Applying the Scientific Mindset to Machine Learning, Data Science and AI with Jonathan Burley of Bloomberg Industry Group

In this episode, Rajiv Shah, Agentic AI Engineer of OpenHands, joins us to explore the shift from inner-loop tab-complete assistants to autonomous outer-loop coding agents that execute complex engineering tasks with minimal human oversight. We discuss enterprise governance challenges, the open-source case for model-agnostic infrastructure and why getting AI agents to create real business value requires far more than writing production-quality code.Rajiv also explains why the semantic layer between human intent and stored data remains a critical gap even the most capable agents can't fill independently, and what it means for data teams still working to close it.Key Takeaways:00:00 Introduction.02:41 Outer-loop coding agents execute autonomously for hours without hand-holding, from vulnerability scans to legacy code modernization.05:25 AI agents with broad enterprise permissions create governance, security and data risks that demand sandboxed infrastructure.11:09 Open-source AI platforms offer enterprises transparency, auditability and model flexibility to avoid vendor lock-in.15:17 Moving from a demo to enterprise value requires change management, leadership buy-in and workflow integration, not just working code.22:39 Data agents still need a semantic layer because human definitions of revenue, customers and churn vary by business and by quarter.24:31 AI agents advance fastest in verifiable, deterministic domains and will continue reshaping any field that works in bits and bytes.Thanks for listening to the “Data Masters Podcast.” If you enjoyed this episode, be sure to subscribe so you never miss our latest discussions and insights into the ever-changing world of data.Resources Mentioned:OpenHands website:https://www.openhands.dev/Rajiv Shah on LinkedIn:https://www.linkedin.com/in/rajistics/OpenHands on LinkedIn:https://www.linkedin.com/company/openhands-ai/#DataStrategy #DataManagement #DataMastersPodcast

Data strategy is shifting upstream, moving from how organizations visualize data to the data that supplies those visualizations. In this episode, Chris Tabb, Co-Founder and Chief Commercial Officer of LEIT DATA, joins us to explore why traditional ROI thinking fails data teams, how the friction framework changes how organizations build business cases and what the emerging context layer means for AI-ready data architectures.KEY TAKEAWAYS00:00 Introduction. 02:50 Dashboards only create value when they drive a clear business decision. 05:15 Data strategy is shifting from visualization back toward the quality of the underlying data. 13:30 Business value is a positive evidence effect on business objectives, not a bottom-line metric.15:10 Friction, measured by time, effort and frequency, is the foundation for a data business case.17:00 Force multipliers solve a problem once and yield compounding returns across the organization.22:10 Reading the annual report first will anchor any data project to the business's actual objectives.26:30 The context layer unifies ontologies, knowledge graphs, semantic layers and vector databases under one concept.28:30 Scoring and tuning prompt context is how organizations get more accurate, consistent AI outputs.35:10 Meta metadata, data about the data about the data, is where enterprise value now lives.Thanks for listening to the “Data Masters Podcast.” If you enjoyed this episode, be sure to subscribe so you never miss our latest discussions and insights into the ever-changing world of data.RESOURCES MENTIONEDChris Tabb:https://www.linkedin.com/in/chris-tabb-mean-data-streets/LEIT DATA LinkedIn:https://www.linkedin.com/company/leit-data/LEIT DATA website:https://leit-data.com/O'Reilly book by Joe Reis and Matt Housley [Fundamentals of Data Engineering]:https://www.amazon.com/Fundamentals-Data-Engineering-Robust-Systems/dp/1098108302#DataStrategy #DataManagement #DataMastersPodcast

Building effective AI products isn't just about using the latest large language model; it's about asking the right questions and solving real problems. In this episode, we’re joined by Jonathan Burley, Director of AI of Bloomberg Industry Group, to explore his journey from modeling climate systems to leading AI strategy. Jonathan discusses why the scientific mindset is critical in machine learning, the value of the minimum viable experiment and how to avoid the pitfalls of generative AI demos.Key Takeaways:00:00 Introduction.03:14 The evergreen skills of handling data nuances help scientists transition into industry.08:57 The scientific method provides a foundational mindset for reasoning under uncertainty.16:39 Frame conversations around concrete business problems instead of leading with new technology.19:46 Focus on minimum viable experiments to test core assumptions before committing to a minimum viable product.24:46 Find unexciting areas of the economy where AI tools can deliver rapid and measurable ROI.38:20 Approach generative AI demos with caution because they easily disguise incomplete products.41:51 Solve the most boring, thankless and repetitive tasks to build tools experts actually want to use.Resources Mentioned:Jonathan Burleyhttps://www.google.com/search?q=https://www.linkedin.com/in/jonathanburley/Bloomberg Industry Group | LinkedInhttps://www.linkedin.com/company/bloomberg-industry-group/Bloomberg Industry Group | Websitehttps://www.bloombergindustry.com/Actifai | Websitehttps://www.actif.ai/index.htmlContinuous Delivery — David Farleyhttps://www.youtube.com/c/ContinuousDeliveryThanks for listening to the “Data Masters Podcast.” If you enjoyed this episode, be sure to subscribe so you never miss our latest discussions and insights into the ever-changing world of data.#DataStrategy #DataManagement #DataMastersPodcast

