Data Masters Podcast

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

Wes McKinney, Principal Architect at Posit PBC and creator of the pandas library, joins the discussion on how open-source tools like Python are reshaping data analytics. He shares insights into the evolution of the modern data stack and the importance of interoperability. Wes highlights the necessity of human judgment in complex analytics, despite the rise of AI-driven agents. He discusses the challenges of language models with tabular data and emphasizes robust testing and defensive coding practices to navigate new AI-driven workflows.

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

The role of the Chief Data Officer is no longer confined to governance and compliance — it’s now about driving innovation, business growth and strategic transformation. We’re joined by Jean-Christophe Lionti, Member of the Board of Trustees of RSF | Regenerative Social Finance and Managing Director and Chief Data Officer of Mizuho, to explore how data leaders balance risk management with growth, build strong foundations for AI and turn data into a business asset. JC shares insights on breaking down organizational silos, building trusted pipelines, aligning data initiatives with business goals and preparing enterprises for an AI-driven future. He also reflects on lessons from his leadership journey and offers advice for aspiring CDOs navigating the data landscape.Key Takeaways:00:00 Introduction.02:05 The CDO balances between offense and defense, driving innovation.04:50 A 360-degree customer view is essential for growth and scale.10:43 Data warehouses help, but rapid tech change makes them only part of the solution.15:48 Build a rigorous foundation with minimal data movement and progressive curation.20:12 Legacy practices often aim to fix data issues, but cleansing remains essential.26:12 Delivering an MVP quickly and unlocking funding and significant annual savings.29:54 AI is everywhere, so organizations must invest strategically to maximize its value.34:57 Stay close to the business and focus data efforts where they deliver value.Resources Mentioned:Jean-Christophe Liontihttps://www.linkedin.com/in/jean-christophe-lionti-539499/RSF | Regenerative Social Finance | LinkedInhttps://www.linkedin.com/company/rsf-social-finance/RSF | Regenerative Social Finance | Websitehttps://rsfsocialfinance.org/Mizuho | LinkedInhttps://www.linkedin.com/company/mizuho/Mizuho | Websitehttps://www.mizuhogroup.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 future of data-driven organizations depends on context, not just models. We’re joined by Sastry Durvasula, Chief Operating, Information and  Digital Officer of TIAA, to explore how a century-old institution is transforming operations, client experiences and services with next-generation technology. Sastry shares how TIAA balances innovation with responsibility, leveraging vast datasets and AI to solve complex problems at scale while protecting vulnerable clients. He highlights strategies for embedding empathy in customer interactions, improving data quality and building a “workforce of the future” where humans and machines collaborate seamlessly.Key Takeaways:00:00 Introduction.03:09 TIAA manages vast data across retirement, wealth and asset management.08:00 AI strategy rests on four pillars: tech, client experience, operations, and services.16:10 Human service agents handle complex participant needs with AI support.21:42 Operating officers must blend operational expertise with AI and technology.25:13 AI solutions help protect older adults from fraud and cognitive decline risks.30:25 Complaints come through every channel and require timely resolution.34:56 Beyond semantics, the real focus is on building the workforce of the future with AI.38:55 AI agents shift economies by changing how consumers shop and services operate.Resources Mentioned:Sastry Durvasulahttps://www.linkedin.com/in/durvasula/TIAA | LinkedInhttps://www.linkedin.com/company/tiaa/TIAA | Websitehttps://www.tiaa.org/publicTIAA Institute Researchhttps://www.tiaa.org/public/instituteThanks 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

A traditional energy company is rewriting its future with data, AI and a customer-first mindset. We’re joined by Dak Liyanearachchi, Executive Vice President and Chief Technology Officer of NRG Energy, to explore how one of America’s largest energy companies is redefining its identity as a tech-forward, customer-centric organization. Dak shares how NRG is connecting the entire energy value chain — from generation to smart home consumption — through data and AI innovation. We also discuss the broader implications of grid efficiency, AI-driven forecasting and the cultural change required to power true digital transformation.Key Takeaways:(03:04) NRG spans energy generation to smart home energy use across deregulated markets.(07:05) Transforming the customer experience starts with managing home energy at the last mile.(09:48) Smart energy usage requires intelligent management of consumption and generation.(10:49) Data enables personalized insights to help customers choose the right energy plan.(14:24) A forecasting model simulates usage and market data to guide five-year energy planning.(20:00) GenAI boosts productivity and improves customer engagement through automated support.(25:02) The challenge isn’t adopting GenAI — it’s clearly defining what to do with it.(28:57) Ongoing transformation is fueled by a tech-first mindset and a focus on delivering value.Resources Mentioned:Dak Liyanearachchihttps://www.linkedin.com/in/dak-liyanearachchi-0664381/NRG Energy | LinkedInhttps://www.linkedin.com/company/nrgenergy/NRG Energy | Websitehttps://www.nrg.com/Renew Homehttps://www.renewhome.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