Data Analytics Chat

Ben Parker
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*** The podcast is on a short pause ***🎧 Data Analytics Chat explores how the world's leading organisations are building, scaling and transforming through Data & AI. Hosted by Ben Parker, Founder of Parker B Associates, each episode features senior Data, AI and technology leaders discussing what they're building, what's getting in the way, and what they've learned along the way. From AI adoption and data platforms to leadership, talent and transformation — these are conversations with the people actually doing it. 20,000+ downloads | Featuring leaders from AWS, Google, IBM, Oracle and Fortune 500 organisations. Find us on:🎧 Apple – https://bit.ly/3D0Ro8Y🎧 Spotify – https://bit.ly/4381oaU🎧 YouTube – https://bit.ly/41sJf6I 👉 Hit subscribe and join us on the journey.  Connect with the host - https://www.linkedin.com/in/ben---parker/  

  1. 2월 4일

    From Data Projects to Data Products: Essential Skills for AI Leaders

    Send us Fan Mail With Elena Alikhachkina — 4x Chief AI & Data Officer and Board Advisor   What does it really take to move from data projects to data products?   In this episode, Ben Parker speaks with Elena Alikhachkina about one of the biggest shifts happening across Data and AI and why technical expertise alone is no longer enough.   Drawing on more than 25 years in the industry, Elena explores how organisations can build more customer-focused, commercially relevant Data and AI products through stronger product thinking, business understanding and collaboration.   You’ll hear practical insights on:   Why Data and AI teams need to think in products, not projects How to connect technical work to business outcomes Why product skills are becoming essential in AI Bridging the gap between business and technology The growing importance of communication and commercial awareness The skills future Data and AI leaders need to develop   Chapters   00:00 Introduction 01:33 Elena’s career and leadership journey 09:17 From data projects to data products 15:06 Building a product mindset in Data & AI 22:34 The skills Data & AI professionals need next 29:50 Bridging business and technology 34:00 Turning product thinking into business value Thank you for listening!

  2. 1월 29일

    The Future of Data Science & Data Engineering in the Age of AI

    Send us Fan Mail With Phoenix Pei — SVP, Analytics Manager at Truist   What will the Data Scientist and Data Engineer of the future look like?   In this episode, Ben Parker speaks with Phoenix Pei about how AI and automation are changing data roles — and why technical expertise alone may no longer be enough.   Phoenix explores the growing importance of business understanding, trust and leadership alignment, why many data initiatives still struggle to create meaningful impact, and how organisations may need to rethink the structure of their data teams.   You’ll hear practical insights on:   How AI and automation are changing Data Science and Data Engineering Whether the future belongs to specialists or full-stack data professionals The technical, business and leadership skills that will matter most Why so many data initiatives struggle to deliver business value What prevents Data Science projects reaching production How Data Scientists and Data Engineers will work together in the future   Chapters   00:00 Introduction 02:18 Phoenix’s career and leadership journey 09:34 How AI is changing Data Science & Engineering 11:08 Why business understanding matters more than ever 24:54 Why Data Science initiatives struggle to deliver 25:07 The importance of leadership alignment 33:53 Preparing Data teams for the future Thank you for listening!

  3. 1월 21일

    How To Make Successful Decisions In AI

    Send us Fan Mail With Durai Rajamanickam — Senior AI Leader   How do leaders make better decisions about AI when the technology, risks and expectations are changing so quickly?   In this episode, Ben Parker speaks with Durai Rajamanickam about what it takes to turn AI ambition into something organisations can trust, scale and create value from.   They explore why AI initiatives can go wrong before technology is even the problem, the danger of hype-driven decisions, and why clear business objectives and leadership alignment matter.   The conversation also examines build vs buy, balancing speed with governance, when leaders should trust AI outputs, and the decisions organisations can't afford to delay.   You’ll hear practical insights on:   Why organisations misdiagnose the problems they want AI to solve How to make better build-vs-buy decisions Why promising AI initiatives fail Balancing speed, innovation, governance and trust When leaders should trust or challenge AI outputs The AI decisions organisations need to make now   Chapters   00:00 Why AI strategies go wrong 01:09 Meet Durai Rajamanickam 03:46 Build vs buy in AI 05:36 Avoiding hype-driven AI decisions 07:41 Aligning AI with the business 09:11 Building trust and governance 12:22 Balancing speed with control 15:22 Making better decisions with AI 21:03 Advice for AI leaders Thank you for listening!