AI is not a side project; it is a business model shift. In this episode, we’re joined by Dr. Elena Alikhachkina,  Chief Data and AI Officer of TE Connectivity, director, board advisor and author, to explore why AI demands a product mindset, how “learning data” powers continuous AI loops and what boards must do now to govern AI responsibly. Elena shares why technical skills alone are no longer enough, how trust is being redefined in the age of digital employees, and why 2026 will mark a turning point for data, governance and enterprise transformation.Elena challenges the idea that data is “the new oil” and instead reframes it as a living, learning asset embedded in business processes. We unpack what it really means to build AI loops, why product leaders must be deeply embedded in operations and how governance, data readiness and performance accountability must evolve as agents join the workforce.Key Takeaways:00:00 Introduction.02:38 AI is reshaping data careers and elevating product and business skills.06:00 Product leaders go beyond dashboards to uncover the real business problem.09:43 Data teams must embed in operations and “walk the floor” to drive adoption.13:33 Data is not oil — it must power continuous learning loops.16:15 AI pilots fail when they do not close the loop with user feedback and new data.20:20 Boards face confusion and must translate AI into governance responsibilities.23:00 Data governance and data readiness must become board-level metrics.25:00 Leaders will manage digital employees and agents alongside humans.28:21 2026 will move AI beyond chat interfaces into embedded enterprise systems.30:50 The AI opportunity is underestimated, but security and compliance may slow scale.Resources Mentioned:Dr. Elena Alikhachkinahttps://www.linkedin.com/in/dr-elena-alikhachkina-2265041/TE Connectivity | LinkedInhttps://www.linkedin.com/company/te-connectivity/TE Connectivity | Websitehttps://www.linkedin.com/company/te-connectivity/Re:Coded Newsletterhttps://www.re-coded.comNational Association of Corporate Directors | Websitehttps://www.nacdonline.orgJohnson and Johnson | Websitehttps://www.jnj.comThanks for listening to the “Data Masters Podcast.” If you enjoyed this episode, be sure to subscribe so you never miss our latest discussions and insights into the ever-changing world of data.#DataStrategy #DataManagement #DataMastersPodcast

Most startups don’t fail because the product is bad; they fail because nobody is willing to pay for it. In this episode, Martin Miller, CTO of FastCTO and M-Vision, Inc., and Host of Unriveted Media Podcast, joins us to break down what it really takes to go from a startup idea on a napkin to a real product people will pay for. Martin shares why founders need to validate demand early, understand the buying cycle of their target market and avoid relying on hope as a strategy. Key Takeaways:00:00 Introduction.03:15 Validate your idea early by asking customers if it solves a problem they’d pay to fix.04:34 Experiential learning shows solving your own problem is a start; traction comes when others pay.10:28 Building rarely guarantees demand; people need to know you exist.14:58 Vibe coding feels like magic but it’s built on design patterns.20:19 Entry-level roles are shrinking but experienced builders benefit most.25:45 Five great builders beat fifty low-cost developers.30:18 Don’t obsess over tech, focus on whether customers will pay.32:15 Talk to prospects early, turn them into paying customers and focus on sales, not just code.Resources Mentioned:Martin Millerhttps://www.linkedin.com/in/martinemiller/FastCTO | LinkedInhttps://www.linkedin.com/company/fastcto/FastCTO | Websitehttps://fastcto.com/M-Vision, Inc. | LinkedInhttps://www.linkedin.com/company/m-vision-inc/M-Vision, Inc. | Websitehttps://www.m-vision.comUnriveted Media Podcast | LinkedInhttps://www.linkedin.com/company/unriveted-media/Unriveted Media Podcast | Websitehttps://www.unriveted.mediaThanks for listening to the “Data Masters Podcast.” If you enjoyed this episode, be sure to subscribe so you never miss our latest discussions and insights into the ever-changing world of data.#DataStrategy #DataManagement #DataMastersPodcast

