine learning to do for you." - Ashwin Sinha"Prompt engineering—even though it has 'engineering' in it— I see that as a general-purpose technology. It's a bit like we've just got access to a super powerful search with a lot more analytical and reasoning capability. That's how I think of the usage of any of the foundational or large language models for, you know, the general population who are not in engineering or technical roles. Whether they're in business roles, sales and distribution, finance, marketing, or any of those functions, the use of prompt engineering just enables the next level of productivity for them. - Ashwin SinhaMentionsPrompt Engineering in the Age of AIAI Agent GovernanceThoughtSpot Spotter: Your AI AnalystScuba Diving and the History of the Liberty Shipwreck in BaliThe Importance of Child Education in IndiaGuest Bio Ashwin Sinha is the Chief Data Officer and Executive Director at Macquarie Bank, where he oversees the strategy and execution of Data and AI. Before joining Macquarie in 2019, Ashwin was a Partner at KPMG, leading the Data business. He has also held various global software engineering, start-up, and consulting roles over the past 22 years, focusing on data and digital transformations. Outside work, Ashwin is passionate about child education and macroeconomics Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Episode 14 | The Effortless PodcastIn this episode of The Effortless Podcast, hosts Dheeraj Pandey and Amit Prakash sit down with Harpinder "Happy" Singh, AI/ML Engineer at DevRev, to explore the future of AI and machine learning in business automation. Happy, who joined DevRev in 2021, shares his journey from computer science to AI, discussing how DevRev is leveraging cutting-edge technologies like large language models (LLMs) and function calling to streamline enterprise workflows.Happy breaks down the key concepts of AI-driven workflows, the debate between federated and integrated systems, and the growing importance of Python in AI. He also shares insights from the CodeAct paper, which proposes using Python code execution for more efficient and flexible LLMs. The conversation highlights the transformative potential of AI in enterprise automation and how it is reshaping industries.They also cover:The evolution of AI at DevRev: From workflows to AI-driven automationThe role of Python in executing complex tasks for LLMsUnderstanding the user-agent-environment model in AI systemsHow federated vs. integrated systems impact AI performanceThe future of AI in enterprise automation and DevRev's innovationsHappy's decision to stay in India and the growing tech ecosystem in IndiaThis episode provides valuable insights into how AI is transforming business operations, making complex workflows more efficient and accessible. Whether you're an AI enthusiast, a developer, or a business leader, this conversation is a must-listen for anyone interested in the next wave of AI-driven innovation.Key Topics & Timestamps:00:00 – Introduction to Harpinder "Happy" Singh & His Journey into AI03:00 – Happy's Early Background: From Shahjahanpur to BITS Pilani06:30 – Transition to AI at DevRev09:30 – Bangalore Life and Growing with DevRev13:00 – AI in India vs. the US18:00 – Federated vs. Integrated Systems: Which Approach Works Best for AI?25:00 – The Role of Python in AI32:00 – User, Agent, and Environment Model in AI39:30 – The CodeAct Paper: Replacing Tool Calls with Python Code Execution47:00 – AI in Enterprise Automation: How DevRev Uses AI to Streamline Workflows54:00 – Looking Ahead at DevRev's AI Innovations1:00:00 – Final Reflections: The Future of AI in Business and AutomationHosts:Dheeraj Pandey: Co-founder and CEO at DevRev, formerly CEO of Nutanix, a tech visionary passionate about AI and systems thinking.Amit Prakash: Co-founder and CTO at ThoughtSpot, former engineer at Google and Microsoft, and expert in distributed systems and machine learning.Guest:Harpinder Jot Singh: AI/ML Engineer at DevRev, working on the cutting edge of large language models (LLMs), AI-driven workflows, and integrating AI into enterprise systems.Follow the Host and the Guest:Dheeraj Pandey: LinkedIn | XAmit Prakash: LinkedIn | XHarpinder Singh: LinkedInHave questions or thoughts on AI? Drop us a mail at effortlesspodcasthq@gmail.comDon't forget to like, comment, and subscribe for more insights into the future of AI, business automation, and enterprise technology!

Ever wondered how data powers the magic behind your favorite theme park experiences? Join Cindi Howson and Gavin Hupp, VP of Technology, Enterprise Architecture, Data and Martech, E-commerce and Analytics at United Parks and Resorts, as they explore the complex data ecosystem of a theme park, from e-commerce and guest experience to AI's role in shaping the future of entertainment.Key Moments: Theme Park Business Model (03:12): Theme parks are described as a mix of multiple businesses, including e-commerce for ticket sales, animal experiences, entertainment venues, culinary and restaurant services, and retail operations. This combination creates a complex ecosystem, similar to city planning, within a single physical location.  Data Ecosystem Challenges (03:37): Gavin highlights the challenge of managing data within theme parks due to the variety of business areas. Each area generates unique data, leading to disparate and sometimes siloed data sets across different business applications.  AI as an Innovation Driver (11:24): AI is viewed as a key driver of innovation within the theme park industry, capable of creating new products and services, such as augmented reality experiences, and enhancing personalization at scale.  AI for Process Optimization (11:24): Beyond guest-facing innovation, AI is also seen as a tool to optimize business processes, streamline operations, reduce costs, and identify opportunities for revenue growth through personalization and increased efficiency.  Data-Driven Decision-Making (17:30): United Parks and Resorts emphasizes the importance of guest feedback, collected through surveys and other means, and uses it to inform decision-making and guide the company's overall strategy.  Agile Development Approach (28:50): Gavin explains how the company employs agile development principles, using "skateboards" as a metaphor for quickly delivering initial solutions and value while simultaneously iterating and building more comprehensive and scalable solutions ("scooters" and "factories").Key Quotes:"To become more data-driven, you have to break down silos. This requires making people aware of the silos, the challenges they create, and framing it as a data quality discussion. Getting business leaders to care about data quality isn't easy; they want end results and impact." - Gavin Hupp"There's product and service innovation, and business process innovation, where AI optimizes and streamlines operations, decreasing costs and increasing revenue through personalization." - Gavin Hupp“There's an agile concept, a principle where, at the end of the day, you need to get movement, you need to get going. And so you can use a skateboard to go from point A to point B.” - Gavin HuppMentionsGavin Hupp, Forbes ArticlePenguin Trek: Seaworld Roller CoasterConway's Law4 Values of Agile DevelopmentScrumDiet & Eating Habits of Killer WhalesGuest Bio Gavin Hupp is currently the VP of Technology: Enterprise Architecture & Data, Martech, e-Commerce & Analytics at SeaWorld Parks & Entertainment (United Parks & Resorts). In addition, he is also a member of the Quartz CIO & CISO Advisory Board. Gavin's expertise is helping shape the agenda to ensure it's packed with actionable strategies and forward-thinking insights. Gavin Hupp has a strong background in technology, data, and marketing, with experience in various leadership roles in companies such as PetSmart, Denny's, and Transdev North America. Gavin has a strong educational background, with degrees from the Massachusetts Institute of Technology, Stanford University, and Western International University. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Episode 13 | The Effortless PodcastIn this episode of The Effortless Podcast, hosts Dheeraj Pandey and Amit Prakash sit down with Dr. Sonika Johri, Founder and CEO of Coherent Computing, to dive deep into the revolutionary world of quantum computing. Dr. Johri, a physicist with a PhD in condensed matter physics from Princeton University, takes us on her journey from engineering physics at IIT Delhi to becoming a leading figure in the quantum industry, having worked at Intel and IonQ.Sonika explains the core concepts of quantum computing—superposition, entanglement, and the quantum state space—and how they fundamentally change how we approach complex problems in fields like chemistry, material science, and AI. She discusses the future potential of quantum technologies, including the exciting prospects for Quantum AI and the shift in programming paradigms as we move from low-level machine code to higher-level abstractions.They also cover: The evolution of quantum hardware: From small qubits to scaling quantum systemsWhat makes quantum computing different from classical computingThe intersection of quantum computing and artificial intelligence Sonika's mission to democratize quantum through Coherent ComputingThe current state of quantum software and the tools that will shape the futureThis episode offers insights into quantum computing, AI, and how these emerging technologies will reshape the future of computing. Whether you're a tech enthusiast, developer, or entrepreneur, this conversation is a must-listen for anyone curious about the next frontier in technology.Key Topics & Timestamps:[00:00] – Introduction to Dr. Sonika Johri & Her Journey into Quantum Tech[03:00] – Sonika's early influences: Einstein and IIT Delhi[06:30] – Understanding Condensed Matter Physics[12:00] – Quantum Computing vs Classical Computing[20:00] – How Quantum Can Solve Complex Problems (Chemistry, Optimization, AI)[28:00] – Quantum Hardware: The Role of Qubits and Their Physical Realization[35:00] – Programming Quantum Computers: From Low-Level Gates to High-Level Abstractions[43:00] – Building Quantum Applications: Real-World Use Cases from IonQ and Coherent Computing[52:00] – The Future of Quantum AI: Machine Learning and Quantum Reasoning[1:00:00] – Quantum's Impact on Cryptography and Data Security[1:05:00] – The Mission of Coherent Computing: Making Quantum Accessible[1:12:00] – Looking Ahead: Future Episodes on Quantum Computing and AI[1:20:00] – Wrap-Up and Final ThoughtsHosts:Dheeraj Pandey: Co-founder and CEO at DevRev, formerly CEO of Nutanix, a tech visionary passionate about AI and systems thinking.Amit Prakash: Co-founder and CTO at ThoughtSpot, former engineer at Google and Microsoft, and expert in distributed systems and machine learning.Guest:Dr. Sonika Johri: Founder and CEO of Coherent Computing, a quantum software startup aiming to make quantum models accessible through developer-friendly tools. Formerly at Intel and IonQ, Sonika brings her experience in building quantum software and applications for industries like finance, chemistry, and optimization.Follow the Host and the Guest:Dheeraj Pandey: LinkedIn | XAmit Prakash: LinkedIn | XDr. Sonika Johri: LinkedIn | XHave questions or thoughts on quantum computing? Drop us a mail at EffortlessPodcastHQ@gmail.comDon't forget to like, comment, and subscribe for more deep dives into the future of technology, AI, and quantum computing!

In this episode of Tech Talks Daily, I sit down with James Smith, who leads ThoughtSpot's business across EMEA. With over 15 years in the analytics space, James shares how AI is shifting the way organizations use data, and why that change is not just about technology, but also about mindset and culture. James explains how ThoughtSpot is helping businesses move toward a more autonomous model, where AI-powered tools handle repetitive queries and free up analysts for more strategic work. He shares how ThoughtSpot's “Spotter” tool enables business users to ask and answer their own data questions, helping to reduce the bottlenecks that many central data teams face. But this isn't about removing people from the process. It's about enabling better collaboration between AI and human decision-makers, where transparency and context guide smarter actions. We also talk about ThoughtSpot's internal motto, “2% done,” and how that mentality drives continuous innovation. In a world that's changing rapidly, it's a reminder that staying curious, challenging assumptions, and building from first principles can unlock new levels of performance. As demand for AI-powered insights increases, James highlights the growing importance of strong data foundations. It's not enough to just add AI on top. Organizations need to invest in data quality, governance, and flexible platforms that support users at every level of maturity. From enabling better business decisions to giving non-technical users easier access to insights, James offers a grounded view of what AI can really deliver today. If you're working to build a data-driven culture, or looking to put more power in the hands of your teams, this conversation offers practical ideas to guide that transformation. How are you preparing your business for this shift in analytics? Let me know.

How does SharkNinja use data to fuel its rapid growth and product innovation? Join Cindi Howson and Elpida Ormanidou, VP of Analytics and Insights at SharkNinja, as they dissect SharkNinja's data-driven culture, Elpida's journey in the data space across CPG and retail, and her insights on AI in the workplace. Key Moments: Data-Driven Culture (03:36): SharkNinja strongly emphasizes data in its culture, utilizing it to inform decision-making processes. The company is committed to using customer feedback gathered through data to drive the development and refinement of its product offerings.  CEO's Data Focus for Customer-centric Innovation (05:43): SharkNinja's CEO demonstrates a notable dedication to data by actively engaging with it. This involvement includes closely reviewing customer feedback and using data insights to guide product discussions and challenge teams to improve.  Data Ethics and Privacy (09:17): SharkNinja places a high priority on data ethics and privacy, emphasizing the importance of earning customer trust. Elpida shares how the company is committed to using customer data responsibly and has implemented strong controls to protect privacy.  AI and the Future of Work (20:31): Elpida discusses the transformative impact of AI on the future of work, characterizing it as a revolution. She emphasizes the importance of proactively addressing the changes by reskilling and upskilling the workforce to adapt to new roles and technologies.  Key Quotes:"Value gets created at the time of consumption. We create value for the business when data gets consumed, not when it gets connected, not when it gets processed, not when it gets synthesized, only when it's being used to drive decisions that create value for the company." - Elpida Ormanidou"Think of a company as a chain, where everything is interlinked to level up. Today's struggle is that while we have good AI applications, it's an art to connect them to create the next level of experience, particularly for customers. What works in a lab doesn't work the same in real life; there are so many different factors.” -Elpida Ormanidou"Where others have fear, I have hope and optimism that the more we automate and we remove mundane tasks from our day-to-day life or even our work life, the more we would be able to use our beautiful brains to reimagine and create new things that as a race will drive us forward for another 3,000 years." -Elpida OrmanidouMentions:SharkNinja Coolar: FrostVault TechnologySharkNinja HydrovacSurat: 100 Resilient Cities of the WorldMadam Curie: A Biography, By Eve CurieGuest Bio Elpida Ormanidou Elpida Ormanidou is the Vice President of Analytics & Insights at SharkNinja. She has extensive experience in data and analytics, having worked at companies like Walmart and Starbucks. At SharkNinja, she leads the data strategy and is passionate about fostering a data-driven culture. Elpida is a strong advocate for ethical data practices and responsible AI implementation. She is a recognized voice in the data and analytics community, frequently speaking at industry events and mentoring young professionals. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

How is Sephora using data to create personalized experiences that customers love? Join Cindi Howson and Manbir Paul, VP of Engineering, Data Insights & MarTech at Sephora, as they explore the role of data and AI in understanding customer needs, predicting preferences, and delivering impactful moments. Key Moments: Micro-Moments that Matter (3:30): Sephora leverages data to create impactful moments for customers, like sending a timely reminder to a traveler about their moisturizer.Modern Data & AI Stack (5:00): Manbir discusses Sephora's best-of-breed data and AI stack, spanning cloud data platform, BI solution, cataloging, and machine learning democratization.The Power of the Semantic Layer (7:30): The semantic layer is crucial for enabling meaningful data discovery and governance. Sephora's investment in ThoughtSpot was driven by the need to enhance their semantic layer and drive intelligence in their BI space.Collaborative Data Governance (10:00): Sephora fosters a collaborative approach to data governance, with data stewards playing a key role. They identify individuals who are subject matter experts in their areas and are passionate about data to help drive governance and enrichment.Unlearning and Relearning (16:30): The challenge of keeping up with the evolving data landscape requires unlearning old practices and embracing new ones. Manbir highlights the importance of giving individuals the opportunity to look at the changing landscape from a new lens and empowering them to drive transformation.The Importance of Continuous Learning (20:30): Manbir acknowledges the challenge of balancing learning with delivering results, but stresses the importance of continuous learning in a rapidly evolving field. She notes that individuals are often willing to go above and beyond if there is a learning opportunity.Building High-Performing Teams (22:30): Manbir discusses the nuances of creating a high-performing, nimble team that can adapt to change and drive innovation. He mentions the importance of understanding the nuances that are important in transforming a team into a high-performing one.Key Quotes:"The intimate details, we always talk about getting closer to our clients. We want to experience our clients. I feel the intimate details that data gives you, getting your clients so close to you, is a very different lens to look at data from. It is a gift of feedback that the clients give to you or your consumers give to you in terms of data.”  - Manbir Paul"Democratizing these technologies is key to our tech stack. We have a multi-cloud strategy to capture the best tools. Tools, plus our BI investment, help us. ThoughtSpot was chosen for meaningful data insights, reaching clients where they interact with data and enhancing our BI intelligence." - Manbir Paul"We want to make sure that there are tools that help us enable scaled implementations in driving personalization, and that's where our Databricks platform enables us doing that."  - Manbir PaulMentionsFarmacy: Honey Halo Ultra-Hydrating Ceramide MoisturizerThe Geek Way by Andrew McAfeeGuest Bio Manbir PaulManbir Paul is VP of Engineering, Data Insights & MarTech at Sephora. Prior to this, he served as global head of ML engineering at Levi Strauss & Co.As a proactive, results-driven technology leader specializing in the retail industry, Manbir's expertise lies not just in understanding the industry's complexities, but also in harnessing the transformative power of Data and AI. With a passion deeply rooted in technological innovation, his most recent endeavors have involved leading in the realms of Data and AI to develop, scale, and implement solutions that amplify business growth Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Join host Cindi Howson as she dives into the critical topic of diversity and inclusion in the data and AI space with Roisin McCarthy, founder of Women in Data UK, and Robin Sutara, Field CDO at Databricks. They discuss the challenges of recruiting and retaining diverse talent, the importance of male allies, and the role of AI in creating a more inclusive workforceKey Moments: The Power of Community: Building a Network for Women in Data: Roisin McCarthy shares the story behind founding Women in Data, inspired by her mother's advice to "put up or shut up." She highlights the organization's growth to 80,000 members in 120 countries and emphasizes the importance of male allies in achieving gender representation. (2:41)  From Apache Helicopters to Chief Data Officer: A Non-Traditional Journey: Robin Sutara shares her unique career path, starting with repairing Apache helicopters in the US Army and eventually becoming a CDO. She discusses the challenges she faced as a woman in tech and the importance of fixing systemic issues to achieve equity. (6:19)  The Talent Crunch: Addressing the Data Skills Gap: The conversation shifts to the shortage of qualified individuals in data and technology. Roisin McCarthy highlights the need for organizations to rethink their recruitment strategies and remove unnecessary barriers to entry. (11:31)  Closing the Pay Gap: A Shared Responsibility: Roisin and Robin discuss the persistent pay gap in the data industry and the risk of it widening further. They emphasize the importance of both individual and systemic action to achieve pay parity. (20:46)  Generative AI: A Double-Edged Sword for Recruiting: Roisin McCarthy shares a cautionary tale about the potential for bias in AI-generated job descriptions. She stresses the importance of human oversight and highlights Women in Data's work to develop technology that removes bias from job descriptions. (46:30)  The Future of Data and AI: Embracing Innovation and Inclusion: Robin Sutara expresses excitement about the potential of AI to simplify complex tasks and unlock the power of data. She emphasizes the importance of leveraging technology to innovate and create a more equitable and inclusive data workforce. (49:22)Key Quotes:"We simply do not have enough people coming into the industry. Regardless of gender, let's take that away. We do not have enough qualified individuals coming into the workplace in data and technology." - Roisin McCarthy   "We can't affect the change that this mission is so focused on reaching if we don't have everybody at the table." - Roisin McCarthy  "Hire talent that's not currently in the ecosystem, bring in people with a different perspective or a different experience or a different capability. You can teach them technology, right?" - Robin Sutara  "If I start 20% behind my male cohorts, doesn't matter how much you reward on meritocracy, I will never catch up." - Robin Sutara  "GenAI tech is there for so many things as to Robin's point to really take some of the heavy lifting out. But when we're looking to build inclusive teams, diverse, inclusive teams, I think that we just need a bit of a sense check and ensuring that we've got the human in the loop." - Roisin McCarthy  MentionsWomen in Data PodcastDatabricks BlogGuest Bios Roisin McCarthyAs a result of her own efforts, over two thousand people have moved into more satisfying roles and dozens of teams put together. Furthermore, she has managed a successful team of professional recruiters which, over the years, has placed thousands more. Today, she runs the successful recruitment firm, Datatech Analytics, and is the co-founder of the ground-breaking initiative, Women in Data UK. Over the past 19 years, McCarthy has been responsible for building some of the UK's most cutting-edge data teams and has facilitated some of the most influential and successful careers in this sector, building relationships, influence and firm friendships along the way. McCarthy is seen as a thought-leader and an authority on careers, team development and talent acquisition in the field. Her unrivalled network of contacts, commitment to the data and analytics community and her unwavering passion for building strong, skilled teams is what makes her so unique.Robin SutaraFrom repairing Apache helicopters near the Korean DMZ to the corporate battlefield, Robin has demonstrated success in navigating the high stress, and sometimes combative, complexities of data-led transformations. She has consulted with hundreds of organisations 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 chair of Women in Data North America Committee. She was also recognized in 2023 as a Top 20 Women in Data and Tech, as well as DataIQ 100 Most Influential.  Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

