Data Masters Podcast

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Data Masters is the go-to place for data enthusiasts. We speak with data leaders from around the world about data, analytics, and the emerging technologies and techniques data-savvy organizations are tapping into to gain a competitive edge. Our experts also share their opinions and perspectives about the hyped, or overhyped, industry trends we may all be geeking out over.

  1. Startup Reality Check: Why Cool Products Don’t Always Sell with Martin Miller of FastCTO, M-Vision and Unriveted Media Podcast

    אתמול

    Startup Reality Check: Why Cool Products Don’t Always Sell with Martin Miller of FastCTO, M-Vision and Unriveted Media Podcast

    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 Miller https://www.linkedin.com/in/martinemiller/ FastCTO | LinkedIn https://www.linkedin.com/company/fastcto/ FastCTO | Website https://fastcto.com/ M-Vision, Inc. | LinkedIn https://www.linkedin.com/company/m-vision-inc/ M-Vision, Inc. | Website https://www.m-vision.com Unriveted Media Podcast | LinkedIn https://www.linkedin.com/company/unriveted-media/ Unriveted Media Podcast | Website https://www.unriveted.media 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

    34 דק׳
  2. Breaking Into Data Careers: Practical Paths for Newcomers and Career Switchers with Avery Smith of Data Career Jumpstart

    21 בינו׳

    Breaking Into Data Careers: Practical Paths for Newcomers and Career Switchers with Avery Smith of Data Career Jumpstart

    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 Smith https://www.linkedin.com/in/averyjsmith/ Data Career Jumpstart https://datacareerjumpstart.com Data Career Podcast https://datacareerpodcast.com Tableau https://www.tableau.com/ Excel https://excel.cloud.microsoft/en-us/ Streamlit https://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

    38 דק׳
  3. How Open Source, Python and AI Are Shaping the Data Future with Wes McKinney of Posit PBC, Voltron Data and Composed Ventures

    7 בינו׳

    How Open Source, Python and AI Are Shaping the Data Future with Wes McKinney of Posit PBC, Voltron Data and Composed Ventures

    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 McKinney https://www.linkedin.com/in/wesmckinn/ Posit PBC | LinkedIn https://www.linkedin.com/company/posit-software/ Posit PBC | Website https://posit.co/ Voltron Data | LinkedIn https://www.linkedin.com/company/voltrondata/ Voltron Data | Website https://voltrondata.com/ Composed Ventures | LinkedIn https://www.linkedin.com/company/composedvc/ Composed Ventures | Website https://composed.vc/ pandas https://pandas.pydata.org/ Apache Arrow https://arrow.apache.org/ DuckDB https://duckdb.org/ DataFusion https://datafusion.apache.org/ Jupyter Notebook https://jupyter.org/ Parquet https://parquet.apache.org/ Iceberg https://iceberg.apache.org/ Delta Lake https://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

    46 דק׳
  4. Empowering Clinicians Through AI and Data Innovation with Spriha Gogia of Ophelia

    15.12.2025

    Empowering Clinicians Through AI and Data Innovation with Spriha Gogia of Ophelia

    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 Gogia https://www.linkedin.com/in/sprihagogia/ Ophelia | LinkedIn https://www.linkedin.com/company/opheliahealth/ Ophelia | Website https://ophelia.com/ Generative AI https://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

    36 דק׳
  5. From Insights to Impact: Making Data Work for Customers with Peter Laflin of Morrisons

    03.12.2025

    From Insights to Impact: Making Data Work for Customers with Peter Laflin of Morrisons

    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 Laflin https://www.linkedin.com/in/peter-laflin-3a92092/?originalSubdomain=uk Morrisons | LinkedIn https://www.linkedin.com/company/morrisonsjobs/ Morrisons | Website https://www.morrisons.jobs/ Google https://www.google.com/ Gemini https://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

    41 דק׳
  6. How Semantic Layers Future-Proof Data Strategies with David P. Mariani of AtScale

    06.11.2025

    How Semantic Layers Future-Proof Data Strategies with David P. Mariani of AtScale

    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. Mariani https://www.linkedin.com/in/davidpmariani/ AtScale | LinkeIn https://www.linkedin.com/company/atscale-inc-/ AtScale | Website https://www.atscale.com/ SML https://www.atscale.com/blog/introduction-to-sml-a-standard-semantic-modeling-language/ Model Context Protocol (MCP) https://modelcontextprotocol.io/docs/getting-started/intro 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

    40 דק׳
  7. Leading Financial Transformation Through Data Innovation with Jean-Christophe Lionti of Mizuho

    27.10.2025

    Leading Financial Transformation Through Data Innovation with Jean-Christophe Lionti of Mizuho

    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 Lionti https://www.linkedin.com/in/jean-christophe-lionti-539499/ RSF | Regenerative Social Finance | LinkedIn https://www.linkedin.com/company/rsf-social-finance/ RSF | Regenerative Social Finance | Website https://rsfsocialfinance.org/ Mizuho | LinkedIn https://www.linkedin.com/company/mizuho/ Mizuho | Website https://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

    37 דק׳
  8. Balancing Data Context and AI Scale with Sastry Durvasula of TIAA

    17.09.2025

    Balancing Data Context and AI Scale with Sastry Durvasula of TIAA

    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 Durvasula https://www.linkedin.com/in/durvasula/ TIAA | LinkedIn https://www.linkedin.com/company/tiaa/ TIAA | Website https://www.tiaa.org/public TIAA Institute Research https://www.tiaa.org/public/institute 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

    41 דק׳
הצגת הכול (60)

אודות

Data Masters is the go-to place for data enthusiasts. We speak with data leaders from around the world about data, analytics, and the emerging technologies and techniques data-savvy organizations are tapping into to gain a competitive edge. Our experts also share their opinions and perspectives about the hyped, or overhyped, industry trends we may all be geeking out over.

מידע

  • יוצרים
    TAMR
  • שנות פעילות
    2020 - 2026
  • פרקים
    60
  • סיווג
    תוכן הולם
  • זכויות יוצרים
    © 338262
  • אתר התכנית
    Data Masters Podcast