Breaking into the data world doesn’t always have to follow a straight path. Avery Smith, Founder at Data Career Jumpstart, joins us to explore exactly how newcomers and career switchers can do it successfully. Avery shares why most people start with the wrong tools, why data analyst roles offer the easiest entry point and how focusing on skills, portfolio and network creates real momentum. He also breaks down why internal pivots often outperform external job hunts and how AI is reshaping, but not replacing, the work of analysts and data scientists.Key Takeaways:00:00 Introduction.02:37 Beginners shouldn’t start with Python because it adds unnecessary complexity early on.06:07 Data work should prioritize real business impact over flashy tools.10:11 In tight markets, companies prefer analytics because it delivers quicker, more reliable wins.15:13 Skills matter, but your portfolio and network are what actually create opportunities.21:19 The real value isn’t the code — it’s the insights it produces and how clearly you show them.26:14 Lead with small asks; advice opens more doors than asking for a job.33:03 AI has assisted data work, enhancing workflows rather than replacing roles.37:15 Good data still needs clear explanation to drive real decisions.Resources Mentioned:Avery Smithhttps://www.linkedin.com/in/averyjsmith/Data Career Jumpstarthttps://datacareerjumpstart.comData Career Podcasthttps://datacareerpodcast.comTableauhttps://www.tableau.com/Excelhttps://excel.cloud.microsoft/en-us/Streamlithttps://streamlit.io/Thanks for listening to the “Data Masters Podcast.” If you enjoyed this episode, be sure to subscribe so you never miss our latest discussions and insights into the ever-changing world of data.#DataStrategy #DataManagement #DataMastersPodcast

The future of analytics isn’t just about bigger models — it’s about building smarter, more interoperable data systems. Wes McKinney, Principal Architect of Posit PBC, Chief Scientist of Voltron Data and a General Partner at Composed Ventures, joins us to explore how the modern data stack is evolving and what it means for the future of analytics. Wes reflects on his journey building pandas and Apache Arrow, sharing how open-source ecosystems grow, transform and shape the way organizations work with data today. Wes also highlights the rising importance of semantic layers, agentic workflows and defensive coding practices as teams embrace AI-driven development.Key Takeaways:00:00 Introduction.02:32 Wes didn’t expect pandas to drive AI but he recognized Python’s unrealized potential.05:09 A lucky convergence helped Python’s tools snowball into the AI standard.10:40 Early big data focused on essentials, not the interoperable stacks we rely on today.15:44 The composable data stack grew through bottom-up, grassroots open-source momentum.21:56 Many “data science” roles ultimately became business intelligence and dashboard work.25:24 Complex statistical work still depends on human judgment, not fully autonomous agents.30:27 Frontier models retrieve table data reliably, while smaller models fail dramatically.35:16 Better models and coding agents shifted Wes from an AI skeptic to an adopter.40:07 AI-driven code demands stronger testing and review to avoid costly failures.45:14 An AI-built finance project ballooned, revealing how agents inflate codebases.Resources Mentioned:Wes McKinneyhttps://www.linkedin.com/in/wesmckinn/Posit PBC | LinkedInhttps://www.linkedin.com/company/posit-software/Posit PBC | Websitehttps://posit.co/Voltron Data | LinkedInhttps://www.linkedin.com/company/voltrondata/Voltron Data | Websitehttps://voltrondata.com/Composed Ventures | LinkedInhttps://www.linkedin.com/company/composedvc/Composed Ventures | Websitehttps://composed.vc/pandashttps://pandas.pydata.org/Apache Arrowhttps://arrow.apache.org/DuckDBhttps://duckdb.org/DataFusionhttps://datafusion.apache.org/Jupyter Notebookhttps://jupyter.org/Parquethttps://parquet.apache.org/Iceberghttps://iceberg.apache.org/Delta Lakehttps://delta.io/Thanks for listening to the “Data Masters Podcast.” If you enjoyed this episode, be sure to subscribe so you never miss our latest discussions and insights into the ever-changing world of data.#DataStrategy #DataManagement #DataMastersPodcast