In this episode, Amit and Dheeraj dive deep into the world of AI reasoning models with Alex, an AI researcher involved in OpenThinker and OpenThoughts. They explore two recent groundbreaking papers—SkyT1 and S1 (Simple Test Time Scaling)—that showcase new insights into how large language models (LLMs) develop reasoning capabilities.From structured reasoning vs. content accuracy to fine-tuning efficiency and the role of active learning, this conversation highlights the shift from prompt engineering to structured supervised fine-tuning (SFT) and post-training techniques. The discussion also touches on open weights, open data, and open-source AI, revealing the evolving AI landscape and its impact on startups, research, and beyond.Key Topics & Chapter Markers[00:00] Introduction – Why reasoning models matter & today's agenda[05:15] Breaking Down SkyT1 – Structure vs. Content in reasoning[15:45] Open weights, open data, and open-source AI[22:30] Fine-tuning vs. RL – When do you need reinforcement learning?[30:10] S1 and the power of test-time scaling[40:25] Budget forcing – Making AI "think" more efficiently[50:50] RAG vs. SFT – What should startups use?[01:05:30] Active learning – AI asking the right questions[01:15:00] Final thoughts – Where AI reasoning is heading nextResources & Links

Join host Cindi Howson alongside Chris Bruman, Chief Data and Analytics Officer at Dow, and Dan Futter, Chief Commercial Officer at Dow, as they explore how data-driven innovation is reshaping business and customer experiences. From the hub-and-spoke model for data management to the power of real-time insights, they discuss the role of data literacy, leadership, and AI-driven decision-making in driving success. Don't miss this conversation on the future of AI, data strategy, and innovation.Discover the innovations that inspire Chris Bruman and Dan Futter, how data has shaped their careers, and which tech leaders they admire most.Key Moments: Revolutionizing Data: The Hub-and-Spoke Model: The Dow team highlights the shift to digital, while Cindi Howson reflects on IT's evolution. They explain Dow's decentralized hub-and-spoke model, balancing governance with agility for faster insights, accuracy, and career growth. (9:04)Why Data & Business Literacy Matter: Our guests stress understanding business needs, defining clear roles within the hub-and-spoke model, and supporting skill development. This approach simplifies processes, builds confidence in analytics, and drives value for Dow. (18:02)The Integrated Data Hub: A Game-Changer: Dow's data hub slashes data science time from months to a day. Prioritizing quality over speed prevents tech debt, ensuring strong governance. Now the go-to source for innovation, it plays a crucial role in Dow's data strategy. (28:03)Balancing Competing Demands in Industry: Our knowledgeable leaders in the industry underscore the importance of prioritizing data projects for impact. Decentralization eases bottlenecks, but demand remains high. Dow now requires senior sponsorship to ensure measurable value and optimize resources. (36:06)Key Quotes:"Too often, we wait until the project's done to figure out how to get the value and who's going to sponsor it. We have to flip that around and secure senior sponsorship before we even start." – Chris Bruman"If I talk data mesh to my business clients, there's going to be a blank stare, right? So we use hub and spoke—it's more visual and makes a lot more sense. At the end of the day, it's really about decentralization." – Chris Bruman"Instead of just showing a data sheet or marketing collateral and making the customer hunt for insights, we now surface specific text, data, and even language customization—getting them straight to the front door, not just the right street." – Dan Futter"It's not just about finding data—it's about ensuring its integrity. Where is that data? How does it get created? Which processes generate it? How do we train people so that, from the start, it stays high-integrity?" – Dan FutterMentionsWhat is a data mesh?SpaceXIn Our Time PodcastWalking the Dog PodcastGuest Bios:Chris Bruman Chris Bruman is the Chief Data and Analytics Officer at Dow, a multinational company with operations in 31 countries that serves customers in a wide range of markets.Dan Futter Dan Futter is the Chief Commercial Officer for Dow. Through his leadership in Customer Experience and Marketing/Sales disciplines, Dow is on track to become the most customer-centric material science company in the world. He was the program lead for the design, development, and launch of the company's groundbreaking Dow.com e-commerce platform and is passionate about the role digital technology plays in transforming customer journeys. Dan serves on the Executive Committee and is Chair of the Medals Committee of the Society of Chemical Industry America. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Your host, Cindi Howson, and CTO of NVIDIA AI agentic software, Bartley Richardson, discuss the transformative potential of generative and agentic AI in business, focusing on customer service, HR, and workplace innovation. They explore real-world use cases, the challenges of managing diverse data sources, and the tools and technologies shaping the future of AI which lead to….Data Challenges: Cindi and Bartley discuss the complexities of managing structured, semi-structured, and unstructured data in the context of generative AI. They explore the challenges and opportunities presented by different data formats.Tools and Technologies: Bartley provides guidance for AI and tech leaders on evaluating and building AI agents, emphasizing the importance of listening to employee needs and selecting the right tools for specific use cases.Real-World Use Cases: The conversation digs into practical applications of agentic AI, with a focus on customer service and software development. Bartley highlights examples of how companies are using AI agents to improve efficiency and productivity.The Future of AI: The episode concludes with a look ahead at the future of AI, with Bartley sharing his optimism for the transformative potential of agentic AI and offering advice for data and AI leadersDiscover the creative facets that inspire Bartley and how data has been a driving force in his life since earning his PhD.Key Moments: Understand agentic AI: Bartley explains how agentic AI is one of the most exciting and transformative developments in the AI space, evolving from generative AI and LLMs (large language models) to create systems capable of taking actions on behalf of users. (2:20)  Use Case Summary - AI-Powered Agentic Workflows at NVIDIA: NVIDIA has embraced agentic AI workflows to enhance both employee efficiency and customer experience. A prime example is their implementation of Agent Morpheus, a system designed to streamline software delivery and security processes. (13:16)AI is the new HR: Bartley highlights how generative AI has been effectively applied in HR, particularly in employee handbooks and onboarding documents. HR documents, often buried in PDFs, contain a wealth of structured data, making them a rich source for AI applications. (15:26) Data ingestion within the future of data processing: Bartley hones in on the primary concern of how data is ingested and how structured queries are executed in ways that align with business needs. The technology is progressing rapidly, but refinement is still needed for impactful data usage. (37:43)Key Quotes:"Generative AI and agentic AI are really exciting because we're finally at the point where the experience of using AI meets our expectations. It's no longer just a label or something that might be statistics; it's something meaningful in our day-to-day life." -Bartley Richardson"If I had to pick the time to be alive and in this industry, it would be right now. The amount of progress just leaps every day, with new breakthroughs, announcements, or capabilities that didn't exist the day before." -Bartley Richardson"AI does not absolve you of critical thinking and this data literacy thing. If anything, it amplifies the need for this." -Bartley RichardsonMentions:How to Create a Data and AI Literate Company with Bridgestone and The Data LodgeErsilia Open Source AICEO Gemma Turon Examines Ersilia's Impact on Biomedical ResearchThe Happiness Hypothesis: By Jonathan HaidtSetting the Table: By Danny MeyerGuest Bio:Bartley Richardson is CTO of NVIDIA AI agentic software and Director of Engineering for cybersecurity AI development and product engagement, including accelerated computing and generative AI. Previously, Bartley was a technical lead on multiple DARPA research projects. He was also the principal investigator of an Internet of Things research project which focused on applying machine and deep learning techniques to large amounts of IoT data to provide intelligence value relating to form function, and pattern-of-life. His primary research areas involve NLP and sequence-based methods applied to cyber network datasets as well as cross-domain applications of machine and deep learning solutions to tackle the growing number of cybersecurity threats. He holds a PhD in Computer Science and Engineering with a focus on AI.  Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

This is the second part of episode 10 of Effortless Podcast, hosts Dheeraj Pandey and Amit Prakash sit down with Alex Dimakis, a renowned AI researcher and professor, to discuss one of the biggest breakthroughs in open AI models—DeepSeek R1. They explore how DeepSeek's innovations in reasoning, reinforcement learning, and efficiency optimizations are reshaping the AI landscape.The conversation covers the shift from large, proprietary AI models to open-source alternatives, the role of post-training fine-tuning, and how reinforcement learning (GRPO) enables reasoning capabilities in LLMs. They also dive into KV caching, mixture of experts, multi-token prediction, and what this means for NVIDIA, hardware players, and AI startups.Key Topics & Timestamps:[00:00] - Introduction & Why DeepSeek Matters[01:30] - DeepSeek R1: Open-Source AI Disrupting the Industry[03:00] - Has China Become an AI Innovator?[07:30] - Open Weights vs. Open Data: What Really Matters?[10:00] - KV Caching, Mixture of Experts & Model Optimizations[21:00] - How Reinforcement Learning (GRPO) Enables Reasoning[32:00] - Why OpenAI is Keeping Its Reasoning Traces Hidden[45:00] - The Impact of AI on NVIDIA & Hardware Demand[1:02:00] - AGI: Language Models vs. Multimodal AI[1:15:00] - The Future of AI: Fine-Tuning, Open-Source & Specialized ModelsHosts:Dheeraj Pandey: Co-founder and CEO at DevRev, formerly Co-founder and CEO of Nutanix. A tech visionary with a deep interest in AI and systems thinking.Amit Prakash: Co-founder and CTO at ThoughtSpot, formerly at Google AdSense and Bing, with extensive expertise in analytics and large-scale systems.Guest:Alex Dimakis: Professor at UC Berkeley and co-founder of Bespoke Labs, Alex has made significant contributions to deep learning, machine learning infrastructure, and the development of AI reasoning frameworks.Follow the Hosts and the Guest:Dheeraj Pandey:LinkedIn - https://www.linkedin.com/in/dpandeyTwitter - https://x.com/dheerajAmit Prakash:LinkedIn - https://www.linkedin.com/in/amit-prak...Twitter - https://x.com/amitp42Alex Dimakis:LinkedIn - https://www.linkedin.com/in/alex-dima...Twitter - https://x.com/AlexGDimakisShare Your Thoughts:Have questions, comments, or ideas for future episodes? Email us at EffortlessPodcastHQ@gmail.comDon't forget to Like, Comment, and Subscribe for more in-depth discussions on AI, technology, and innovation!

Not all remakes are created equal. The Dune of 2021 is not the Dune of 1984. Maybe we should say not all “retellings” are created equal. Because both movies are based on the 1965 book. But film critic Roger Ebert scathingly called the first one "an incomprehensible, ugly, unstructured, pointless excursion." Ouch.The second one - the one we're talking about today - is a different story. It's been called “aesthetically perfect,” and the “faithful retelling of a complicated story.” And it won a handful of Academy Awards.So we're talking about what makes a great retelling and how to use those lessons in your marketing content. With the help of our special guest, VP of Corporate Marketing at Cribl, Ryan Mattison, we also talk about delivering a singular experience, doing partnership-driven marketing, and much more.About our guest, Ryan MattisonRyan Mattison is VP of Corporate Marketing at Cribl, where he leads the corporate marketing team, and looks after brand, creative, communications, and events. Prior to joining Cribl in July 2024, Ryan served as VP of Brand & Communications at ThoughtSpot. There, he led brand, creative and design, communications, PR and analyst relations, customer marketing, employee advocacy, investor relations and content marketing functions. Since joining as an individual contributor in 2017, he built the team from the ground up to deliver world class campaigns that cut through a highly competitive landscape, improve ThoughtSpot's reputation and awareness among key audiences, and generated pipeline that led to revenue.What B2B Companies Can Learn From Dune:Deliver a singular experience. And deliver it in the format that optimizes the experience. Like how Dune was presented on iMax. The large format made the experience of the sounds and the vast landscape of the film a more immersive and real experience. Ryan says, ”Deliver an authentic experience, and people will pay for it. That idea of being intentional and connecting it with a really authentic value prop, that for Dune was super real. That large screen format with the premium sound is going to really, really differentiate your experience with the movie.”Partner up.  Find other companies to cross promote content with. Dune did this with the video game Fortnite, allowing players to buy a “skin” so their avatar looks like a Dune character. Ryan says, “Dune also did probably one of the best jobs in recent years of doing partnership-driven marketing. Theater going is skewing older and older, but something like Fortnite,  has a much younger skewed audience. They were like, ‘We need to target a younger demographic because if we can get kids or young folks to ask their parents to go see Dune and their parents were already kind of thinking about going, now it's a family thing. And you're not selling one ticket or two tickets. You're selling four, five, six tickets at one time.” Ryan adds, “ How do you find the right partner that's not just the easy partner, but the partner where you're able to find or open up new channels, new demographics, new audiences in a way that feels like there's intentional value being delivered?”Create a remake. Or as Ian says, “Try it again for the first time.” Because the movie Dune that came out in 2021 was a remake of the 1984 movie. Or really it was a retelling of the same story with little regard for the first version. And because of the generational difference between viewers, Ian says, “This [2021 version] was the first interaction with Dune for, I would bet, a massive percentage of the audience that saw this. They never read the books. They didn't watch the first movie.  But for people like your mom, it's like, ‘Hey, try it again for the first time.' Like, ‘Yeah, I know you saw the old movie. This is nothing like that. Just try it again.” It reframes everyone's idea of the original story, and gives a totally new experience of it. So make a remake but keep it fresh.Throw the party. Ian says, “ If you make something worth talking about,  throw the party so that the people can talk about it.” That's what this podcast is all about, getting people to talk about Remarkable content and spreading knowledge of it through word of mouth. He adds, “ If you make content also make the event, make a premiere, make a big deal out of it.” Also because you deserve to celebrate good content and the hard work that went into it.Quotes*” There is no time when somebody is engaging with any facet of your brand or your business in which you shouldn't be representing the perception that you want to drive.  Every one of those is an opportunity to tell your story in a potentially different channel, different way.”*”Deliver an authentic experience, and people will pay for it. That idea of like, being intentional, and connecting it with a really authentic value prop, that for Dune, was super real. That large screen format with the premium sound is going to really, really differentiate your experience with the movie.”Time Stamps[0:55] Meet Ryan Mattison, VP of Corporate Marketing at Cribl[0:56] B2B Marketing Lessons from Dune[4:11] Marketing at Cribl[7:16] The Storytelling Power of Corporate Marketing[9:18] Dune: From Book to Blockbuster[12:09] Denis Villeneuve's Vision for Dune[15:28] Marketing Strategies Behind Dune's Success[22:56] Partnership-Driven Marketing Insights[27:24] The Challenge of Marketing B2B Products[27:46] Lessons from the Dune Movie Marketing[28:57] The Role of Mega Stars in Movie Success[31:23] The Importance of Authentic Marketing[35:32] Building Anticipation and Word of Mouth[44:20] The Power of the CEO's Voice[48:52] Upcoming Events and Final ThoughtsLinksConnect with Ryan on LinkedInLearn more about CriblAbout Remarkable!Remarkable! is created by the team at Caspian Studios, the premier B2B Podcast-as-a-Service company. Caspian creates both nonfiction and fiction series for B2B companies. If you want a fiction series check out our new offering - The Business Thriller - Hollywood style storytelling for B2B. Learn more at CaspianStudios.com. In today's episode, you heard from Ian Faison (CEO of Caspian Studios) and Meredith Gooderham (Senior Producer). Remarkable was produced this week by Meredith Gooderham, mixed by Scott Goodrich, and our theme song is “Solomon” by FALAK. Create something remarkable. Rise above the noise.

