Data Analytics Chat

Hosted by Ben Parker

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Oct 2026

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About the show

Data Analytics Chat explores how leading organisations are building, scaling and transforming through Data & AI. Hosted by Ben Parker, Founder of Parker B Associates, a specialist recruitment firm helping organisations hire Data, AI and Technology talent. Each episode features senior Data, AI and Technology leaders discussing: • what they’re building • what’s getting in the way • what’s working • what they’ve learned • what other leaders can apply Topics include AI adoption, enterprise data, data platforms, transformation, leadership, talent and ROI. 24,000+ downloads Featuring leaders from AWS, Google, IBM, Oracle and global enterprises. New episode every Tuesday. Follow and subscribe for practical conversations with the leaders actually building and scaling Data & AI.

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60 recent

October 20, 2026Episode 7949 min

Nestlé’s Chief Data & AI Officer on Scaling AI

In this episode of Data Analytics Chat, we welcome Jessica Matheron, Chief Data & AI Officer at Nestlé. Jessica brings a rare mix of experience to the role, from a French engineering and mathematics background and a Harvard MBA to a decade trading structured credit on Wall Street, followed by roles across fintech, ad tech and e-commerce before joining Nestlé. Jessica shares how she took a data team that was seen as a service centre, built on Excel and Power BI and reliant on external vendors, and rebuilt it into an internal department able to deliver GenAI at scale. The discussion examines what was really slowing projects down, why the bottleneck was upstream in the data rather than the data science, and how a single consumer data foundation on Segment and Snowflake made it possible to engage tens of millions of consumers across Europe. The data topic explores the three flavours of GenAI, from everyday copilots to coding copilots that more than doubled productivity and the AI agents now in production. Jessica also covers winning trust from the CEO down, why organisational change and technology can't be separated in a 270,000-person company, how to build a business case before measuring value, and why redesigning end-to-end workflows around AI is the biggest opportunity ahead. 01:20 Introduction to Data Analytics Chat 02:01 Jessica's Background and Career Journey 04:05 When Things Were at Their Messiest at Nestlé 05:59 How It Was Affecting the Business 07:09 The Turning Point for Change 10:51 Fixing the Data Before GenAI 12:48 Where GenAI First Created Real Value 16:41 The Moment GenAI Proved It Worked 17:55 What Had to Change in Data, Technology and Operating Model 21:23 Moving From a Promising Use Case to Scale 24:19 Winning Trust Across the Organisation 26:57 Technology vs Changing the Organisation 31:50 How AI Helps Engage Tens of Millions of Consumers 34:08 Measuring Whether GenAI Creates Value 36:50 Surprises of Operating at Scale 40:01 What She Got Wrong and Would Do Differently 42:11 Where Enterprises Should Focus First 45:24 The Biggest Opportunity Ahead Connect with our guest: Jessica Matheron: https://www.linkedin.com/in/jmatheron/ Thank you for listening!

October 13, 2026Episode 7846 min

AI Is Moving Fast. Is Your Data Ready?

In this episode of Data Analytics Chat, we welcome Erica Howard, Head of Consumer Data Ownership at TD. Erica has spent 18 years at the bank, starting in the call centre before working on TD's early data governance model in 2012, moving into analytics, and returning to data management to bridge the gap between technology and the business. Today she works with business segments to embed data management practices and owns the bank's customer master data. Erica shares how data governance in banking took shape after the 2008 housing crisis, when regulators began asking not only for reports but for proof of how they were produced. The discussion examines why ownership, rather than technology, is the biggest hurdle, how she has built a hub model with segment data owners across the business, and the challenge of telling the data story in a language business leaders understand. The data topic explores why AI raises the stakes for getting data foundations right, from bias in historical credit data to the careful use of synthetic data. Erica also explains why data management should be a recurring strategic investment rather than a one-off project, what good data ownership looks like in practice, and what she would advise leaders to do in their first 90 days. 01:15 Introduction to Data Analytics Chat 04:09 When Data Governance Became Critical 07:28 The Problems Exposed by the Housing Crisis 09:14 Why Governance Wasn't Already in Place 12:45 The Biggest Hurdle: Ownership, Quality or Access? 15:51 When the Issue Wasn't the Technology 17:59 Getting the Message Across to Business Leaders 21:37 Building Stronger Data Ownership with a Hub Model 25:11 Is AI Helping Data Governance? 26:43 Changing How People Think About Ownership and Accountability 29:12 Lessons Other Organisations Can Learn 31:46 What Has Been Harder Than Expected 32:42 Why AI Raises the Stakes for Data Foundations 37:26 What Good Data Ownership Looks Like 39:48 Impact on Customer Trust and Business Value 41:11 Advice for Leaders Starting Out: The First 90 Days 42:40 Will Data Governance Become a Major Investment Area? Connect with our guest: Erica Howard: https://www.linkedin.com/in/erica-m-howard-65850527/ Thank you for listening!