Data is messy, especially in healthcare. In this episode, Spriha Gogia, Senior Director of Data at Ophelia, joins us to explore how data teams can navigate complexity while driving meaningful outcomes. She shares how embracing the chaos, prioritizing business impact and connecting data to organizational goals help healthcare organizations move from reactive to proactive. Spriha also discusses the evolving role of AI in healthcare, clarifying what AI truly means and how generative models can empower clinicians and improve patient care without losing the human touch.Key Takeaways:00:00 Introduction.02:42 Spriha’s passion for science led her from academia to data-driven healthcare.06:46 Healthcare data spans systems to wearables, and data teams must make it cohesive.10:14 Data silos persist, but an enterprise data warehouse brings order to chaos.16:32 Data leaders should embed in strategy discussions to align with business goals.20:08 A data product is any analysis or dataset that delivers value to its stakeholders.24:28 AI mimics human tasks — machine learning predicts outcomes autonomously.30:10 GenAI can ease clinician burnout by automating repetitive documentation tasks.35:25 OUD care generates persistent, complex data requiring ongoing patient tracking.Resources Mentioned:Spriha Gogiahttps://www.linkedin.com/in/sprihagogia/Ophelia | LinkedInhttps://www.linkedin.com/company/opheliahealth/Ophelia | Websitehttps://ophelia.com/Generative AIhttps://generativeai.net/Thanks for listening to the “Data Masters Podcast.” If you enjoyed this episode, be sure to subscribe so you never miss our latest discussions and insights into the ever-changing world of data.#DataStrategy #DataManagement #DataMastersPodcast

Data becomes truly powerful when it starts with people, not platforms. In this episode, Peter Laflin, Director of Data and Analytics at Morrisons, joins us to explore how one of the UK’s largest supermarket chains turns customer insights into smarter business decisions. Peter shares how empathy drives Morrisons’ data strategy, from understanding shoppers’ in-store needs to building AI-driven solutions that make everyday experiences smoother. He also discusses how his team measures success by business impact, fosters neurodiverse collaboration and ensures data remains trustworthy in an AI-first world.Key Takeaways:00:00 Introduction.02:45 Starting with customers helps solve problems, like finding cranberry sauce at Christmastime.05:50 Data team members work in stores to build empathy and improve the shopping experience.10:11 Success is measured by business impact, not tickets or code.14:39 High-performing teams win or lose together, driving customer satisfaction and growth.22:20 The right environment helps people thrive and do their best work.25:05 Different thinking styles spark better ideas and stronger solutions.30:41 Centralize for speed, then decentralize once data foundations are strong.38:10 The age of data governance demands trust so AI can enhance human judgment.Resources Mentioned:Peter Laflinhttps://www.linkedin.com/in/peter-laflin-3a92092/?originalSubdomain=ukMorrisons | LinkedInhttps://www.linkedin.com/company/morrisonsjobs/Morrisons | Websitehttps://www.morrisons.jobs/Googlehttps://www.google.com/Geminihttps://gemini.google.com/Thanks for listening to the “Data Masters Podcast.” If you enjoyed this episode, be sure to subscribe so you never miss our latest discussions and insights into the ever-changing world of data.#DataStrategy #DataManagement #DataMastersPodcast

The semantic layer is becoming the backbone of trusted, AI-ready data. We’re joined by David P. Mariani, Chief Technology Officer and Co-Founder of AtScale, to explore why defining a shared business language is critical for scalable analytics and AI innovation. David explains how the semantic layer enables teams to align on metrics, eliminate silos and create flexibility across BI tools, data platforms and emerging AI interfaces. He also shares how open standards and large language models are reshaping how businesses interact with their data.Key Takeaways:00:00 Introduction.02:22 Semantic layers began in BI tools, tightly linked to the presentation layer.08:02 Combining a semantic layer with LLMs unlocks powerful insights.12:57 Relying on one BI tool creates inconsistent metrics as AI adds new consumption layers.17:29 Open-sourcing SML prevents lock-in and standardizes semantic models.22:38 Semantic layers with GenAI reshape strategy through language and a strong query engine.25:45 Without a semantic layer, LLMs were wrong 80% of the time.30:33 Data engineers should build base semantic objects as part of their pipeline.38:53 MCP with semantic layers and knowledge graphs gives LLMs a richer context.Resources Mentioned:David P. Marianihttps://www.linkedin.com/in/davidpmariani/AtScale | LinkeInhttps://www.linkedin.com/company/atscale-inc-/AtScale | Websitehttps://www.atscale.com/SMLhttps://www.atscale.com/blog/introduction-to-sml-a-standard-semantic-modeling-language/Model Context Protocol (MCP)https://modelcontextprotocol.io/docs/getting-started/introThanks for listening to the “Data Masters Podcast.” If you enjoyed this episode, be sure to subscribe so you never miss our latest discussions and insights into the ever-changing world of data.#DataStrategy #DataManagement #DataMastersPodcast