In this episode of the Effortless Podcast, hosts Dheeraj Pandey and Amit Prakash sit down with Alex Dimakis, a renowned AI researcher and professor at UC Berkeley. With a background in deep learning, graphical models, and foundational AI frameworks, Alex provides unparalleled insights into the evolving landscape of AI.The discussion delves into the detailing of foundation models, modular AI architectures, fine-tuning, and the role of synthetic data in post-training. They also explore practical applications, challenges in creating reasoning frameworks, and the future of AI specialization and generalization.As Alex puts it, "To deep seek or not, that's the $1 trillion question." Tune in to hear his take on how companies can bridge the gap between large generalist models and smaller specialized agents to achieve meaningful AI outcomes.Key Topics and Chapter Markers:Introduction to Alex Dimakis & His Journey [0:00]From Foundation Models to Modular AI Systems [6:00]Fine-Tuning vs. Prompting: Understanding Post-Training [15:00]Synthetic Data in AI Development: Challenges and Solutions [25:00]The Role of Reasoning and Chain of Thought in AI [45:00]AI's Future: Specialized Models vs. General Systems [1:05:00]Alex's Reflections on AI Research and Innovation [1:20:00]Hosts:Dheeraj Pandey: Co-founder and CEO at DevRev, formerly Co-founder and CEO of Nutanix. A tech visionary with a deep interest in AI and systems thinking.Amit Prakash: Co-founder and CTO at ThoughtSpot, formerly at Google AdSense and Bing, with extensive expertise in analytics and large-scale systems.Guest:Alex Dimakis: Professor at UC Berkeley and co-founder of Bespoke Labs, Alex has made significant contributions to deep learning, machine learning infrastructure, and the development of AI reasoning frameworks.Follow the Hosts and the Guest:Dheeraj Pandey:LinkedIn: Dheeraj PandeyTwitter: @dheeraj Amit Prakash:LinkedIn: Amit PrakashTwitter: @amitp42 Alex Shtoyanov:LinkedIn: Alex DimakisTwitter: @AlexGDimakisShare Your Thoughts:Have questions, comments, or ideas for future episodes? Email us at EffortlessPodcastHQ@gmail.comDon't forget to Like, Comment, and Subscribe for more in-depth discussions on AI, technology, and innovation!

In this season premiere of The Data Chief podcast, your host Cindi Howson sits down with three industry visionaries to explore the trends, predictions, and must-take actions for data leaders in 2025. Get ready for a deep dive into: The generative AI revolution with Matt Turck, Partner at FirstMark CapitalThe future of data science and genAI with Steve Nouri, Founder of GenAI Works and AI for DiversityData Engineering in the Age of AI with Joe Reis, author of "Fundamentals of Data Engineering" and the upcoming "Mixed Model Arts."Plus: Hear their fun predictions for everything from sports to space travel!Key Moments:The generative AI revolution: Matt Turck, Partner at FirstMark Capital shares his insights on the evolving AI landscape, the rise of unstructured data, and why now is the time for enterprises to embrace AI. (1:40) The Future of Data Science: Steve Nouri, Founder of GenAI Works (an 8-million-strong community!) and AI for Diversity, discusses the impact of GenAI on data science roles, the ethical considerations of AI, and exciting trends like embodied AI and agentic AI. (29:36) Data Engineering in the Age of AI: Joe Reis, author of "Fundamentals of Data Engineering" and the upcoming "Mixed Model Arts," provides his expert perspective on the importance of data modeling, the need for upskilling in data teams, and the potential for a universal semantic layer. (1:00:00) Key Quotes:“I would predict that there's going to be a number of big acquisitions in our general space in 2025. This whole tension between the public markets doing very well, especially in tech, but the private markets still recovering - I think lends itself well to a wave of consolidation.”  - Matt Turck“Anything that requires democratization, I'm a big fan of. And certainly, the ability to query natural language databases and all things, making that available to everyone is a very powerful idea. You guys at ThoughtSpot know this better than anyone.” - Matt Turck“We are seeing people doing less coding, more relying on their co-pilots. It's going to evolve to become more and more robust. So we will be relying more on AI to do the coding.” - Steve Nouri“Well, that's what, you know, the tagline is, AI will do everything for you. It'll even do your laundry, the jobs that we don't like. And so you're actually saying you see a future where that actually is not too far off.” - Steve Nouri“I think that there's definitely a FOMO and a bit of a prisoner's dilemma problem with adopting AI in the organization because they're getting a lot of pressure from the top down, especially to do AI. Understanding what that means to your organization should be table stakes.” - Joe Reis“Learning never stops, investment never stops. And the best investment you can make is always improving yourself, no matter what that looks like.” Joe ReisMentions:FirstMark MAD Landscape 2024The MAD Podcast with Matt TurckAI4DiversityGenAI.WorksFundamentals of Data EngineeringJoe Reis Substack Guest Bios:Matt Turck is a Partner at FirstMark, where he focuses primarily on early-stage enterprise investing in the US and Europe. Matt is particularly active in the data, machine learning and AI space. For the last 10+ years, he has been organizing Data Driven NYC, the largest data/AI community in the US, and publishing the MAD Landscape, an annual analysis of the data/AI industry. He also hosts the weekly MAD (ML, AI, Data) Podcast. He can be followed on X/Twitter at @mattturck.Steve Nouri is the CEO and Co-founder of GenAI Works, the largest AI community. He is a renowned AI leader and Australia's ICT Professional of the Year, has revolutionized AI perspectives while championing Responsible and inclusive AI, founding a global non-profit initiative.Joe Reis, a "recovering data scientist" with 20 years in the data industry, is the co-author of the best-selling O'Reilly book, "Fundamentals of Data Engineering." He's also the instructor for the wildly popular Data Engineering Professional Certificate on Coursera, in partnership with DeepLearning.ai and AWS.Joe's extensive experience encompasses data engineering, data architecture, machine learning, and more. He regularly keynotes major data conferences globally, advises and invests in innovative data product companies, writes at Practical Data Modeling and his personal blog, and hosts the popular data podcasts "The Monday Morning Data Chat" and "The Joe Reis Show." In his free time, Joe is dedicated to writing new books and articles, and thinking of ways to advance the data industry. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Our podcast network, The Curiosity Department, has a new show called Reconsidering. Though design and your work are a big part of who you are, you are so much more. Reconsidering is a show hosted by designers that aims to bring fresh eyes to the important things in life. Co-hosts Meredith Black, formerly at Pinterest and Figma, Bob Baxley, formerly at Apple and Thoughtspot, and Aarron Walter explore topics like how to build a fulfilling career, navigate big life changes like illness and death, and we talk with experts about the skills you need to cultivate to become the healthy, well adjusted adult we all want to be. You can learn more about Reconsidering and subscribe to the show at Reconsidering.org. But to give you a taste, we're sharing an episode here. Bob, Meredith, and Aarron talked to Brad Stuhlberg, who writes about excellence, resilience, performance, and well-being. His latest book, Mastering Change, is a New York Times best-seller and a great read as we enter the new year with big goals for ourselves. Brad's in depth research and lucid guidance made an impression on us, and we know it will do the same for you.  --- Like it or not, change is inevitable. Your career, relationships, body, health, mood are all in constant motion. We can fight it but it's unproductive and leads to suffering.  Our pal Brad Stulberg is back on the show to help us look at change differently. His new book Master of Change: How To Excel When Everything Is Changing - Including You is full of deeply researched wisdom from science and philosophy that will help you become more resilient and adaptable. About Brad Stulberg Brad Stulberg researches, writes, and coaches on health, well-being, and sustainable excellence. He is the bestselling author of The Practice of Groundedness and co-author of Peak Performance. Stulberg regularly contributes to the New York Times, and his work has been featured in the Wall Street Journal, Washington Post, Los Angeles Times, The New Yorker, Sports Illustrated, Outside Magazine, Forbes, and other outlets. He also serves as the co-host of The Growth Equation podcast and is on faculty at the University of Michigan's Graduate School of Public Health. In his coaching practice, he works with executives, entrepreneurs, physicians, and athletes on their mental skills and overall well-being. He lives in Asheville, North Carolina.

In this special guest episode of the Effortless Podcast, Amit Prakash sits down with Rajat Monga, the creator of TensorFlow and current Corporate Vice President of Engineering at Microsoft. With a career spanning Google Brain, founding Inference, and leading AI inferencing at Microsoft, Rajat offers a unique perspective on the evolution of AI. The conversation dives into TensorFlow's revolutionary impact, the challenges of building startups, the rise of PyTorch, the future of inferencing, and how transformative tools like GPT-4 and OpenAI's Gemini are reshaping the AI landscape.Key Topics and Chapter Markers:Introduction to Rajat Monga & TensorFlow Legacy [0:00]The inflection points in AI: TensorFlow's role and challenges [6:00]PyTorch vs. TensorFlow: A tale of shifting paradigms [16:00]The startup journey: Building Inference and lessons learned [27:00]Exploring O1 and advancements in reasoning frameworks [54:00]AI inference: Cost optimizations and hardware innovations [57:00]Agents, trust, and validation: AI in decision-making workflows [1:05:00]Rajat's personal journey: Tools for resilience and finding balance [1:20:00] Host:Amit Prakash: Co-founder and CTO at ThoughtSpot, formerly at Google AdSense and Bing, and a PhD in Computer Engineering. Amit has a strong track record in analytics, machine learning, and large-scale systems. Follow Amit on:LinkedIn - https://www.linkedin.com/in/amit-prakash-50719a2/  X (Twitter) - https://x.com/amitp42 Guest:Rajat Monga: He is a pioneer in the AI industry, best known as the co-creator of TensorFlow. He has held senior roles at Google Brain and Microsoft, shaping the foundational tools that power today's AI systems. Rajat also co-founded Inference, a startup focused on anomaly detection in data analytics. At Microsoft, he leads AI software engineering, advancing inferencing infrastructure for the next generation of AI applications. He holds a Btech Degree from IIT, Delhi. Follow Rajat on:LinkedIn - https://www.linkedin.com/in/rajatmonga/  X (Twitter) - https://twitter.com/rajatmonga Share Your Thoughts: Have questions or comments? Drop us a mail at EffortlessPodcastHQ@gmail.com Email: EffortlessPodcastHQ@gmail.com

Join us for an insightful session on how to unlock the true potential of your data. There are various tools and technologies that help enable data monetization. This talk will feature the extremely effective ThoughtSpot Embedded. We will host a thought-provoking panel discussion, designed to provide insights from seasoned professionals who are ready to share their expertise and real-world experiences.   Agenda: Our collective aim is to uncover the essential components of monetization and democratization (specifically for the financial services industry) within an analytics framework, empowering our attendees to unlock the full potential of their data resources. Throughout the session, we'll delve into critical topics such as: Customer retention enhancement The strategic shift of analytics from a cost center to a profit center The untapped potential of natural language processing How the financial services industry can specifically monetize data   About our Speakers:    Mike Lampa: Mike Lampa is a seasoned expert in digital transformation, focusing on modernizing enterprise data and analytics programs. With over 20 years of experience, Mike excels in implementing advanced technology tools and platforms. As a dedicated mentor, he guides seamless transitions and enhances analytics programs across various industries, including banking, manufacturing, telecommunications, oil and gas, retail, and consumer packaged goods. His expertise and dedication drive successful transformations, empowering organizations to thrive in the digital era.   Brian Reynolds: Brian Reynolds helps lead the Embedded Analytics Go To Market team globally at ThoughtSpot. He's passionate about the incredible potential that data holds for businesses to grow and enhance their customer relationships. With years of experience working for industry leaders in data and analytics such as Tableau, Accenture, Starburst and GoodData, Brian has helped businesses of all sizes, from seed-stage startups to Wall Street banks tackle the challenge of delivering information and insights to their customers. During his tenure at Tableau, Brian played a key role in launching and growing the embedded analytics group to $250M per year business line.   Explore our Online Presence: Dive into content and information on our website: https://www.greatdataminds.com/ Stay updated on upcoming events: https://www.greatdataminds.com/events/ Discover our services at: https://hike2.com/service/data-analyt... Join us on Social Media: LinkedIn:   / great-data-minds  

Key Moments: Focusing on Value with Bill Schmarzo 1:48Unlocking the Collective Genius with Walid Mehanna 4:07Building a Data-Literate Workforce with Valerie Logan 5:58Creating a Human-Centric AI Strategy with Sadie St. Lawrence 7:40Selecting the Right Tools with Katie Russell 11:23Implementing tools responsibly with Robert Garnett 16:00Why Clean Data Matters with Barr Moses 19:36Ensuring Responsible AI for the Long-Term with Dr. Gary Marcus 25:45 Key Quotes:“Data-driven is not important. Value-driven—that's what's important. We should focus on value.” — Bill Schmarzo, Head of Customer Data Innovation at Dell Technologies“Our role was rather to activate the organizational muscle… to try things out and tell us what has the highest opportunity and possibility.” — Walid Mehanna, Chief Data and AI Officer at Merck Group“It's really a mindset and a muscle… we need to foster this kind of lasting change.” — Valerie Logan, CEO of the Datalodge“Teaching people to ask better questions is more about critical thinking than technology.” — Sadie St. Lawrence, Founder of the Human Machine Collaboration Institute“We wanted to make analytics accessible to everyone, combining real-time data and intuitive tools so every team member can gain insights and contribute to our mission to decarbonize.” — Katie Russell, Head of Data and Analytics at OVO Energy As we are looking at applications of AI within our environment, we are focused first on responsibility, making sure that we have a broad enough data set when we're building machine learning models, for instance. And so that's at the heart of anything that we do.” – Robert Garnett, Vice President for Government Analytics and Health Benefits Cost of Care at Elevance Health“Our world is moving towards a place where data is the product—and in that world, directionally accurate just doesn't cut it anymore.” — Barr Moses, CEO and Co-Founder of Monte Carlo“The tech policy that we set right now is going to really affect the rest of our lives.” —  Dr. Gary Marcus, Scientist, Advisor to Governments and Corporations, and Author of Taming Silicon ValleyGuest Bios Bill Schmarzo Bill Schmarzo has extensive hands-on experience in the areas of big data, data science, designthinking, data monetization, and data economics. Bill is currently part of Dell Technology's core data management leadership team, where he is responsible for spearheading customer co-creation engagement to identify and prioritize the key data management, data science, and data monetization requirements.Walid MehannaWalid Mehanna is Chief Data & AI Officer at Merck KGaA, Darmstadt, Germany, where he leads the company's Data & AI organization, delivering value, governance, architecture, engineering, and operations across the company globally. With many years experience in startups, IT, and consulting major corporations, Walid encompasses a strong understanding of the intersection between business and technology. Katie RussellKatie Russell is the Data Director at OVO Energy, leading teams of Data Scientists, Data Engineers and Analysts who are transforming OVO's data capability. As part of a technology led business, leveraging data using artificial intelligence keeps OVO truly innovative, delivering the best possible service for our customers. Rob GarnettRobert Garnett serves as Vice President for Government Analytics and Health Benefits Cost of Care at Elevance Health. In this role, he leads a data-driven organization supporting analytics and insights for Medicaid, Medicare, Commercial and enterprise customers in the areas of population health, cost of care, performance management, operational excellence, and quality improvement. Valerie LoganFounding The Data Lodge in 2019, Valerie is as committed to data literacy as it gets. With train-the-trainer bootcamps, and a peer community, she's certifying the world's first Data Literacy Program Leads. In 2023, The Data Lodge was acquired as the basis of a newly formed venture, Data Society Group (DSG), aimed at fostering data and AI literacy and cultural change at scale. Valerie is excited to also serve as the Chief Strategy Officer of DSG. Previously, Valerie was a Gartner Research VP in the CDO team where she pioneered Data Literacy research and was awarded Gartner's Top Thought Leadership Award.Sadie St. LawrenceSadie St. Lawrence  is on a personal mission to create a more compassionate and connected world through technology. Having grown up on a farm in Iowa she witnessed first-hand how advancements in technology rapidly changed how we work and earn a living, which in turn affected the overall success of a community. Through her work, she noticed that while many organizations and individuals have good intentions when it comes to D&I in data careers, there was a lack of progress.Dr. Gary MarcusGary Marcus is a leading voice in artificial intelligence. He is a scientist, best-selling author, and serial entrepreneur (Founder of Robust.AI and Geometric.AI, acquired by Uber). He is well-known for his challenges to contemporary AI, anticipating many of the current limitations decades in advance, and for his research in human language development and cognitive neuroscience. An Emeritus Professor of Psychology and Neural Science at NYU, he is the author of six books. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Description: Marni Baker Stein, Chief Content Officer at Coursera, joins host, Cindi Howson, and dives into  the impact of Generative AI on skills, diversity in tech, and the future of upskilling.Key Moments: The impact GenAI and the surge of learning demand (05:45)Why employers must prioritize AI literacy (10.32)The gender gap in AI learning and why it matters (19:40)Leveraging data to drive personalization and learner success (24:00)Predictions for the future of AI in the labor market (29.53)Key Quotes:“Generative AI is going to require us to all be a lot more emotionally intelligent because it's going to create such disruption and change. And we're all going to have to navigate the complexities of this change. We're going to have to bring our organizations through this change. That's going to take emotional intelligence as the one thing this technology isn't, is human. Understanding and human empathy is going to remain paramount.”“In terms of data and AI skills, what is extraordinary is that the demand for these skills in the last year has grown over a thousand percent. We now have seven individuals a minute enroll in GenAI content.”“Millions of people globally are deciding that it's time to upskill and reskill in these AI, regardless of whether their employer is telling them to or not. People see it happening. They're reading about it. They're hearing about it. And they're actively going out and chasing down those skills.”Mentions:  Caste: The Origins of Our Discontent by Isabel WilkersonFrom Academia to EdTech: The Path to an Equitable Education in the Digital Age Girls Who CodeMarni Baker Stein Bio: Marni Baker Stein is Coursera's Chief Content Officer, where she oversees the company's content and credential strategy and partner relationships. Marni has more than 25 years of experience in producing and scaling online and hybrid education programs. Prior to joining Coursera, she was Chief Academic Officer and Provost at Western Governors University, where she led its four colleges serving more than 135,000 students with programs that improved access and affordability without compromising academic quality. Before that, Marni held several leadership positions focused on access, student success, and program design at institutions such as the University of Texas, Columbia University, and the University of Pennsylvania. She earned her PhD in Educational Leadership from the University of Pennsylvania.  Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Key MomentsFrom Engineer to Data Leader (03:05)A Mindset Shift: Business Problem First, Data Second (9:31)Learning From Missteps (11:00)The Gazelle and the Lion Analogy (14:53) The Role of AI: Do Things, Do Things Better, and Do Better Things (25:58)Built to Last versus Built to Adapt (40:01) Key Quotes"Instead of a data-first mindset, you need to have a business problem-first and data-second mindset. That has helped me transform myself as a leader quite a bit.""It's more important to define the problem right than solving the problem. How can we understand what you're trying to solve, and how it impacts the stakeholder?"“The head of data analytics functions need to be business problem driven, empathy driven, and not technology-first minded or AI-first minded. Our objective is to solve the business problems of the organization. Data, AI, and tech are the enablers.”"In the past, we built capabilities to last. Now, the mindset has to be to build capabilities to adapt."MentionsIs Data Quality the Biggest Threat to Humanity? With Barr Moses and Olga MaydanchikResponsible AI InstituteTwo-Pizza RuleGirl Scouts STEM ClassesFrom Cancerman to Ironman: A Police Officer's Journey of Arresting IllnessHans ZimmerDeepak Jose Biography Deepak Jose is Vice President, Head of Data Sciences & Business Intelligence at Niagara Bottling. He is a member of the Forbes Tech Council, AWS Retail and CPG Executive Advisory Forum, industry standards associations, Editorial Board for CDO Magazine, and an advisor for startups and AI analytics service companies.Before Niagara, Jose was part of global brands like Coca-Cola, Mars, ABB Group, Asurion and Mu Sigma in strategic roles driving business growth. He was named to the 2023 Consumer Good Visionaries by Consumer Goods Technology and Retail Info Systems News, the 2023 40 under 40 by CDO Magazine, the 2022 and 2023 Top 100 Innovators in Data & Analytics by Corinium Global Intelligence, the 2023 100 Most Influential AI Leaders in USA by AIM Research, the 2023 Direct 60 List by The Lead, and the 2023 DataIQ 100 lists. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Key Moments: Disappointment With Today's AI Systems (4:00) Congressional Inaction And The Need for AI Regulation (9:00)The Seduction of AI Propaganda (15:00)The Misguided Hypothesis of "Scale is All You Need" (23:00)Don't Be Fooled by the Masters of AI Hype (27:00) The Global AI Race and the Need for International Cooperation (33:00)Key Quotes:“This matters. It matters as much as immigration policy or financial policy. The tech policy that we set right now is going to really affect the rest of our lives.”“We should want to have AI that can be like an oracle that can answer any question. There is value in trying to build such a technology. But, we don't actually have that technology. A lot of people are seduced into thinking that we do. But it may be decades away.”“Nobody can look you in the eye and say, ‘I understand how human intelligence works'. If they say that, they're lying to you. It's still an unexplored domain.” Mentions:  Taming Silicon Valley: How We Can Ensure AI Works for All Of Us Kluge: The Haphazard Construction of the Human MindThe Algebraic Mind: Integrating Connectionism and Cognitive Science (Learning, Development, and Conceptual Change)The EU AI ActAI Generates Covertly Racist Decisions About People Based On Their DialectDr. Gary Marcus Bio: Gary Marcus is a leading voice in artificial intelligence. He is a scientist, best-selling author, and serial entrepreneur (Founder of Robust.AI and Geometric.AI, acquired by Uber). He is well-known for his challenges to contemporary AI, anticipating many of the current limitations decades in advance, and for his research in human language development and cognitive neuroscience.An Emeritus Professor of Psychology and Neural Science at NYU, he is the author of six books, including, The Algebraic Mind, Kluge, The Birth of the Mind, the New York Times Bestseller Guitar Zero, and most recently Taming Silicon Valley: How We Can Ensure AI Works for All of Us.  He has often contributed to The New Yorker, Wired, and The New York Times. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