  4. 1월 14일

    What It Really Takes to Adopt Generative AI at Scale

    Send us Fan Mail With Nayan Paul — Managing Director & Chief Architect, Generative AI at Accenture   Why are so many organisations experimenting with Generative AI, but so few turning it into meaningful business impact?   In this episode, Ben Parker speaks with Nayan Paul about what it really takes to move GenAI from experimentation into production and scale.   They explore why successful adoption isn't simply a technology challenge. It requires business ownership, the right operating model, strong data foundations and a clear approach to governance.   The conversation also examines how organisations can move quickly without sacrificing trust and responsibility and what separates AI experimentation from genuine business transformation.   You’ll hear practical insights on:   Why GenAI pilots struggle to reach production Moving from experimentation to measurable business value Why business ownership matters as much as technology Building the foundations for GenAI at scale Creating an effective AI operating model Balancing speed, governance and responsibility Turning GenAI from an experiment into an organisational capability   Chapters   00:00 Why scaling Generative AI is difficult 01:08 Meet Nayan Paul 05:08 Early GenAI experiments and lessons 07:02 Moving from experimentation to business value 10:14 Driving adoption across the business 16:15 Building the foundations for AI at scale 29:24 Balancing speed with responsibility 34:09 Moving from curiosity to impact Thank you for listening!

  5. 1월 7일

    Why Most Organisations Aren’t Ready for AI — Even If They Think They Are

    Send us Fan Mail With Sujit Narapareddy — Head of Data & Analytics, AWS Sales   What separates organisations experimenting with AI from those actually changing how work gets done?   In this episode, Ben Parker speaks with Sujit Narapareddy about what it really takes to embed AI into an organisation and why technology is only part of the challenge.   Sujit explores the importance of human judgement, strong data foundations and leadership alignment, alongside the organisational changes required to move from AI experimentation to real adoption.   The conversation also examines how AI could reshape everyday work by embedding intelligence directly into workflows, helping people move faster from insight to action without removing the need for human judgement.   You’ll hear practical insights on:    Why organisations underestimate what AI adoption really requires  What separates AI experimentation from real adoption  Why strong data foundations still matter  How AI can complement people rather than simply replace roles  How leaders should rethink teams and decision-making  Why human judgement becomes more important, not less  What organisations should be doing now to prepare   Chapters   00:00 The challenge of AI transformation 01:42 Meet Sujit Narapareddy 02:32 Sujit’s journey from technology to leadership 09:21 How AI changes human roles 16:17 Why organisations struggle to integrate AI 28:28 Preparing organisations for what comes next Thank you for listening!

  6. 2025. 12. 17.

     The Reality of AI Today: Beyond the Hype

    Send us Fan Mail With Carlos Pineda — Head of Data Analytics & Insight, Diageo North America   Where is AI actually creating value today and where are organisations still getting distracted by the hype?   In this episode, Ben Parker speaks with Carlos Pineda about the reality of implementing AI inside large organisations and what separates experimentation from meaningful business transformation.   Carlos explores why successful AI starts with understanding the business problem, having the right data foundations and embedding AI into real processes rather than simply adopting the latest technology.   The conversation also covers leadership, stakeholder engagement, experimentation and why organisations need to think about AI transformation end-to-end if they want to create lasting value.   You’ll hear practical insights on:   Where AI and GenAI are creating genuine business value today Why AI initiatives need to start with the business problem The importance of strong data foundations Why business understanding matters alongside technical expertise What prevents organisations from successfully integrating AI How experimentation can lead to scalable transformation How leaders should evaluate the cost and potential value of AI   Chapters   00:00 The reality of AI today 01:53 Meet Carlos Pineda 02:51 Carlos's career and leadership journey 08:02 Where AI is actually creating value 09:58 Why business understanding matters 13:13 The challenges of implementing AI 30:38 Understanding the cost and value of AI 33:39 Final thoughts Thank you for listening!