October 6, 2026Episode 7746 min

They Tried 3 Times. Then AI Changed the Enterprise Data Build

In this episode of Data Analytics Chat, we welcome Mary Alfheim, VP of Data and Analytics, and Joseph Kroon, Data Architect, both from Headspace. Mary previously led analytics teams at Amazon Prime Video and HBO, while Joseph spent time in Cigna's data organisation and, earlier, as a Black Hawk helicopter crew chief in the National Guard. Mary and Joseph share how Headspace rebuilt its enterprise data model after three earlier attempts failed to keep pace with the business. The discussion examines the trust problem behind the project and why organisational alignment and technology have to move together. The data topic explores how AI changed the build: generating the data model from data contracts, mapping end-to-end lineage, and letting business users self-serve trusted numbers through AI agents. They also cover keeping humans in the loop, evaluating accuracy, and why leaders should agree on the use case before choosing the tools. 00:00 They Tried 3 Times: A Paradigm Shift in Building Data 01:22 Introduction to Data Analytics Chat 02:04 Meet Mary Alfheim and Joseph Kroon from Headspace 04:16 The Original Problem: A Crisis of Trust in Data 14:02 Three Attempts and the Lessons Learned 19:44 When AI Became Part of the Solution 28:43 Combining AI with Human Expertise 32:44 What Building Enterprise Data with AI Means in Practice 35:06 Making Sure AI Output Is Accurate Enough to Trust 39:37 Driving Adoption Across the Organisation 40:24 What Changed for the Business 41:46 What They Would Do Differently Next Time 43:17 Lessons for Other Large Organisations 44:09 The First Step for Data and Analytics Leaders 45:37 Where to Connect with Mary and Joseph Connect with our guests: Mary Alfheim: https://www.linkedin.com/in/mary-alfheim/ Joseph Kroon: https://www.linkedin.com/in/joseph-kroon-902243109/ Thank you for listening!

September 29, 2026Episode 7659 min

Why Trust Is the Missing Link Between AI and ROI

Trust in AI: How Do You Build Trust That Creates Real Business Value? Companies are pouring billions into AI, but investment alone doesn't deliver value. In this episode, Ben Parker sits down with Paul Drennan to unpack why trust is the missing link between AI spending and real business outcomes. They explore what builds — and breaks — trust in AI systems, how organisations can balance encouraging adoption with maintaining healthy scepticism, and why technically excellent AI can still fail without user confidence. From managing hallucination risks and human-in-the-loop decisions to measuring genuine ROI, Paul shares practical steps leaders can take within 90 days to move from AI experimentation to measurable, trusted results. Timestamps: 1:59 — Quick introduction: Who is Paul Drennan? 4:25 — How much of the AI value problem comes down to trust? 8:26 — What needs to happen before employees and leaders are comfortable relying on AI? 13:13 — Can technically excellent AI still fail without trust and adoption? 15:35 — What are the biggest things that cause people to lose trust in AI? 16:34 — How should organisations manage the risk of convincing but wrong AI answers? 20:36 — Finding the balance between encouraging AI use and questioning its outputs 23:42 — Where should human judgement remain as organisations automate more decisions? 29:22 — What is the relationship between trust, adoption, and ROI? 33:12 — How should leaders measure genuine business value from AI initiatives? 34:40 — What should organisations put in place across technology, data, governance, and people? 43:25 — How should organisations continue testing and monitoring AI to maintain trust? 47:42 — What are successful organisations doing differently to build trust and adoption? 53:17 — The first meaningful action to improve trust and ROI in 90 days Connect with guest: https://www.linkedin.com/in/pauldrennan/ Connect with host: https://www.linkedin.com/in/ben---parker/ Thank you for listening!

September 22, 2026Episode 7544 min

From Legacy to AI: The Playbook for Transformation

From Legacy to AI: The Playbook for Transformation In this episode of Data Analytics Chat, host Ben Parker interviews Echeyde Cubillo, a veteran technologist with experience from eBay to multiple startups, now leading AI transformations in enterprises. They explore why large organizations struggle with AI adoption despite greater resources than startups. The real barrier isn't outdated technology—it's legacy thinking, hierarchies, and silos. Echeyde reveals that small, empowered teams connected directly to customers outperform massive structures. The conversation offers a practical playbook: flatten organizations, bring entrepreneurial talent into leadership, focus on builders over managers, and embrace experimentation. Essential insights for leaders navigating digital transformation in the AI era. Question Timestamps 01:47 — Before we dive in, could you give listeners a quick introduction to who you are and the transformation work you're leading today? 03:31 — Large organisations often have more resources than start-ups, so why do they frequently find transformation much harder? 11:50 — When we talk about legacy, is outdated technology really the biggest problem, or are legacy thinking, processes and incentives even harder to change? 15:24 — What does an entrepreneurial mindset actually look like inside a large organisation? 19:03 — How can leaders create greater speed, ownership and experimentation without losing the governance and controls a large organisation needs? 23:28 — When an organisation has dozens of potential AI opportunities, how should it decide where to start? 25:22 — Do companies need to modernise their entire technology and data environment first, or can they begin delivering AI value while legacy systems are still in place? 28:49 — How can organisations move away from lengthy transformation programmes and start testing ideas quickly, learning from users and proving value early? 30:44 — Transformation can challenge existing roles, budgets and ways of working. How should leaders bring employees and stakeholders with them? 34:41 — Many organisations can launch successful pilots. What prevents those pilots from becoming scaled, business-critical solutions? 36:28 — How do companies make continuous innovation part of how the organisation operates, rather than treating transformation as a one-off programme? 39:43 — To bring the playbook together, what are the most important steps leaders should remember when moving from a legacy environment into the AI era? 43:23 — If a leadership team wanted to begin this journey over the next 90 days, what is the first meaningful action you would encourage them to take? 44:30 — Finally, for anyone listening who would like to continue the conversation with you, where is the best place to connect? Connect with guest: https://www.linkedin.com/in/echeyde/ Connect with host: https://www.linkedin.com/in/ben---parker/ Thank you for listening!

February 4, 2026Episode 7442 min

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

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!

January 29, 2026Episode 7337 min

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

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!

January 21, 2026Episode 7223 min

How To Make Successful Decisions In AI

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!

January 14, 2026Episode 7139 min

What It Really Takes to Adopt Generative AI at Scale

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!

January 7, 2026Episode 7030 min

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

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!