During this episode of TECHtonic, host Thomas Lah has a compelling conversation with Kelley Jarrett, SVP of Revenue Strategy, Operations, and Enablement at ThoughtSpot—an AI-powered analytics platform. They discuss how AI is transforming revenue generation workflows and operational efficiency within organizations.Kelly shares her unique insights on leveraging AI for quick, data-driven decision-making, highlighting real-world use cases across industries like retail, healthcare, and aviation. She discusses ThoughtSpot's journey from an on-premise solution to a SaaS model embracing AI, and how this evolution is enabling businesses to unlock unprecedented value from their data.Listen in to discover how AI can be a game-changer in reducing costs, accelerating growth, and driving revenue excellence.

Companies are dealing with open model vulnerabilities Bill Tennant Industries are facing challenges in the practical and effective adoption of AI, with 33% of enterprises citing limited AI skills and expertise as a significant challenge. Companies are dealing with open model vulnerabilities. Despite 72% of organizations adopting AI in 2024, integration issues remain a major hurdle, exacerbated by the shortage of skilled personnel. Additionally, the risks associated with generative AI—particularly inaccuracies—necessitate robust strategies for mitigation. Data privacy concerns also arise, as AI's need for vast data raises privacy and security issues. Furthermore, doubts about cost and ROI are prevalent, given that AI implementation is expensive, with uncertain returns. For example, 25% of IT leaders regret quick AI investments, as AI readiness impacts ROI assessments. Enterprises are discovering the need to shift from outdated models to agile, tech-driven approaches. This Cognitive Digital Revolution demands innovation with systems that drive growth and support the economy. In this podcast, Bill Tennant, BlueCloud's Chief Revenue Officer outlines these challenges and discusses how BlueCloud helps guide their clients. We discuss: What strategies can enterprises adopt to overcome the integration hurdles associated with AI deployment? In what ways can businesses balance the need for vast data in AI systems with growing data privacy concerns? What measures can organizations take to ensure a clear ROI when investing in AI technologies Can enterprises enhance their AI talent pool to overcome current skill gaps and integration challenges? What role do ethical considerations play in AI implementation, and how can businesses develop a robust strategy to tackle inaccuracies and risks associated with generative AI? About BlueCloud: BlueCloud is not just another entity in the cloud computing space; it stands as a trailblazer in the digital transformation revolution. Positioned as architects of the future, BlueCloud leads the way for enterprises seeking to thrive in the digital age with its bold vision and unwavering commitment to innovation. The company's comprehensive portfolio, encompassing avant-garde AI services, data engineering solutions, and transformative digital strategies, has propelled businesses into a new era, resulting in a staggering 185% year-over-year revenue growth and securing a valuation surpassing $100 million, thanks to its partnership with Hudson Hill Capital. By serving titans of various industries and forging collaborations with technology behemoths like Snowflake and ThoughtSpot, BlueCloud has demonstrated its prowess in navigating the intricacies of the digital domain. More than merely transforming businesses, BlueCloud is on a mission to reshape the digital landscape itself, one innovative cloud solution at a time. About Bill Tennant: Bill Tennant stands at the forefront of BlueCloud as the Chief Revenue Officer, where his nearly 20 years of experience across various industries fuel the company's growth and innovation. With a decorated background featuring honors like TBBJ 40 under 40 and the CRN Next-Gen Solution Provider Leader, Tennant's leadership has been pivotal in securing BlueCloud's position as a partner of choice for tech giants. His vision has led to milestones such as the Snowflake Elite Services Partner Designation and numerous awards for channel partnerships. Tennant is an advocate for leveraging Generative AI, Machine Learning, and Data Governance to deliver business value and is open to discussions about joint ventures that push the boundaries of technology and strategy.

Key Moments: Jeremy KhanThe history of AI: the Turing Test and the Eliza Effect with Jeremy Khan (1:50)Jeremy's view on how we can learn from lessons of the past (9:00)It starts with data and people: leveraging AI to increase productivity (16:00)Sol RashidiSol Rashidi on failing to succeed in AI (31:00)The need for rogue executives (37:00)Sol's view on prioritizing GenAI use cases and measuring ROI (42:50)Bernard MarrBernard Marr on demystifying AI (60:02)Is society ready for AI's impact on augmenting jobs? (66:00)AI's impact on personalization of medicine, treatment and drug discovery (72:00)Key Quotes: Jeremy Khan“We're in a position where we can take action while this technology is still being shaped, to try to set some sensible guardrails. If we do that, we will see a lot of benefit from this technology. If we wait, we're going to be in a situation like with social media. We will have a deskilling of essential human cognitive abilities.”We don't talk enough about how to train people to use AI software. The organizations that think hardest about that are going to be very successful.”Sol Rashidi “Usually I start the conversations of how ROI shouldn't just be a financial measure. There's three ROI's in my opinion. There's a financial ROI, there's a cultural ROI, and there's a relevancy ROI.”“I am adamant that business value is not the number one marker. Everything needs to be scored and graded by criticality and complexity. And your criticality is a measure of ‘what is the impact if we don't do this?'”Bernard Marr“What we are seeing is we will see an augmentation of pretty much every single job. I can't think of many jobs that will not be augmented by GenAI. We need to really expect change as individuals.”“We must create a world where education is seen as something that never stops, that carries on. I believe that we are currently entering a hyper evolution cycle, with artificial intelligence right at the center of it.”Mentions: 'Mastering AI: A Survival Guide to Our Superpowered Future' by Jeremy Khan 'Your AI Survival Guide: Scraped Knees, Bruised Elbows, and Lessons Learned from Real-World AI Deployments' by Sol Rashidi'Generative AI in Practice: 100+ Amazing Ways Generative Artificial Intelligence is Changing Business and Society' by Bernard MarrBios: About Jeremy Khan Jeremy Khan is an award-winning journalist for Fortune magazine, where he covers AI and other emerging technologies. Previously, he wrote about technology, including AI, for Bloomberg. His writing on a range of subjects has also appeared in The New York Times, Newsweek,The Atlantic, Smithsonian magazine, The Boston Globe, The New Republic, and Slate. An Ohio native, he now lives with his family in Oxford, England.About Sol RashidiWith 10 patents granted and winning numerous awards that include: 'Forbes AI Maverick & Visionary of the 21st Century', 'Top 100 People in AI', 'Global 100 Power List', 'Top 75 Innovators', 'CAO of the Year', 'Top 5 CDO's', 'Top 65 Most Influential Women', Sol Rashidi is an seasoned executive, leader, and influencer within the AI, data, and technology space. Sol's experience comes from real-world deployments where she has had to roll up her sleeves and do the work, while keeping the strategic intent in mind. Sol is currently Head of Technology for Startups, North America, at AWS.About Bernard MarrBernard Marr is a multi-award-winning and internationally best-selling author of over 20 books, who writes a regular column for Forbes and advises and works with many of the world's best-known organizations. He has a combined following of 4 million people across his social media channels and newsletters and was ranked by LinkedIn as one of the top 5 business influencers in the world. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Key Moments:The road to entrepreneurship (1:00) Bringing transparency to the prescription drugs industry (4:30) Analyzing in-game sports data to shoot for new heights (20:00) Changing the world by making data and AI accessible to everyone (25:00) How LLMs can build curiosity for the next generation of tech talent (33:00)Key Quotes:“As with all things technology, everybody has access to the information, but few people take the time. But those who do tend to have an edge. If you're curious, if you love to learn, you're going to do pretty well. But how do you find those people when they're kids and how do you try to just capture their imagination and get them excited about the technology? That's why we started the bootcamp.”“I wanted to make technology accessible to people who otherwise couldn't get it. It doesn't matter what you look like, who you are, what your ethnicity is, what your background is. There are just going to be people who don't have access. I wanted to open that door for them. I'm a big believer in diversity, and that when you look at places where other people aren't, that's when you find brilliance that can change the world.”“The path to least resistance to learning AI is simple. All you've got to do is use it. You can use it in ways that you can't possibly imagine. You can learn how to use large language models to start your own programming and teach yourself how to do it. The sky's the limit. Better way to put it, there is literally next to nothing you can't teach yourself using a large language model. You can even train and educate the model. It's a virtuous cycle. It can surge curiosity with kids.”Mentions:Cost Plus DrugsThe Master Algorithm: How the Quest for the Ultimate Learning Machine will Remake our World Mathletics: How Gamblers, Managers, and Sports Enthusiasts Use Mathematics in Baseball, Basketball, and Football Mark Cuban AI BootcampsMark Cuban Bio: Mark Cuban has been a natural businessman since age 12, when he sold garbage bags door to door.  He went on to found MicroSolutions right out of college, selling it to H&R Block.From there he became an active stock trader, building a track record, starting a hedge fund and selling it a year later. In 1995, he and Todd Wagner started the first commercial streaming company, AudioNet, which became Broadcast.com. They later sold the company in 2000 for 5.7B dollars.Mark acquired the Dallas Mavericks in 2000.  The Mavs competed in their first NBA Finals in 2006, won their first League title in 2011.  Mark sold majority ownership in 2023, but still retains a significant stake. During his time as majority owner, the Mavs had the second best record in the NBA. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Key Moments:Why is the data wrong? (6:00)Our products are our data (11:00)The true size of the data quality problem (14:00)Clean your data before you prioritize shiny new tools (26:00)The next frontier: GenAI and unstructured data (31:00)Key Quotes:“The data estate has changed significantly. But the way in which we manage data and data quality specifically has not adapted.” – Barr Moses“I tracked every single change in the data that I made, and could calculate how much money a company saved after a data cleanup. For a mid-size company, the difference was approximately a quarter of a billion dollars. For a large company, it could be several billion dollars. 45% of the data I cleaned had errors.” – Olga Maydanchik“The competitive advantage is really the access to your proprietary data that you have as an enterprise. So you need to make sure that that data is accurate, reliable, and on time. Now, how do you do that? That's something that people are still figuring out.”  – Barr MosesMentions:Information Quality Applied: Best Practices for Improving Business Information, Outcomes and Systems: Book by Larry EnglishThe Rest is History PodcastFreakonomics PodcastThe Matrix Film SeriesThe Play That Goes WrongBio: Barr Moses: Barr Moses is the CEO and Co-Founder of Monte Carlo, the data reliability company. Monte Carlo is the creator of the industry's first end-to-end Data Observability platform. She is also co-author of O'Reilly's Data Quality Fundamentals: Building Reliable Data Pipelines. Previously, she was VP Customer Operations at Gainsight, a management consultant at Bain & Company and served in the Israeli Air Force as a commander of an intelligence data analyst unit.Olga Maydanchik:Olga Maydanchik is a data governance, data quality, and data architecture thought leader and practitioner. She is an expert in design and implementation of enterprise-wide data management programs, who has led data quality efforts at Deutsche Bank, AIG, and at Citi. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Catch up on the latest in independent pharmacy in this episode of The Bottom Line Pharmacy Podcast where we share our recap of the 2024 tradeshow circuit.  We share our experience at McKesson, RBC, and ThoughtSpot. We also talk about the opportunities pharmacies have in areas such as GLP compounding, the buying and selling market, pharmacies starting their own insurance, and more! Join the discussion with us. Did you like this episode? Stay up to date on new episodes by liking and subscribing!  Click below to learn more about our podcast team and previous episodes: https://www.sykes-cpa.com/media-resources/the-bottom-line-pharmacy-podcast/ Want to stay connected? Check out all of our social media:  Sykes & Company P.A.:  Facebook Twitter (X) LinkedIn Instagram  CPA's: Scotty Sykes – CPA, CFP LinkedIn Scotty Sykes – CPA, CFP Twitter Bonnie Bond – CPA LinkedIn Bonnie Bond – CPA Twitter More resources about this topic:  Video – GLP Compounding Deep Dive Video – Pharmacies Launching Their Own Insurance Agencies Podcast – Master the Margin Podcast – Navigating GLP and Other Pharmacy Opportunities 

Key Moments:Leveraging data for good (2:00) Every leader is responsible for data management (13:00) New metrics to validate AI's sustainability (21:00) Mitigating AI's risks to society (23:00) The current shape of global AI regulation (28:00) The importance of diversity in mitigating data bias (37:00) Key Quotes:“Every leader must understand that they have a responsibility for data management. It's an underlying skill that we really have to harness in all of our college, university, and high school programs. It's fundamental. We seem to teach people how to problem solve, but this is table stakes. In order to ever get AI right, we've got to solve the data challenges.”“There's no question on whether business value and how to measure AI's return on investment (ROI) is always top of mind in my discussions with executives. But what they really want to know is if their existing ROI methods are sufficient or not. What are the new metrics that they need to put in place to validate AI and its sustainability?”“We're not at the high-growth stage of AI innovation. We're in the early experimentation stage. We don't have international guardrails. All of these systems are going to take around 20-years to put in place. It takes six years to put a new university curriculum in place. People have to take responsibility to learn. This is a fundamental shift and it's one that's happening at break lightning speed.”Mentions:Mood InsightsGallup Research: 1 in 5 Employees Feel Lonely Worldwide KFF Loneliness and Support Networks Survey United States Artificial Intelligence Institute Dr. Cindy Gordon's AI Insights NewsletterHispanic Alliance for Career Enhancement and SalesChoice Whitepaper: Why Diversity Equity and Inclusion Leaders Must Lead in AIBio: Dr. Cindy Gordon ICD.D. is the CEO of SalesChoice, a SaaS AI company focused on Ending Growth Uncertainty for Human Advantage, and has been recognized by Onalytica as one of the top AI global influencers. Prior, she has held senior executive and partner roles at Accenture, Xerox, and Citicorp. She has also been a venture capitalist and angel advancing B2B technology software companies. Internationally, she is recognized for her innovative thought leadership with over 14 books in the market. Cindy is also a board advisor, thought leader in SaaS, AI and AI education, market research companies at: The AI Forum, Corent Technology, Forbes, Kaji.AI, USAII. Her AI community track record is extensive, University of Arizona – Business and Technology AI Board Advisor, Adjunct Professor, George Brown College, Applied AI. She regularly speaks at international conferences to advance AI Ethics and AI Education to board directors and C-suite executives. Academically, Dr. Gordon has an honorary Applied AI Doctorate Certification from George Brown College, an MIT AI Strategy Certification, and a doctorate in Complexity Science and Social Networks. She is also a certified Board Director with an ICD.D. designation. Under Dr. Gordon's leadership, the company has won over twenty international awards, most recently she was recognized as the CEO of the Year Award for Women in Digital Transformation. She has also received the Governor General Award for her Innovation and Community Leadership. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

In this episode of Detection at Scale, Jack speaks to Alessio Faiella, Director of Security Engineering & Security Operations at ThoughtSpot, to discuss building forward-looking security programs for 2024.  Alessio dives into the dynamic and ephemeral nature of modern security environments and the importance of understanding the nuances of the product and user base. He also highlights how ThoughtSpot leverages AI to enhance detection and response capabilities. Additionally, Alessio shares insights on codifying playbooks and prioritizing core focuses to ensure a robust cybersecurity posture.    Topics discussed: The importance of defining clear goals and laying strong foundations for scalable security programs. Emphasizing the need for security teams to deeply understand the product they are defending and the behaviors of its user base. The significance of developing and prioritizing detailed playbooks to guide detection and response efforts effectively. How AI can assist in real-time response, log data parsing, and providing actionable recommendations during security incidents. Identifying and focusing on critical areas like persistence, lateral movement, and data exfiltration to optimize security efforts with limited resources. Techniques for evaluating the success of security playbooks and ensuring they align with the organization's goals and infrastructure. Combining automated processes with human oversight to enhance the efficiency and accuracy of security operations. The difficulties in gathering and integrating data from various sources to enable quick and informed security responses. Crafting security rules that are tailored to the specific needs and priorities of the organization's environment. Advice on maintaining focus and ensuring foundational security practices are in place for a strong and resilient cybersecurity posture.