  7. 2025. 12. 12.

    Why Hiring and Retaining Top AI Talent Has Become Harder Than Ever

    Send us Fan Mail With Misha Trubskyy — Head of Claims Data Science, Mercury Insurance   Why is hiring exceptional AI and Data talent still so difficult even in a market full of candidates?   In this episode, Ben Parker speaks with Misha Trubskyy about what organisations are getting wrong when hiring, assessing and retaining AI and Data professionals.   Drawing on his experience leading Data Science in insurance, Misha explores the disconnect between companies struggling to find the right skills and candidates who feel hiring processes have become too selective.   The conversation examines what leaders should really look for beyond technical ability, why critical thinking and authenticity matter, and what organisations need to do differently to keep their strongest people once they've hired them.   You’ll hear practical insights on:   Why AI and Data talent remains difficult to hire The disconnect between employers and candidates What actually separates exceptional candidates Why technical skills alone aren't enough How to assess critical thinking and real-world capability The case for investing in junior talent What keeps top AI and Data professionals from leaving How the talent market could evolve over the next few years   Chapters   00:00 Introduction 02:24 Misha's career and leadership journey 07:37 Lessons in leading Data Science teams 29:52 Why hiring AI & Data talent is so difficult 37:02 What to look for beyond technical skills 44:21 What's happening in the talent market 48:55 Why organisations should invest in junior talent 01:04:33 How to retain top performers 01:10:03 The future of AI & Data hiring Thank you for listening!

  8. 2025. 12. 04.

    The Hidden Cost of Poor Data Quality

    Send us Fan Mail With Carol Kim — Executive Director, IBM   Can an organisation really be data-driven if its data can't be trusted?   In this episode, Ben Parker speaks with Carol Kim about why Data Governance and Data Quality are fundamental to better decision-making and what happens when organisations fail to get them right.   Carol explores how poor-quality data affects far more than technology, creating hidden costs across decision-making, operations and the wider business.   Drawing on her journey from finance into technology and Data and AI leadership, Carol also discusses the importance of curiosity, storytelling and authentic leadership — and the lessons she's learned navigating transformation across different cultures and environments.   You’ll hear practical insights on:   Why trusted data is essential for better decision-making The hidden business costs of poor Data Quality Where Data Governance typically breaks down How people, process and technology need to work together What organisations need to build effective Data Governance Why storytelling matters for Data leaders The importance of curiosity and continuous learning in leadership   Chapters   00:00 The importance of continuous learning 02:01 Carol's career and leadership journey 07:54 Why storytelling matters in Data 09:59 Leadership and career transformation 15:13 Why Data Governance & Data Quality matter 24:06 Final thoughts Thank you for listening!

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*** The podcast is on a short pause ***🎧 Data Analytics Chat explores how the world's leading organisations are building, scaling and transforming through Data & AI. Hosted by Ben Parker, Founder of Parker B Associates, each episode features senior Data, AI and technology leaders discussing what they're building, what's getting in the way, and what they've learned along the way. From AI adoption and data platforms to leadership, talent and transformation — these are conversations with the people actually doing it. 20,000+ downloads | Featuring leaders from AWS, Google, IBM, Oracle and Fortune 500 organisations. Find us on:🎧 Apple – https://bit.ly/3D0Ro8Y🎧 Spotify – https://bit.ly/4381oaU🎧 YouTube – https://bit.ly/41sJf6I 👉 Hit subscribe and join us on the journey.  Connect with the host - https://www.linkedin.com/in/ben---parker/  

정보

  • 제작진
    Ben Parker
  • 방송 연도
    2023년 - 2026년
  • 에피소드
    74
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    무삭제판
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    © 2026 Data Analytics Chat
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    Data Analytics Chat

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