Key Moments: A journey from intern to CEO (05:10)Encouraging a harmonized relationship between humans and AI (09:58)Why embracing stress can drive urgency and effective change (17:18)Generative AI's impact on the skills landscape (30:39)Fostering a data-driven company culture (36:41)Embrace change, and quickly (40:25)Key Quotes: “AI does amazing things, like summarizations and semantic search. Humans do amazing things like curation of knowledge, making sure it's accurate, connecting the dots, and creating relationships. So bringing the power of humans-in-the loop, especially given a broader trust deficit, felt like the right thing to do at this point in time.”“I think ultimately what guides us is we want to be useful to our users and our customers. That's the guiding light. Because why do we exist as an organization or a community? We should all just go home. If we don't actually have a mission and purpose that adds value, then we don't have a purpose. So the question is, what is that? What is the highest purpose?”“When you think about the future of software development, there's a lot of doomsdayers about job losses. I think it's going to be the opposite. I think AI reduces the barrier to entry. I think a lot of people will be “developers”, even though they may be doing very different things.”Mentions: WeAreDevelopers World Congress 2023 OverflowAIOverflow API Stack Overflow for TeamsAmp It Up Book Bio: Prashanth Chandrasekar is Chief Executive Officer of Stack Overflow and is responsible for driving Stack Overflow's overall strategic direction and results.Prashanth is a proven technology executive with extensive experience leading and scaling high-growth global organizations. Previously, he served as Senior Vice President & General Manager of Rackspace's Cloud & Infrastructure Services portfolio of businesses, including the Managed Public Clouds, Private Clouds, Colocation and Managed Security businesses. Before that, Prashanth held a range of senior leadership roles at Rackspace including Senior Vice President & General Manager of Rackspace's high growth, global business focused on the world's leading Public Clouds including Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP) and Alibaba Cloud, which became the fastest growing business in Rackspace's history. Prior to joining Rackspace, Prashanth was a Vice President at Barclays Investment Bank, focused on providing Strategic and Mergers & Acquisitions (M&A) advice for clients in the Technology, Media and Telecom (TMT) industries.  Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Key Moments: The emotional temperature for change in analytics (5:06)There's no playbook for change management (7:53) Why Generative AI success requires a melding of expertise (17:20) Measuring the success of LLMs (18:58) How do you embrace the new when you're facing technical debt? (33:27)How to fine-tune your career in today's data and AI landscape (37:30) Key Quotes: “Business intelligence tends to have this notion of looking backwards. It's not thinking about prescriptive or predictive analytics, or live analytics, powered by the new capabilities that we're seeing. I do think we're going to evolve to a new name.” (03:42) “You'll find companies that were and are ahead of the curve will be able to take advantage of these new technologies, GenAI, LLMs, et cetera, much more quickly than other companies. So companies that have not invested the time, resources, money, attention, and prioritization into data governance, data use, and data literacy are at a serious disadvantage. And companies that have done the opposite, that have proactively invested, will be able to make significant gains.”“I think there's three pillars of success. One is understanding your data from end to end. What is it used for? What domain is it in? Understand it as much as possible. What is the product? What is the data product that you have in all aspects of it? Then, understand your business, right? How does data relate to your business? These people are going to be the ones that leverage the technology the most efficiently.”“Don't hesitate to take a lateral mobility move. I know people are always interested in going up, up, up, up, and up. However, you know, sometimes consider going sideways.” (40:46) Mentions: Jamie Dimon Annual Shareholder Letter Data Literacy Data Storytelling Unfrosted Film Hacks TV ShowAbraham Lincoln Bio: Scott Stevens is responsible for Intelligent Solutions, which empowers JPMorgan Chase employees through innovative data, Business Intelligence and low-code capabilities.Scott has been with JPMorgan Chase since 2011 and has worked in Financial Services his entire 32-year career, with prior roles at MBNA, Bank of America and Sallie Mae. Scott has been in data and analytics roles since 1997.Scott enjoys working with local universities on modernizing data science curriculum, guest lectures and coaching student teams on analytics projects.  While at work, Scott enjoys mentoring and likes to help people advance their careers and skills.  Scott serves on multiple non-profit boards, and Scott is the executive sponsor for the JPMorgan Chase Delaware Volunteer Leadership Group.Scott lives in Delaware with his family. Scott's personal interests include travel, wine-making, photography and rooting for the Philadelphia Eagles. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Key Moments: Leveraging Generative AI for work? Start by asking the right questions (04:13) What's in store for the future world of work (18:18) How Generative AI can expand humans' divergent thinking (26:16) Taking people and culture along the Generative AI journey (31:20) The value of diversity in data (42:00)A tale of mentorship (44:20)Key Quotes: “With Generative AI, now we have a command line interface that's allowed us to converse, which is so core and essential to who we are as humans. The ability to be able to talk to one another. That's allowed us to survive for thousands and thousands of years and evolve.”“I think that there's a lot of greater potential in terms of expanding our own creativity and strategic thinking. So while humans have flexible and moldable brains and we have neuroplasticity that allows us to learn new things, we have to put ourselves in those environments. AI is really good at divergent thinking. So when we think about creativity, a core aspect of that is divergent thinking. What comes to mind when you think of a tree? Maybe leaves and fruit. Divergent thinking is thinking of all the outside things, like sunshine and soil, that may be associated with the tree or that tree growth. There's a lot more potential that we've yet to unlock in terms of updating our own thinking to expand our own divergent and creative thinking” “We know that when we have more diverse teams where everyone feels that they can speak up, and you get better ideas. You get more collaboration. So having that core vision of what is that culture and environment that you want to have is really key” Mentions: Women in DataHuman Machine Collaboration Institute Beyond AI Exposure: Which Tasks are Cost-Effective to Automate with Computer Vision?Dialect Prejudice Predicts AI Decisions About People's Character, Employability, and CriminalityAWS: What Are AI Agents?Women in Data Climate Sustainability Datathon 2023 The Creative Way: A Way of BeingBio: Sadie St. Lawrence  is on a personal mission to create a more compassionate and connected world through technology. Having grown up on a farm in Iowa she witnessed first-hand how advancements in technology rapidly changed how we work and earn a living, which in turn affected the overall success of a community. In addition, Sadie was homeschooled her entire childhood which led to a unique perspective in self-directed learning approaches and out of the box thinking.Sadie holds a diverse education having degrees in piano performance, psychology, and data science, but at her core she has always been a teacher. In 2014 she transitioned from working in a neuroscience lab studying emotional learning and memory, to working in data science. During her time as a data scientist, she went on to lead data science teams and consult for Fortune 500 companies in AI. Through her work, she noticed that while many organizations and individuals have good intentions when it comes to D&I in data careers, there was a lack of progress.Today, Sadie's work is focused on educating individuals in technology, increasing access and pathways for all people and creating a more equal future for all. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

In this episode of the B2B Sales Trends podcast, host Harry Kendlbacher interviews Ford Williams, the VP of Commercial Sales at ThoughtSpot, diving deep into the strategies and insights that have propelled him to success in the world of B2B sales. Ford highlights the importance of sales efficiency and urgency, emphasizing the need for sales teams to adapt to the rapidly evolving landscape of technology sales. He discusses the transformation he led within his team, streamlining the sales cycle and implementing strategies to drive deal velocity and overall efficiency. A key aspect of Ford's approach is the concept of the "Library of Pain," a resource that helps sales reps tap into customer pain points and effectively communicate how their product addresses those needs. He emphasizes the importance of generating new needs in the minds of buyers and championing the value proposition of their product. Throughout the episode, Ford shares invaluable insights into the emerging trends in B2B selling, including the increasing speed of decision-making, the importance of consensus selling, and the emphasis on time-to-value. He also discusses the top three skills that salespeople need to excel: the ability to generate revenue, champion building, and closing deals early. Listeners will gain actionable strategies and techniques to enhance their sales performance and navigate the ever-changing landscape of B2B sales effectively. Don't miss out on this insightful conversation with Ford Williams, packed with practical advice and expert insights for sales professionals striving for success in today's competitive market.

Key Moments:The voyage to a data-driven US Coast Guard (5:21)Navigating data-driven approaches to US Coast Guard operations (20:28) Balancing experience-based decision making with data-informed decision making (25:34)In whose data do you trust? (30:10) Measuring the value of data (33:80)Should an AI ethicist be part of the team or should everyone really be an ethicist? (45:00) Key Quotes:“Up until three years ago when we started this, some people – and really our entire organization –  just thought data as IT. They didn't think much past that, because no one had ever really challenged them to think about it. So it wasn't really thought of as, ‘this is the data that we have, and this is the commander's business. This is how the business is going to run. It's not just letting IT figure it out.'” “I think that technology has helped us along the way to visualize data that otherwise would be difficult and time consuming to conceptualize and understand. And as we continue to find ways to make humans understand better what it is that they're looking at – especially in extremely dynamic and complex data situations – I think you'll start to see a shift of trust and that's really experience. It's experience in using data informed decision making activities.” “Would an ethics, an AI ethics advisor to the CDAO be a great thing? Absolutely. Are we all just ethicists? Yes, but I would say that there is a lot of understanding needed. There's a huge area where you could be an expert in the ethics of artificial intelligence and provide sound guidance day after day. I would think that this particular type of employee would be extremely valuable.” Mentions:U.S. Coast Guard 11 Missions AI U.S. Executive Orders White House Orders Federal Agencies to Name Chief AI Officers America's Cyber Defense AgencyMake Your Bed: Little Things That Can Change Your Life… And Maybe the World Bio: Captain Brian Erickson currently serves as the United States Coast Guard's first Chief Data and Artificial Intelligence Officer and is principally responsible for the coordination of data and artificial intelligence activities across the organization. His previous assignments focused primarily on engineering and operations, serving at five operational assignments piloting rotary  and fixed wing aircraft performing search and rescue, law enforcement and other military mission profiles. Brian is a licensed Professional Engineer specializing in aerospace and also served in the Office of Budget and Programs working directly for the Chief Financial Officer (CFO). In 2020, he was selected as the Coast Guard's MIT Sloan Fellow following service as Commanding Officer of Coast Guard Air Station Savannah, GA. Brian is a 1998 graduate of the U.S. Coast Guard Academy, and holds a Master of Science degree in Aeronautics and Astronautics from Purdue University as well as a Master of Business Administration from Massachusetts Institute of Technology. He is a 2022 DataIQ Top 100 most influential persons in data and the 2023 MachineCon AI Leader of the Year. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Tune in to learn:Make your business strategy a data strategy  (3:10)‘Cover my back' approaches (09:00)Straddling risks in data and business (11:28)Learnings from implementing myGPT (16:18) Activating your organization's collective genius (24:05)Creating the right processes and culture to breed AI success (31:00) The current state of AI regulation (38:35) Key Quotes: “You can have an AI strategy without a data strategy or without a business strategy, but it will not help you much. So your data strategy is your business strategy and vice versa. (04:42).“I would say we started with the collective genius or activating the collective genius. What we wanted to do is enable everybody to try things out and tell us what has the highest opportunity and possibility” (25:46)“We're now slowly going into a paradigm shift where we go away from more reports, from more dashboards, and into what's important for me to know today. I don't want to go through 200 dashboards in three different technologies. The one thing that I want is an intelligent model that has access to all of my data and tells me, well, there's five KPIs you should have a look at. Maybe it's a data quality problem. Maybe it's nothing. Maybe it's just a deferred invoice or whatever. But maybe it's something that needs your attention and you should now get active on it.” (30:26)“I think AI is a wonderful technology. I think it has a lot of potential upside. It has the high risk of being misunderstood and overestimated. But honestly, you can't blame it on technology. Often, that's part of the history of large organizations. It's not always the technology, it's the adoption of the technology. And this has a lot to do with maturity of the workforce, maturity of the organization, processes, culture. So you can bring the best technology in the world, but if you don't have fertile ground, if you don't have the right people on the ground that make sure that your workforce understands it and also that your processes are adjusted accordingly, then the technology will fail” (34:05)“My dream is that you don't need me anymore because I'm a transformational leader. And when everybody in this organization breathes data, breathes AI and applies it every single day, then my task is done, then you don't need me anymore. I still have a few days.” (38:00)Mentions: Bring Your Own DocumentsLangdockSnowflake Gary MarcusKoshari RecipeBio: Walid is Chief Data & AI Officer at Merck KGaA, Darmstadt, Germany, where he leads the company's Data & AI organization, delivering value, governance, architecture, engineering, and operations across the company globally. With many years experience in startups, IT, and consulting major corporations, Walid encompasses a strong understanding of the intersection between business and technology. Born in Egypt and raised in three different states in Germany, Walid celebrates his multicultural background and leverages it to inform his commitment to DE&I. As a father of two amazing daughters, he advocates for a more equal workplace to ensure a better future for the next generation. Walid strives to be the best ally he can be, making these values the cornerstone of his leadership approach. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

People are only as healthy as the communities they live in. In this episode, discover how Elevance Health is reimagining  the healthcare system, and strengthening our communities in the process with a data-driven strategy. Learn how Elevance Health leverages community data to paint a more complete picture of an individual's health, making healthcare journeys more personalized and equitable. You'll also hear why analytics must have a seat at the business table, the importance of extending data democratization beyond the organization, and the need for change management along the journey. Tune in to learn:How Elevance Health forms holistic views of patients' heath by connecting community data (03.55)Why analytics should sit at the business table (13:08)How to understand your customers as individuals, learn their business problems, and meet them where they are (19:10)Why data democratization should go beyond the organization (21:16)How to engage in a new relationship with data with natural language (35:35)Key Quotes:“Once you're sitting across from a community-based organization and you're helping them with where to focus efforts, having data in that conversation and being able to show, well, here are where our members are located and here are the members that have a chronic need for food or for transportation. Using data in that conversation is a game changer.”“Analytics should be at the table, not a takeaway from the table. So I think analytics, when they're sitting around the table with the business when they're making decisions or they're working through a problem, is a very different construct than traditional models where the business convenes, works through a problem, then decides well we need more data, or we need data to drive a decision here, go ahead and put in a ticket or seek additional data and bring it back."“I think training is critical. I've seen far too many dashboard wastelands where you have dashboards sitting out there that are accessed very little, but have really, really good information. What that tells me is there wasn't a good amount of training. There is not a strong communication plan. There's not a robustness of really ensuring that the solution is oriented to the problem that it was created to solve.”Mentions: The Data Chief: How Healthcare Data Can Save Lives with Truveta CEO, Terry MyersonDoula Care Found to Improve Maternal Health Outcomes StudyAdvancing Health Together 2023 ReportRadical Candor by Kim Scott Essentialism: The Disciplined Pursuit of Less by Graham McCownMultipliers: How the Best Leaders Make Everyone Smarter by Liz WisemanBio: Robert Garnett serves as Vice President for Government Analytics and Health Benefits Cost of Care at Elevance Health. In this role, he leads a data-driven organization supporting analytics and insights for Medicaid, Medicare, Commercial and enterprise customers in the areas of population health, cost of care, performance management, operational excellence, and quality improvement. Prior to his current role, Robert served as President and CEO, Amerigroup Tennessee, where he was responsible for the strategic, fiscal, regulatory, and operational leadership of the health plan. In his role, he was also responsible for building and managing state and local relationships and fostering new growth and strategic opportunities within Tennessee's TennCare Medicaid program. Prior to his promotion to President in 2018, Robert served as the Chief Operating Officer and previously the Director of Medicaid State Operations, leading all day-to-day health plan operations and execution, customer service, quality management, and regulatory oversight from 2014 to 2018. He served in a similar operational leadership capacity for Amerigroup Georgia from 2011 to 2014. In addition to these roles, Robert supported South Region Medicaid with business development & implementation, and strategic operations. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

In developed nations, corporations often rollup and eat independent mom and pop shops into big box retail or big .coms that centralize supply chains and logistics. Meanwhile, independent sellers are the backbone of emerging markets. But a collision is starting to occur and it's happening fast. Big corporations want to bring the same strategy to these emerging markets, so how can the independent seller maintain their independence? To do that, they need technology partners like RedCloud. In this conversation, we learn how RedCloud sits at the forefront of the 3 key disruptions inside of emerging markets: employment, technology, and sustainability. Learn how Soumaya Hamzaoui, Co-Founder and COO and JD de Jong, SVP of Product combine data, technology and deep knowledge of emerging economies to help independent sellers.Key Moments: A personal mission: Born in Algeria, Africa, Soumaya discusses how her childhood shaped her understanding of emerging markets.Unlike Amazon: Commerce has existed in emerging markets since the beginning of civilization. Yet independent sellers face existential challenges if big corporations are allowed to enter their markets and gobble up all of the opportunities. Learn how RedCloud hopes to not be like Amazon, and would rather focus on keeping independent sellers independent.Data for emerging market independent sellers: discover how the team developed the right products for these markets and how they overcame challenges unique to their customers.Challenges in emerging markets: Gain unique perspectives into how international marketplaces work. Key Quotes: “We are not only here to develop the technology, but we are also here to educate these businesses on the value of digitization, on the value of data. [According to a World Bank Report] 90% of these businesses need training and upskilling to keep up with the pace of the evolution of how the economical world and technological world is evolving. 88% need support in digitization on how to take their business from traditional businesses to fully digital business. And another 80% need mentorship and support on how to transform their business. - Soumaya“It is one thing to give a user access to create their own visualization. It's an entirely different thing to create not just the visualization, but an interpretation of what that visualization means. - JD“When companies look at emerging markets and the lack of digitization, they think there's a reluctance to digitize and there really isn't. It's not about the adoption of technology, but the simplification of and the cost of that technology.” - JDMentions: World Bank ReportsM-Pesa in KenyaLarge Language Models (LLMS) with DialectsMarketplace Counterfeit ChallengesBook: The Subtle Art of Not Giving a F*ck: A Counterintuitive Approach to Living a Good LifeBios: Soumaya Hamzaoui describes herself as an Entrepreneur and Product Strategist. She has a strong track record of developing products across Enterprises focused on the fintech and commerce global industries. She has deep sector expertise built over the last 15 years across Africa, Asia, and EMEA in mobile money, digital financial services, and FinTech launches. She attended prestigious universities in France and Algeria.Juandre (JD) de Jong is a seasoned Product professional and Chartered Management Accountant, currently serving as the Senior Vice President of Product at RedCloud. Juandre combines his financial acumen with a deep understanding of customer needs to drive product strategy and innovation. He has a proven track record of successfully launching and scaling innovative products that meet market demands. He was born in South Africa and currently resides in the UK. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Summary Maintaining a single source of truth for your data is the biggest challenge in data engineering. Different roles and tasks in the business need their own ways to access and analyze the data in the organization. In order to enable this use case, while maintaining a single point of access, the semantic layer has evolved as a technological solution to the problem. In this episode Artyom Keydunov, creator of Cube, discusses the evolution and applications of the semantic layer as a component of your data platform, and how Cube provides speed and cost optimization for your data consumers. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management This episode is brought to you by Datafold – a testing automation platform for data engineers that prevents data quality issues from entering every part of your data workflow, from migration to dbt deployment. Datafold has recently launched data replication testing, providing ongoing validation for source-to-target replication. Leverage Datafold's fast cross-database data diffing and Monitoring to test your replication pipelines automatically and continuously. Validate consistency between source and target at any scale, and receive alerts about any discrepancies. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold (https://www.dataengineeringpodcast.com/datafold). Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster (https://www.dataengineeringpodcast.com/dagster) today to get started. Your first 30 days are free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Artyom Keydunov about the role of the semantic layer in your data platform Interview Introduction How did you get involved in the area of data management? Can you start by outlining the technical elements of what it means to have a "semantic layer"? In the past couple of years there was a rapid hype cycle around the "metrics layer" and "headless BI", which has largely faded. Can you give your assessment of the current state of the industry around the adoption/implementation of these concepts? What are the benefits of having a discrete service that offers the business metrics/semantic mappings as opposed to implementing those concepts as part of a more general system? (e.g. dbt, BI, warehouse marts, etc.) At what point does it become necessary/beneficial for a team to adopt such a service? What are the challenges involved in retrofitting a semantic layer into a production data system? evolution of requirements/usage patterns technical complexities/performance and cost optimization What are the most interesting, innovative, or unexpected ways that you have seen Cube used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Cube? When is Cube/a semantic layer the wrong choice? What do you have planned for the future of Cube? Contact Info LinkedIn (https://www.linkedin.com/in/keydunov/) keydunov (https://github.com/keydunov) on GitHub Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. Links Cube (https://cube.dev/) Semantic Layer (https://en.wikipedia.org/wiki/Semantic_layer) Business Objects (https://en.wikipedia.org/wiki/BusinessObjects) Tableau (https://www.tableau.com/) Looker (https://cloud.google.com/looker/?hl=en) Podcast Episode (https://www.dataengineeringpodcast.com/looker-with-daniel-mintz-episode-55/) Mode (https://mode.com/) Thoughtspot (https://www.thoughtspot.com/) LightDash (https://www.lightdash.com/) Podcast Episode (https://www.dataengineeringpodcast.com/lightdash-exploratory-business-intelligence-episode-232/) Embedded Analytics (https://en.wikipedia.org/wiki/Embedded_analytics) Dimensional Modeling (https://en.wikipedia.org/wiki/Dimensional_modeling) Clickhouse (https://clickhouse.com/) Podcast Episode (https://www.dataengineeringpodcast.com/clickhouse-data-warehouse-episode-88/) Druid (https://druid.apache.org/) BigQuery (https://cloud.google.com/bigquery?hl=en) Starburst (https://www.starburst.io/) Pinot (https://pinot.apache.org/) Snowflake (https://www.snowflake.com/en/) Podcast Episode (https://www.dataengineeringpodcast.com/snowflakedb-cloud-data-warehouse-episode-110/) Arrow Datafusion (https://arrow.apache.org/datafusion/) Metabase (https://www.metabase.com/) Podcast Episode (https://www.dataengineeringpodcast.com/metabase-with-sameer-al-sakran-episode-29) Superset (https://superset.apache.org/) Alation (https://www.alation.com/) Collibra (https://www.collibra.com/) Podcast Episode (https://www.dataengineeringpodcast.com/collibra-enterprise-data-governance-episode-188) Atlan (https://atlan.com/) Podcast Episode (https://www.dataengineeringpodcast.com/atlan-data-team-collaboration-episode-179) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

Description: In this episode, Chris Stephens, Field CTO at Appen, dives into how CDO's are navigating the world of generative AI. From setting clear expectations to driving adoption within organizations, Chris and Cindi explore the challenges and opportunities in this evolving landscape. Chris shares Appen's innovative approach to integrating humans into deep learning processes and discusses the potential of synthetic data. Plus, he shares how crucial human expertise is, in shaping ethical AI practices and touches on the impact of legislation and industry trends on AI's future.Key Moments: The Impact of generative AI on CDOs  [06:19]Appen and the excitement of generative AI [10:34]The potential of synthetic data and content curation [12:13]The importance of CDOs embracing generative AI [17:40]The early stage of generative AI and funding innovation [26:39]The importance of human in the loop [34:09]The role of legislation and industry leadership [38:46]Key Quotes: As a CDO, I think you absolutely have to figure out how to grab onto that, take ownership of it, and provide the leadership that your company needs - if you don't, then of course someone else will.  All of the challenges come on the non-technical side. Being successful in these programs is about more humanistic type skills than it is being a wizard in the technology space, in my opinion.The work that Appen does is working in support of all of these global organizations and the key is getting humans involved in these loops. Mentions: Deep learningDeep fake Synthetic data Gartner Generative AI Bio: Chris has been leading large-scale data transformations for over a decade, bringing advanced analytics capabilities to the world for 25 years.  Most recently, he served in CDO roles at GEICO, Zendesk, and American Eagle Outfitters.  Prior to that he helped lead the Data Science practice at Pivotal Software helping organizations around the world adopt modern data and software practices.  He is Field CTO and Head of AI Solutions at Appen bringing AI systems to life for organizations around the world.  He is an advisor to Insight Partners and Battery Ventures helping shape a new generation of technology and teams.  He is Adjunct Faculty at Carnegie Mellon University teaching our next generation of data and AI leaders.   He is passionate about the human side of data, transformation, and innovation.  He hails from Pittsburgh with his wife and 5 young adult children.  An avid music fan, he reminds us that, "you who choose to lead must follow."Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

In an era where artificial intelligence (AI) is rapidly being integrated into every facet of technology, the line between innovation and overreach becomes increasingly blurred. In this episode of Tech Talks Daily, we are joined by Benn Stancil, ThoughtSpot's Field CTO, to delve into a provocative prediction for 2024: the potential of AI to make products worse, not better. As companies race to imbue their offerings with AI, the rush towards "smart" products often overlooks a fundamental principle: the distinction between creating a product and creating a good product. Benn argues that while AI has the power to transform, this transformation will not always be for the better.  The conversation will explore the nuances of AI in product development, emphasizing the importance of starting with real customer problems, thinking beyond the conventional applications of AI, and the necessity of substantial investment for genuine results. ThoughtSpot stands at the forefront of this discussion as an AI-Powered Analytics company dedicated to making the world more fact-driven through an intuitive analytics platform. With its emphasis on natural language search and the ability to generate actionable insights from complex data, ThoughtSpot exemplifies how to navigate the AI paradox successfully. This episode promises to unpack the complexities of integrating AI into products without falling into the trap of overselling and underdelivering. We'll cover how ThoughtSpot has managed to sidestep common pitfalls in AI development by focusing on actual customer needs, maintaining transparency about product capabilities, and integrating AI into workflows in a way that truly adds value. Join us as Benn Stancil takes us through the intricacies of AI's role in product development, offering insights into how businesses can leverage AI responsibly to enhance, rather than complicate, user experiences. Whether you're a tech enthusiast, a business leader, or someone curious about the future of AI, this episode will provide a critical perspective on the challenges and opportunities that lie ahead in the quest to build products that genuinely improve our lives.    

Giving ABC's more meaning, in this episode of The Data Chief host Cindi Howson, engages in a captivating conversation with Nisha Paliwal, the managing VP of Enterprise Data Technology at Capital One. Nisha dives into her multifaceted role as a tech leader, visionary, and advocate for STEM education. The discussion traverses topics ranging from the impact of technology on younger generations to the future of work in the era of AI. With insights into Nisha's unique ABC leadership framework and Capital One's innovative culture, this episode offers a rich exploration of data leadership and human-centered tech strategy. Key Moments:The impact of technology on younger generations [3:05]Nisha's leadership style: The ABC Framework [5:02]The future of work and AI's impact [15:56]Mitigating risks and building trust in AI [20:20]The importance of data in AI [25:22]Capital One's culture of innovation [31:18] Key Quotes:“Let's just be who we are and bring the best in others too. So all the people who I work with, bring their best self to work and are comfortable with whoever they want to be.” “I think AI might not be for everybody to start with. I think it's okay to wait and watch. I think it's okay to let it bake because again, these things are not cheap either, right? These require a lot of investment upfront.” “Data is the king these days, we have a lot of investment in data, we have about 1000 plus people and I'm here to serve them, to serve the organization, serve our product – I care about what we build.” Mentions:Databricks IBMHyperautomationGenerative AIThe Secrets of AI Value Creation Bio:Nisha Paliwal is Managing Vice President of Enterprise Data Technology at Capital One, where she has held a variety of leadership roles over more than eight years. An accomplished leader, visionary technologist, and passionate change agent, she has been a relentless advocate for leveraging technology and data insights to create true business value for more than 20 years. At Capital One, she also actively contributes to and holds leadership roles in the Women in Tech and Origins business resource groups (BRGs). Nisha has a big heart for her associates and desires for them to feel valued, engaged and psychologically safe. With a passion for introducing young girls to technology, she also mentors others and supports several STEM-focused non profits, with a long-term vision of bringing more women into the ranks of technology leadership. Nisha volunteers her personal time with three non-profits - Boolean Girls, CodeVa, and WingsForGrowth, which focus on STEM education for K-12 and education for women in leadership-related topics.Nisha is an avid learner who made the jump from microbiology to the tech world after teaching herself C# programming. She continues her life-long pursuit of learning by reading, listening to podcasts, and participating in internal and external speaking engagements.Nisha has recently co-authored a book “The Secrets of AI Value Creation,” published by Wiley, in her pursuit of learning and sharing those learnings with the community in the form of this book. Order Nisha's new book, The Secrets of AI Value Creation now. Hear more from Cindi Howson here. Sponsored by ThoughtSpot. 

Prepare to unlock the secrets of successful data infrastructure investment with the guidance of Ryan Wexler, VP at Unusual Ventures. Transitioning from the meticulous realm of data engineering right into the heart of venture capitalism, Ryan offers an unparalleled perspective on pinpointing the most promising data companies. This episode is a treasure trove of insights, where we uncover the critical ingredients that elevate a startup from a mere niche player to a scalable powerhouse in the competitive data sector, all thanks to the strategic support Unusual Ventures provides.As we navigate the intricate evolution of the modern data stack, it becomes clear that while data warehouses once lured enterprises with their cost-effectiveness, burgeoning scales of operation have led to some sleepless nights over soaring expenses. This is where our discussion takes a turn into the groundbreaking realm of data lakes and independent storage solutions – the silent disruptors offering a respite by decoupling storage from compute costs. Listen in to understand how businesses are strategizing to harness these technologies for optimized data management, marking a seismic shift in the tech landscape.And then there's the undeniable surge of AI – a tidal wave of innovation that's transforming the face of industry after industry. This episode peeks behind the curtain of AI integration, highlighting how trailblazers like Druva and ThoughtSpot are embedding AI to revolutionize their offerings. As we dissect the proliferation of AI tools, our dialogue serves as a compass for startups and enterprises alike, emphasizing the importance of a laser focus on ROI and the wisdom of keeping those burn rates low amidst an ever-changing economic backdrop. Join us for a journey that not only demystifies the complexities of data ROI but also navigates the myriad choices in the expanding universe of AI adoption.What's New In Data is a data thought leadership series hosted by John Kutay who leads data and products at Striim. What's New In Data hosts industry practitioners to discuss latest trends, common patterns for real world data patterns, and analytics success stories.

JP Bolen is the VP of Global Sales at Rubrik. He has a wealth of experience in sales and sales leadership, having worked at various companies such as Wallace Computer, PTC, Primo, Blade Logic, BMC Software, Dynamic Ops, VMware, ClearSlide, MongoDB, and ThoughtSpot.In this episode, JP emphasizes the importance of sales enablement and the three types of training: onboarding, ongoing, and field training. He shares his experience in implementing a comprehensive onboarding program at Rubrik, which includes interactive classes and real-life scenarios to help new hires become conversationally fluent in the problems Rubrik solves. JP also explains the concept of "winning the stage" in the sales process and how Rubrik measures conversion rates between different stages to identify areas for improvement. The conversation also touches on the significance of having a compelling point of view (POV) when engaging with customers and the role of continuous learning in sales success.Tune in to this conversation with John McMahon and John Kaplan on the Revenue Builders podcast.HERE ARE SOME KEY SECTIONS TO CHECK OUT[00:08:43] JP's initial perception of enablement and his transition into the role[00:09:50] Introduction to Rubrik's onboarding training challenges[00:13:00] A deconstructed approach to onboarding was implemented for better learning.[00:15:59] Onboarding is structured into three tracks for scalability and continuous learning.[00:18:04] The first track focuses on value-based conversations and messaging.[00:19:41] Importance of leaders following up and providing support[00:25:12] Empathy, listening, questioning, and curiosity in discovery[00:28:30] "Change the Game" initiative to drive mindset shift[00:37:39] Stage 1: Identifying pain, stakeholders, quantifying pain, developing champions[00:39:18] Stage 2: Creating a plan, technical validation, financial conversation[00:40:23] Importance of stage 1 and finding the real champion[00:43:12] Focus on understanding how deals got to their current stage[00:44:08] Importance of quantifying pain and understanding why they have to buy[00:45:17] Difficulty in conversion between stage 1 and 2[00:46:15] Implementing friction and champion go/no-go in stage 1 and 2[00:48:00] The importance of answering the 4 essential questions for success[00:49:17] Example of adding information to the framework[00:51:11] Initial challenges faced and the need for a common framework[00:53:55] Training reps to go into accounts with a compelling point of viewADDITIONAL RESOURCESLearn more about JP Bolen:https://www.linkedin.com/in/jpbolen/Download our Sales Transformation Guide for Leaders: https://forc.mx/3sdtEZJHIGHLIGHT QUOTES[00:05:11] “The single most important metric inside a company is sales productivity. You have quota, a product, and how well you perform against the quota. The variables and leverage points are obviously who you hire because that's going to dictate a lot. But once you get them in, it's how quickly they can actually understand the customer's problems, the things that customers are going through, the pains they have, and how well you differently address those problems and become productive with the skills and execution.”[00:57:10] “I remember you and I spoke a lot before I took this role. I talked to Grant Wilson a lot. At one point, I was talking to Grant about the things that we were doing. I was explaining to save the data. I was explaining these different pieces. And he said, ‘You're not enabling, like you're not just enabling, you're transforming, you're doing sales transformation inside of the company.'”

Cindi Howson, Chief Data Strategy Officer at ThoughtSpot joins me in an enlightening episode of the Tech Talks Daily Podcast. With a career spanning over three decades in the analytics space, Cindi brings a wealth of knowledge and insight into the evolving world of business intelligence (BI) and the revolutionary impact of generative AI. In this episode, we delve into the transformative effects of generative AI on BI, discussing the significant advancements since the advent of OpenAI's GPT 3.5. They explore the nuanced shifts in data team roles and the emergence of new specializations, underscoring the importance of adapting to a rapidly changing landscape. The conversation takes a deep dive into the critical role of data literacy in businesses today. Cindi addresses the future of AI-powered analytics and the sectors poised for disruption. They don't shy away from the tough questions, tackling the ethical concerns of AI's integration into our daily decision-making processes and advocating for a balance of innovation with transparency and trust. As ThoughtSpot's mission to democratize data takes center stage,we also discuss the hurdles of cultural change within organizations. They emphasize the importance of design in crafting engaging and user-friendly data experiences, making analytics not just accessible but enjoyable for all users, regardless of technical expertise. Beyond the insights, this episode also gives listeners a glimpse into Cindi's illustrious career and her contributions to the field of data and analytics. As a thought leader and advocate for women in tech and AI ethics, Cindi's perspectives are not just informative but also deeply inspiring. Finally, Cindi shares 10 books on data, analytics, and AI that leaders should read in 2023

The first workshops and talks from the AI Engineer Summit are now up! Join the >20k viewers on YouTube, find clips on Twitter (we're also clipping @latentspacepod), and chat with us on Discord!Text-to-SQL was one of the first applications of NLP. Thoughtspot offered “Ask your data questions” as their core differentiation compared to traditional dashboarding tools. In a way, they provide a much friendlier interface with your own structured (aka “tabular”, as in “SQL tables”) data, the same way that RLHF and Instruction Tuning helped turn the GPT-3 of 2020 into the ChatGPT of 2022.Today, natural language queries on your databases are a commodity. There are 4 different ChatGPT plugins that offer this, as well as a bunch of startups like one of our previous guests, Seek.ai. Perplexity originally started with a similar product in 2022: In March 2023 LangChain wrote a blog post on LLMs and SQL highlighting why they don't consistently work:* “LLMs can write SQL, but they are often prone to making up tables, making up field”* “LLMs have some context window which limits the amount of text they can operate over”* “The SQL it writes may be incorrect for whatever reason, or it could be correct but just return an unexpected result.”For example, if you ask a model to “return all active users in the last 7 days” it might hallucinate a `is_active` column, join to an `activity` table that doesn't exist, or potentially get the wrong date (especially in leap years!).We previously talked to Shreya Rajpal at Guardrails AI, which also supports Text2SQL enforcement. Their approach was to run the actual SQL against your database and then use the error messages to improve the query: Semantic Layers to the rescueCube is an open source semantic layer which recently integrated with LangChain to solve these issues in a different way. You can use YAML, Javascript, or Python to create definitions of different metrics, measures and dimensions for your data: Creating these metrics and passing them in the model context limits the possibility for errors as the model just needs to query the `active_users` view, and Cube will then expand that into the full SQL in a reliable way. The downside of this approach compared to the Guardrails one for example is that it requires more upfront work to define metrics, but on the other hand it leads to more reliable and predictable outputs. The promise of adding a great semantic layer to your LLM app is irresistible - you greatly minimize hallucinations, make much more token efficient prompts, and your data stays up to date without any retraining or re-indexing. However, there are also difficulties with implementing semantic layers well, so we were glad to go deep on the topic with Artem as one of the leading players in this space!Timestamps* [00:00:00] Introductions* [00:01:28] Statsbot and limitations of natural language processing in 2017* [00:04:27] Building Cube as the infrastructure for Statsbot* [00:08:01] Open sourcing Cube in 2019* [00:09:09] Explaining the concept of a semantic layer/Cube* [00:11:01] Using semantic layers to provide context for AI models working with tabular data* [00:14:47] Workflow of generating queries from natural language via semantic layer* [00:21:07] Using Cube to power customer-facing analytics and natural language interfaces* [00:22:38] Building data-driven AI applications and agents* [00:25:59] The future of the modern data stack* [00:29:43] Example use cases of Slack bots powered by Cube* [00:30:59] Using GPT models and limitations around math* [00:32:44] Tips for building data-driven AI apps* [00:35:20] Challenges around monetizing embedded analytics* [00:36:27] Lightning RoundTranscriptSwyx: Hey everyone, welcome to the Latent Space podcast. This is Swyx, writer, editor of Latent Space and founder of Smol.ai and Alessio, partner and CTO in residence at Decibel Partners. [00:00:15]Alessio: Hey everyone, and today we have Artem Keydunov on the podcast, co-founder of Cube. Hey Artem. [00:00:21]Artem: Hey Alessio, hi Swyx. Good to be here today, thank you for inviting me. [00:00:25]Alessio: Yeah, thanks for joining. For people that don't know, I've known Artem for a long time, ever since he started Cube. And Cube is actually a spin-out of his previous company, which is Statsbot. And this kind of feels like going both backward and forward in time. So the premise of Statsbot was having a Slack bot that you can ask, basically like text to SQL in Slack, and this was six, seven years ago, something like that. A lot ahead of its time, and you see startups trying to do that today. And then Cube came out of that as a part of the infrastructure that was powering Statsbot. And Cube then evolved from an embedded analytics product to the semantic layer and just an awesome open source evolution. I think you have over 16,000 stars on GitHub today, you have a very active open source community. But maybe for people at home, just give a quick like lay of the land of the original Statsbot product. You know, what got you interested in like text to SQL and what were some of the limitations that you saw then, the limitations that you're also seeing today in the new landscape? [00:01:28]Artem: I started Statsbot in 2016. The original idea was to just make sort of a side project based off my initial project that I did at a company that I was working for back then. And I was working for a company that was building software for schools, and we were using Slack a lot. And Slack was growing really fast, a lot of people were talking about Slack, you know, like Slack apps, chatbots in general. So I think it was, you know, like another wave of, you know, bots and all that. We have one more wave right now, but it always comes in waves. So we were like living through one of those waves. And I wanted to build a bot that would give me information from different places where like a data lives to Slack. So it was like developer data, like New Relic, maybe some marketing data, Google Analytics, and then some just regular data, like a production database, so it sells for sometimes. And I wanted to bring it all into Slack, because we were always chatting, you know, like in Slack, and I wanted to see some stats in Slack. So that was the idea of Statsbot, right, like bring stats to Slack. I built that as a, you know, like a first sort of a side project, and I published it on Reddit. And people started to use it even before Slack came up with that Slack application directory. So it was a little, you know, like a hackish way to install it, but people are still installing it. So it was a lot of fun. And then Slack kind of came up with that application directory, and they reached out to me and they wanted to feature Statsbot, because it was one of the already being kind of widely used bots on Slack. So they featured me on this application directory front page, and I just got a lot of, you know, like new users signing up for that. It was a lot of fun, I think, you know, like, but it was sort of a big limitation in terms of how you can process natural language, because the original idea was to let people ask questions directly in Slack, right, hey, show me my, you know, like opportunities closed last week or something like that. My co founder, who kind of started helping me with this Slack application, him and I were trying to build a system to recognize that natural language. But it was, you know, we didn't have LLMs right back then and all of that technology. So it was really hard to build the system, especially the systems that can kind of, you know, like keep talking to you, like maintain some sort of a dialogue. It was a lot of like one off requests, and like, it was a lot of hit and miss, right? If you know how to construct a query in natural language, you will get a result back. But you know, like, it was not a system that was capable of, you know, like asking follow up questions to try to understand what you actually want. And then kind of finally, you know, like, bring this all context and go to generate a SQL query, get the result back and all of that. So that was a really missing part. And I think right now, that's, you know, like, what is the difference? So right now, I kind of bullish that if I would start Statsbot again, probably would have a much better shot at it. But back then, that was a big limitation. We kind of build a queue, right, as we were working on Statsbot, because we needed it. [00:04:27]Alessio: What was the ML stack at the time? Were you building, trying to build your own natural language understanding models, like were there open source models that were good that you were trying to leverage? [00:04:38]Artem: I think it was mostly combination of a bunch of things. And we tried a lot of different approaches. The first version, which I built, like was Regex. They were working well. [00:04:47]Swyx: It's the same as I did, I did option pricing when I was in finance, and I had a natural language pricing tool thing. And it was Regex. It was just a lot of Regex. [00:04:59]Artem: Yeah. [00:05:00]Artem: And my co-founder, Pavel, he's much smarter than I am. He's like PhD in math, all of that. And he started to do some stuff. I was like, no, you just do that stuff. I don't know. I can do Regex. And he started to do some models and trying to either look at what we had on the market back then, or try to build a different sort of models. Again, we didn't have any foundation back in place, right? We wanted to try to use existing math, obviously, right? But it was not something that we can take the model and try and run it. I think in 2019, we started to see more of stuff, like ecosystem being built, and then it eventually kind of resulted in all this LLM, like what we have right now. But back then in 2016, it was not much available for just the people to build on top. It was some academic research, right, kind of been happening. But it was very, very early for something to actually be able to use. [00:05:58]Alessio: And then that became Cube, which started just as an open source project. And I think I remember going on a walk with you in San Mateo in 2020, something like that. And you had people reaching out to you who were like, hey, we use Cube in production. I just need to give you some money, even though you guys are not a company. What's the story of Cube then from Statsbot to where you are today? [00:06:21]Artem: We built a Cube at Statsbot because we needed it. It was like, the whole Statsbot stack was that we first tried to translate the initial sort of language query into some sort of multidimensional query. It's like we were trying to understand, okay, people wanted to get active opportunities, right? What does it mean? Is it a metric? Is it what a dimension here? Because usually in analytics, you always, you know, like, try to reduce everything down to the sort of, you know, like a multidimensional framework. So that was the first step. And that's where, you know, like it didn't really work well because all this limitation of us not having foundational technologies. But then from the multidimensional query, we wanted to go to SQL. And that's what was SemanticLayer and what was Cube essentially. So we built a framework where you would be able to map your data into this concept, into this metrics. Because when people were coming to Statsbot, they were bringing their own datasets, right? And the big question was, how do we tell the system what is active opportunities for that specific users? How we kind of, you know, like provide that context, how we do the training. So that's why we came up with the idea of building the SemanticLayer so people can actually define their metrics and then kind of use them as a Statsbot. So that's how we built a Cube. At some point, we saw people started to see more value in the Cube itself, you know, like kind of building the SemanticLayer and then using it to power different types of the application. So in 2019, we decided, okay, it feels like it might be a standalone product and a lot of people want to use it. Let's just try to open source it. So we took it out of Statsbot and open-sourced. [00:08:01]Swyx: Can I make sure that everyone has the same foundational knowledge? The concept of a cube is not something that you invented. I think, you know, not everyone has the same background in analytics and data that all three of us do. Maybe you want to explain like OLAP Cube, HyperCube, the brief history of cubes. Right. [00:08:17]Artem: I'll try, you know, like a lot of like Wikipedia pages and like a lot of like a blog post trying to go into academics of it. So I'm trying to like... [00:08:25]Swyx: Cube's according to you. Yeah. [00:08:27]Artem: So when we think about just a table in a database, the problem with the table, it's not a multidimensional, meaning that in many cases, if we want to slice the data, we kind of need to result with a different table, right? Like think about when you're writing a SQL query to answer one question, SQL query always ends up with a table, right? So you write one SQL, you got one. And then you write to answer a different question, you write a second query. So you're kind of getting a bunch of tables. So now let's imagine that we can kind of bring all these tables together into multidimensional table. And that's essentially Cube. So it's just like the way that we can have measures and dimension that can potentially be used at the same time from a different angles. [00:09:09]Alessio: So initially, a lot of your use cases were more BI related, but you recently released a LangChain integration. There's obviously more and more interest in, again, using these models to answer data questions. So you've seen the chat GPT code interpreter, which is renamed as like advanced data analysis. What's kind of like the future of like the semantic layer in AI? You know, what are like some of the use cases that you're seeing and why do you think it's a good strategy to make it easier to do now the text to SQL you wanted to do seven years ago? [00:09:39]Artem: Yeah. So, I mean, you know, when it started to happen, I was just like, oh my God, people are now building Statsbot with Cube. They just have a better technology for, you know, like natural language. So it kind of, it made sense to me, you know, like from the first moment I saw it. So I think it's something that, you know, like happening right now and chat bot is one of the use cases. I think, you know, like if you try to generalize it, the use case would be how do we use structured or tabular data with, you know, like AI models, right? Like how do we turn the data and give the context as a data and then bring it to the model and then model can, you know, like give you answers, make a questions, do whatever you want. But the question is like how we go from just the data in your data warehouse, database, whatever, which is usually just a tabular data, right? Like in a SQL based warehouses to some sort of, you know, like a context that system can do. And if you're building this application, you have to do it. It's like no way you can get away around not doing this. You either map it manually or you come up with some framework or something else. So our take is that and my take is that semantic layer is just really good place for this context to leave because you need to give this context to the humans. You need to give that context to the AI system anyway, right? So that's why you define metric once and then, you know, like you teach your AI system what this metric is about. [00:11:01]Alessio: What are some of the challenges of using tabular versus language data and some of the ways that having the semantic layer kind of makes that easier maybe? [00:11:09]Artem: Imagine you're a human, right? And you're going into like your new data analyst at a company and just people give you a warehouse with a bunch of tables and they tell you, okay, just try to make sense of this data. And you're going through all of these tables and you're really like trying to make sense without any, you know, like additional context or like some columns. In many cases, they might have a weird names. Sometimes, you know, if they follow some kind of like a star schema or, you know, like a Kimball style dimensions, maybe that would be easier because you would have facts and dimensions column, but it's still, it's hard to understand and kind of make sense because it doesn't have descriptions, right? And then there is like a whole like industry of like a data catalogs exist because the whole purpose of that to give context to the data so people can understand that. And I think the same applies to the AI, right? Like, and the same challenge is that if you give it pure tabular data, it doesn't have this sort of context that it can read. So you sort of needed to write a book or like essay about your data and give that book to the system so it can understand it. [00:12:12]Alessio: Can you run through the steps of how that works today? So the initial part is like the natural language query, like what are the steps that happen in between to do model, to semantic layer, semantic layer, to SQL and all that flow? [00:12:26]Artem: The first key step is to do some sort of indexing. That's what I was referring to, like write a book about your data, right? Describe in a text format what your data is about, right? Like what metrics it has, dimensions, what is the structures of that, what a relationship between those metrics, what are potential values of the dimensions. So sort of, you know, like build a really good index as a text representation and then turn it into embeddings into your, you know, like a vector storage. Once you have that, then you can provide that as a context to the model. I mean, there are like a lot of options, like either fine tune or, you know, like sort of in context learning, but somehow kind of give that as a context to the model, right? And then once this model has this context, it can create a query. Now the query I believe should be created against semantic layer because it reduces the room for the error. Because what usually happens is that your query to semantic layer would be very simple. It would be like, give me that metric group by that dimension and maybe that filter should be applied. And then your real query for the warehouse, it might have like a five joins, a lot of different techniques, like how to avoid fan out, fan traps, chasm traps, all of that stuff. And the bigger query, the more room that the model can make an error, right? Like even sometimes it could be a small error and then, you know, like your numbers is going to be off. But making a query against semantic layer, that sort of reduces the error. So the model generates a SQL query and then it executes us again, semantic layer. And semantic layer executes us against your warehouse and then sends result all the way back to the application. And then can be done multiple times because what we were missing was both this ability to have a conversation, right? With the model. You can ask question and then system can do a follow-up questions, you know, like then do a query to get some additional information based on this information, do a query again. And sort of, you know, like it can keep doing this stuff and then eventually maybe give you a big report that consists of a lot of like data points. But the whole flow is that it knows the system, it knows your data because you already kind of did the indexing and then it queries semantic layer instead of a data warehouse directly. [00:14:47]Alessio: Maybe just to make it a little clearer for people that haven't used a semantic layer before, you can add definitions like revenue, where revenue is like select from customers and like join orders and then sum of the amount of orders. But in the semantic layer, you're kind of hiding all of that away. So when you do natural language to queue, it just select revenue from last week and then it turns into a bigger query. [00:15:12]Swyx: One of the biggest difficulties around semantic layer for people who've never thought about this concept before, this all sounds super neat until you have multiple stakeholders within a single company who all have different concepts of what a revenue is. They all have different concepts of what active user is. And then they'll have like, you know, revenue revision one by the sales team, you know, and then revenue revision one, accounting team or tax team, I don't know. I feel like I always want semantic layer discussions to talk about the not so pretty parts of the semantic layer, because this is where effectively you ship your org chart in the semantic layer. [00:15:47]Artem: I think the way I think about it is that at the end of the day, semantic layer is a code base. And in Qubit, it's essentially a code base, right? It's not just a set of YAML files with pythons. I think code is never perfect, right? It's never going to be perfect. It will have a lot of, you know, like revisions of code. We have a version control, which helps it's easier with revisions. So I think we should treat our metrics and semantic layer as a code, right? And then collaboration is a big part of it. You know, like if there are like multiple teams that sort of have a different opinions, let them collaborate on the pull request, you know, they can discuss that, like why they think that should be calculated differently, have an open conversation about it, you know, like when everyone can just discuss it, like an open source community, right? Like you go on a GitHub and you talk about why that code is written the way it's written, right? It should be written differently. And then hopefully at some point you can come up, you know, like to some definition. Now if you still should have multiple versions, right? It's a code, right? You can still manage it. But I think the big part of that is that like, we really need to treat it as a code base. Then it makes a lot of things easier, not as spreadsheets, you know, like a hidden Excel files. [00:16:53]Alessio: The other thing is like then having the definition spread in the organization, like versus everybody trying to come up with their own thing. But yeah, I'm sure that when you talk to customers, there's people that have issues with the product and it's really like two people trying to define the same thing. One in sales that wants to look good, the other is like the finance team that wants to be conservative and they all have different definitions. How important is the natural language to people? Obviously you guys both work in modern data stack companies either now or before. There's going to be the whole wave of empowering data professionals. I think now a big part of the wave is removing the need for data professionals to always be in the loop and having non-technical folks do more of the work. Are you seeing that as a big push too with these models, like allowing everybody to interact with the data? [00:17:42]Artem: I think it's a multidimensional question. That's an example of, you know, like where you have a lot of inside the question. In terms of examples, I think a lot of people building different, you know, like agents or chatbots. You have a company that built an internal Slack bot that sort of answers questions, you know, like based on the data in a warehouse. And then like a lot of people kind of go in and like ask that chatbot this question. Is it like a real big use case? Maybe. Is it still like a toy pet project? Maybe too right now. I think it's really hard to tell them apart at this point because there is a lot of like a hype, you know, and just people building LLM stuff because it's cool and everyone wants to build something, you know, like even at least a pet project. So that's what happened in Krizawa community as well. We see a lot of like people building a lot of cool stuff and it probably will take some time for that stuff to mature and kind of to see like what are real, the best use cases. But I think what I saw so far, one use case was building this chatbot and we have even one company that are building it as a service. So they essentially connect into Q semantic layer and then offering their like chatbot So you can do it in a web, in a slack, so it can, you know, like answer questions based on data in your semantic layer, but also see a lot of things like they're just being built in house. And there are other use cases, sort of automation, you know, like that agent checks on the data and then kind of perform some actions based, you know, like on changes in data. But other dimension of your question is like, will it replace people or not? I think, you know, like what I see so far in data specifically, you know, like a few use cases of LLM, I don't see Q being part of that use case, but it's more like a copilot for data analyst, a copilot for data engineer, where you develop something, you develop a model and it can help you to write a SQL or something like that. So you know, it can create a boilerplate SQL, and then you can edit this SQL, which is fine because you know how to edit SQL, right? So you're not going to make a mistake, but it will help you to just generate, you know, like a bunch of SQL that you write again and again, right? Like boilerplate code. So sort of a copilot use case. I think that's great. And we'll see more of it. I think every platform that is building for data engineers will have some sort of a copilot capabilities and Cubectl, we're building this copilot capabilities to help people build semantic layers easier. I think that just a baseline for every engineering product right now to have some sort of, you know, like a copilot capabilities. Then the other use case is a little bit more where Cube is being involved is like, how do we enable access to data for non-technical people through the natural language as an interface to data, right? Like visual dashboards, charts, it's always has been an interface to data in every BI. Now I think we will see just a second interface as a just kind of a natural language. So I think at this point, many BI's will add it as a commodity feature is like Tableau will probably have a search bar at some point saying like, Hey, ask me a question. I know that some of the, you know, like AWS Squeak site, they're about to announce features like this in their like BI. And I think Power BI will do that, especially with their deal with open AI. So every company, every BI will have this some sort of a search capabilities built in inside their BI. So I think that's just going to be a baseline feature for them as well. But that's where Cube can help because we can provide that context, right? [00:21:07]Alessio: Do you know how, or do you have an idea for how these products will differentiate once you get the same interface? So right now there's like, you know, Tableau is like the super complicated and it's like super sad. It's like easier. Yeah. Do you just see everything will look the same and then how do people differentiate? [00:21:24]Artem: It's like they all have line chart, right? And they all have bar chart. I feel like it pretty much the same and it's going to be fragmented as well. And every major vendor and most of the vendors will try to have some sort of natural language capabilities and they might be a little bit different. Some of them will try to position the whole product around it. Some of them will just have them as a checkbox, right? So we'll see, but I don't think it's going to be something that will change the BI market, you know, like something that will can take the BI market and make it more consolidated rather than, you know, like what we have right now. I think it's still will remain fragmented. [00:22:04]Alessio: Let's talk a bit more about application use cases. So people also use Q for kind of like analytics in their product, like dashboards and things like that. How do you see that changing and more, especially like when it comes to like agents, you know, so there's like a lot of people trying to build agents for reporting, building agents for sales. If you're building a sales agent, you need to know everything about the purchasing history of the customer. All of these things. Yeah. Any thoughts there? What should all the AI engineers listening think about when implementing data into agents? [00:22:38]Artem: Yeah, I think kind of, you know, like trying to solve for two problems. One is how to make sure that agents or LLM model, right, has enough context about, you know, like a tabular data and also, you know, like how do we deliver updates to the context, which is also important because data is changing, right? So every time we change something upstream, we need to surely update that context in our vector database or something. And how do you make sure that the queries are correct? You know, I think it's obviously a big pain and that's all, you know, like AI kind of, you know, like a space right now, how do we make sure that we don't, you know, provide our own cancers, but I think, you know, like be able to reduce the room for error as much as possible that what I would look for, you know, like to try to like minimize potential damage. And then our use case for Qube, it's been using a lot to power sort of customer facing analytics. So I don't think much is going to change is that I feel like again, more and more products will adopt natural language interfaces as sort of a part of that product as well. So we would be able to power this business to not only, you know, like a chart, visuals, but also some sort of, you know, like a summaries, probably in the future, you're going to open the page with some surface stats and you will have a smart summary kind of generated by AI. And that summary can be powered by Qube, right, like, because the rest is already being powered by Qube. [00:24:04]Alessio: You know, we had Linus from Notion on the pod and one of the ideas he had that I really like is kind of like thumbnails of text, kind of like how do you like compress knowledge and then start to expand it. A lot of that comes into dashboards, you know, where like you have a lot of data, you have like a lot of charts and sometimes you just want to know, hey, this is like the three lines summary of it. [00:24:25]Artem: Exactly. [00:24:26]Alessio: Makes sense that you want to power that. How are you thinking about, yeah, the evolution of like the modern data stack in quotes, whatever that means today. What's like the future of what people are going to do? What's the future of like what models and agents are going to do for them? Do you have any, any thoughts? [00:24:42]Artem: I feel like modern data stack sometimes is not very, I mean, it's obviously big crossover between AI, you know, like ecosystem, AI infrastructure, ecosystem, and then sort of a data. But I don't think it's a full overlap. So I feel like when we know, like I'm looking at a lot of like what's happening in a modern data stack where like we use warehouses, we use BI's, you know, different like transformation tools, catalogs, like data quality tools, ETLs, all of that. I don't see a lot of being compacted by AI specifically. I think, you know, that space is being compacted as much as any other space in terms of, yes, we'll have all this copilot capabilities, some of AI capabilities here and there, but I don't see anything sort of dramatically, you know, being sort of, you know, a change or shifted because of, you know, like AI wave. In terms of just in general data space, I think in the last two, three years, we saw an explosion, right? Like we got like a lot of tools, every vendor for every problem. I feel like right now we should go through the cycle of consolidation. If Fivetran and DBT merge, they can be Alteryx of a new generation or something like that. And you know, probably some ETL tool there. I feel it might happen. I mean, it's just natural waves, you know, like in cycles. [00:25:59]Alessio: I wonder if everybody is going to have their own copilot. The other thing I think about these models is like Swyx was at Airbyte and yeah, there's Fivetran. [00:26:08]Swyx: Fivetran versus AirByte, I don't think it'll mix very well. [00:26:10]Alessio: A lot of times these companies are doing the syntax work for you of like building the integration between your data store and like the app or another data store. I feel like now these models are pretty good at coming up with the integration themselves and like using the docs to then connect the two. So I'm really curious, like in the future, what that will look like. And same with data transformation. I mean, you think about DBT and some of these tools and right now you have to create rules to normalize and transform data. In the future, I could see you explaining the model, how you want the data to be, and then the model figuring out how to do the transformation. I think it all needs a semantic layer as far as like figuring out what to do with it. You know, what's the data for and where it goes. [00:26:53]Artem: Yeah, I think many of this, you know, like workflows will be augmented by, you know, like some sort of a copilot. You know, you can describe what transformation you want to see and it can generate a boilerplate right, of transformation for you, or even, you know, like kind of generate a boilerplate of specific ETL driver or ETL integration. I think we're still not at the point where this code can be fully automated. So we still need a human and a loop, right, like who can be, who can use this copilot. But in general, I think, yeah, data work and software engineering work can be augmented quite significantly with all that stuff. [00:27:31]Alessio: You know, the big thing with machine learning before was like, well, all of your data is bad. You know, the data is not good for anything. And I think like now, at least with these models, they have some knowledge of their own and they can also tell you if your data is bad, which I think is like something that before you didn't have. Any cool apps that you've seen being built on Qube, like any kind of like AI native things that people should think about, new experiences, anything like that? [00:27:54]Artem: Well, I see a lot of Slack bots. They all remind me of Statsbot, but I know like I played with a few of them. They're much, much better than Statsbot. It feels like it's on the surface, right? It's just that use case that you really want, you know, think about you, a data engineer in your company, like everyone is like, and you're asking, hey, can you pull that data for me? And you would be like, can I build a bot to replace myself? You know, like, so they can both ping that bot instead. So it's like, that's why a lot of people doing that. So I think it's a first use case that actually people are playing with. But I think inside that use case, people get creative. So I see bots that can actually have a dialogue with you. So, you know, like you would come to that bot and say, hey, show me metrics. And the bot would be like, what kind of metrics? What do you want to look at? You will be like active users. And then it would be like, how do you define active users? You want to see active users sort of cohort, you want to see active users kind of changing behavior over time, like a lot of like a follow up questions. So it tries to sort of, you know, like understand what exactly you want. And that's how many data analysts work, right? When people started to ask you something, you always try to understand what exactly do you mean? Because many people don't know how to ask correct questions about your data. It's a sort of an interesting specter. On one side of the specter, you know, nothing is like, hey, show me metrics. And the other side of specter, you know how to write SQL, and you can write exact query to your data warehouse, right? So many people like a little bit in the middle. And the data analysts, they usually have the knowledge about your data. And that's why they can ask follow up questions and to understand what exactly you want. And I saw people building bots who can do that. That part is amazing. I mean, like generating SQL, all that stuff, it's okay, it's good. But when the bot can actually act like they know that your data and they can ask follow up questions. I think that's great. [00:29:43]Swyx: Yeah. [00:29:44]Alessio: Are there any issues with the models and the way they understand numbers? One of the big complaints people have is like GPT, at least 3.5, cannot do math. Have you seen any limitations and improvement? And also when it comes to what model to use, do you see most people use like GPT-4? Because it's like the best at this kind of analysis. [00:30:03]Artem: I think I saw people use all kinds of models. To be honest, it's usually GPT. So inside GPT, it could be 3.5 or 4, right? But it's not like I see a lot of something else, to be honest, like, I mean, maybe some open source alternatives, but it feels like the market is being dominated by just chat GPT. In terms of the problems, I think chatting about it with a few people. So if math is required to do math, you know, like outside of, you know, like chat GPT itself, so it would be like some additional Python scripts or something. When we're talking about production level use cases, it's quite a lot of Python code around, you know, like your model to make it work. To be honest, it's like, it's not that magic that you just throw the model in and like it can give you all these answers. For like a toy use cases, the one we have on a, you know, like our demo page or something, it works fine. But, you know, like if you want to do like a lot of post-processing, do a mass on URL, you probably need to code it in Python anyway. That's what I see people doing. [00:30:59]Alessio: We heard the same from Harrison and LangChain that most people just use OpenAI. We did a OpenAI has no moat emergency podcast, and it was funny to like just see the reaction that people had to that and how hard it actually is to break down some of the monopoly. What else should people keep in mind, Artem? You're kind of like at the cutting edge of this. You know, if I'm looking to build a data-driven AI application, I'm trying to build data into my AI workflows. Any mistakes people should avoid? Any tips on the best stack to use? What tools to use? [00:31:32]Artem: I would just recommend going through to warehouse as soon as possible. I think a lot of people feel that MySQL can be a warehouse, which can be maybe on like a lower scale, but definitely not from a performance perspective. So just kind of starting with a good warehouse, a query engine, Lakehouse, that's probably like something I would recommend starting from a day zero. And there are good ways to do it, very cheap, with open source technologies too, especially in the Lakehouse architecture. I think, you know, I'm biased, obviously, but using a semantic layer, preferably Cube, and for, you know, like a context. And other than that, I just feel it's a very interesting space in terms of AI ecosystem. I see a lot of people using link chain right now, which is great, you know, like, and we build an integration. But I'm sure the space will continue to evolve and, you know, like we'll see a lot of interesting tools and maybe, you know, like some tools would be a better fit for a job. I'm not aware of any right now, but it's always interesting to see how it evolves. Also it's a little unclear, you know, like how all the infrastructure around actually developing, testing, documenting, all that stuff will kind of evolve too. But yeah, again, it's just like really interesting to see and observe, you know, what's happening in this space. [00:32:44]Swyx: So before we go to the lightning round, I wanted to ask you on your thoughts on embedded analytics and in a sense, the kind of chatbots that people are inserting on their websites and building with LLMs is very much sort of end user programming or end user interaction with their own data. I love seeing embedded analytics, and for those who don't know, embedded analytics is basically user facing dashboards where you can see your own data, right? Instead of the company seeing data across all their customers, it's an individual user seeing their own data as a slice of the overall data that is owned by the platform that they're using. So I love embedded analytics. Well, actually, overwhelmingly, the observation that I've had is that people who try to build in this market fail to monetize. And I was wondering your insights on why. [00:33:31]Artem: I think overall, the statement is true. It's really hard to monetize, you know, like in embedded analytics. That's why at Qube we're excited more about our internal kind of BI use case, or like a company's a building, you know, like a chatbots for their internal data consumption or like internal workflows. Embedded analytics is hard to monetize because it's historically been dominated by the BI vendors. And we still see a lot of organizations are using BI tools as vendors. And what I was talking about, BI vendors adding natural language interfaces, they will probably add that to the embedded analytics capabilities as well, right? So they would be able to embed that too. So I think that's part of it. Also, you know, if you look at the embedded analytics market, the bigger organizations are big GADs, they're really more custom, you know, like it becomes and at some point I see many organizations, they just stop using any vendor, and they just kind of build most of the stuff from scratch, which probably, you know, like the right way to do. So it's sort of, you know, like you got a market that is very kept at the top. And then you also in that middle and small segment, you got a lot of vendors trying to, you know, like to compete for the buyers. And because again, the BI is very fragmented, embedded analytics, therefore is fragmented also. So you're really going after the mid market slice, and then with a lot of other vendors competing for that. So that's why it's historically been hard to monetize, right? I don't think AI really going to change that just because it's using model, you just pay to open AI. And that's it, like everyone can do that, right? So it's not much of a competitive advantage. So it's going to be more like a commodity features that a lot of vendors would be able to leverage. [00:35:20]Alessio: This is great, Artem. As usual, we got our lightning round. So it's three questions. One is about acceleration, one on exploration, and then take away. The acceleration thing is what's something that already happened in AI or maybe, you know, in data that you thought would take much longer, but it's already happening today. [00:35:38]Artem: To be honest, all this foundational models, I thought that we had a lot of models that been in production for like, you know, maybe decade or so. And it was like a very niche use cases, very vertical use cases, it's just like in very customized models. And even when we're building Statsbot back then in 2016, right, even back then, we had some natural language models being deployed, like a Google Translate or something that was still was a sort of a model, right, but it was very customized with a specific use case. So I thought that would continue for like, many years, we will use AI, we'll have all these customized niche models. But there is like foundational model, they like very generic now, they can serve many, many different use cases. So I think that is a big change. And I didn't expect that, to be honest. [00:36:27]Swyx: The next question is about exploration. What is one thing that you think is the most interesting unsolved question in AI? [00:36:33]Artem: I think AI is a subset of software engineering in general. And it's sort of connected to the data as well. Because software engineering as a discipline, it has quite a history. We build a lot of processes, you know, like toolkits and methodologies, how we prod that, [00:36:50]Swyx: right. [00:36:51]Artem: But AI, I don't think it's completely different. But it has some unique traits, you know, like, it's quite not idempotent, right, and kind of from many dimensions and like other traits. So which kind of may require a different methodologies may require different approaches and a different toolkit. I don't think how much is going to deviate from a standard software engineering, I think many tools and practices that we develop our software engineering can be applied to AI. And some of the data best practices can be applied as well. But it's like we got a DevOps, right, like it's just a bunch of tools, like ecosystem. So now like AI is kind of feels like it's shaping into that with a lot of its own, you know, like methodologies, practices and toolkits. So I'm really excited about it. And I think it's a lot of unsolved still question again, how do we develop that? How do we test you know, like, what is the best practices? How what is a methodologist? So I think that would be an interesting to see. [00:37:44]Alessio: Awesome. Yeah. Our final message, you know, you have a big audience of engineers and technical folks, what's something you want everybody to remember to think about to explore? [00:37:55]Artem: I mean, it says being hooked to try to build a chatbot, you know, like for analytics, back then and kind of, you know, like looking at what people do right now, I think, yeah, just do that. I mean, it's working right now, with foundational models, it's actually now it's possible to build all those cool applications. I'm so excited to see, you know, like, how much changed in the last six years or so that we actually now can build a smart agents. So I think that sort of, you know, like a takeaways and yeah, we are, as humans in general, we like we really move technology forward. And it's fun to see, you know, like, it's just a first hand. [00:38:30]Alessio: Well, thank you so much for coming on Artem. [00:38:32]Swyx: This was great. [00:38:32] Get full access to Latent Space at www.latent.space/subscribe

Description:On this episode, Benn Stancil, Co-Founder of Mode Analytics and current Field CTO at Thoughtspot  shares his thoughts on who wants to use data to solve problems, and who just wants data to validate an opinion. He brings unique perspectives to how data excellence can be proliferated through an organization, whether shadow IT is a nuisance or a guide post, and how large language models will influence the future of data. Key Moments:How an internal analytics tool Benn built at Yammer led to starting Mode Analytics to serve the new breed of technical data teams (4:00)Why the urgency around BI consolidation in 2023 prompted conversations with ThoughtSpot (52:30) The risks of shadow tools and lack of consistency; importance of a centralized data infrastructure (26:30)Using natural language search as an interface for asking novel questions and changing how people use data (44:00)Data teams need visibility into the business context; centralization for infrastructure but decentralization to embed in business units (23:30)How ambition plays an outsized role in the type of data tools selected in an organization (17:58)Key Quotes:Courage, ultimately, is needed in making a decision that you have to own. [Unfortunately], data can be a punt. If you make a data driven decision, nobody made the decision. The data made the decision. There is like an abstraction there of who was actually responsible for it. What ends up happening [in shadow IT] is you spend all this time just trying to figure out, like, which one do you trust? Who's right? Everybody has their different perspectiveGood Googlers are not people that know the, the exact syntax of weird Google searches. Good Googlers are the people who have some spider sense about where to go looking for things. And I think AI is probably going to be similar to that, where it's not the crazy prompt engineers that'll make it good. It's like some of the next level spider sense skills that we don't know yet.MentionsRShadow ITMicrosoft acquires YammerLarge Language Models (LLMs)ModeBioBenn Stancil is ThoughtSpot's Field CTO. 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.Personal Details:- Enjoys playing baseball and is a Braves fan- Favorite pump up song: "Labour" by Paris Paloma- Grateful for opportunities to travel and gain new perspectives

In the rapidly evolving world of data analytics, staying ahead of the curve is essential. Today on Tech Talks Daily, I'm thrilled to have Sumeet Arora from ThoughtSpot to walk us through their game-changing announcements. ThoughtSpot is already renowned for its advanced analytics tool, but with its recent launches, they are truly pushing the boundaries. Dive in with us as we discuss the all-new ThoughtSpot Sage, a search experience that blends foundational language models with ThoughtSpot's unmatched search technology. Get excited as we unveil its integration into free trials, enhancing accessibility for users everywhere. As businesses globally rely heavily on workplace productivity tools, ThoughtSpot's integrations with giants like Excel and Google Workspace aim to embed analytics right within your everyday workflow. But that's just the start. With Liveboards now enabling real-time collaborative decision-making using data, we examine the tangible benefits this brings to the table. The episode then takes a deeper dive into the expanded universe of ThoughtSpot's features. With the recent launch of ThoughtSpot SaaS on Google Cloud Platform, the introduction of a range of new database connectors, the unveiling of the Data Modeling Studio, and innovative embedded Liveboard features, businesses are in for an enriched data experience like never before. Sumeet also elaborates on the exciting launch of Monitor for Mobile, a feature that not only notifies users about business metric changes but provides invaluable insights into the 'why' behind these shifts. We then switch gears to discuss ThoughtSpot's strategic acquisition of Mode Analytics for a whopping $200M. How does this merger aim to empower data teams with Generative AI? And how will it redefine the landscape of Business Intelligence? If you've been seeking to understand how to truly harness the power of your data, this episode is packed with insights, updates, and a vision of the future you won't want to miss. Join Neil and Sumeet as they chart the course of AI-driven analytics and explore the next frontier in data interpretation.