Advertise on podcast: Data Analytics Chat

Send us Fan Mail 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!

Send us Fan Mail 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!

Send us Fan Mail 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!

Send us Fan Mail 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!

Send us Fan Mail 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!

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!

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!

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!

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!

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!

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!

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!

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!

Send us Fan Mail With Ilya Meyzin — SVP, Head of AI Solutions at Dun & Bradstreet   Are AI agents genuinely the next major shift in enterprise AI or are many still sophisticated workflows wrapped around LLMs?   In this episode, Ben Parker speaks with Ilya Meyzin about what AI agents can actually do today and what it takes to turn experimentation into real business value.   Ilya explores how agents differ from traditional AI systems, where the strongest use cases are emerging, and why many promising pilots struggle when organisations attempt to scale them.   The conversation also examines data quality, enterprise architecture, cross-functional collaboration and whether AI agents will become a fundamental part of how organisations operate.   You’ll hear practical insights on:   What AI agents can do that traditional LLMs cannot How to identify genuinely valuable agentic use cases Why AI agent pilots struggle to scale The difference between orchestration and genuine intelligence Why data quality remains critical How teams need to collaborate to deploy agents successfully Whether agents will become embedded in enterprise architecture   Chapters   00:00 Introduction 01:14 Ilya's career and leadership journey 14:45 Balancing technical expertise with business understanding 23:18 What AI agents actually are 28:40 Where AI agents create real value 32:14 Building and scaling agents in the enterprise 37:21 Why data quality matters 45:12 The future of AI agents Thank you for listening!

Send us Fan Mail With Allison Olson — SVP, Analytics Solutions at Merkle   How much of what we buy is influenced by AI without us even realising it?   In this episode, Ben Parker speaks with Allison Olson about how AI and real-time data are transforming personalisation and changing the way brands understand, influence and interact with their customers.   Allison explores the shift from static recommendations to dynamic experiences that adapt to customer behaviour in real time, the data required to make this possible, and where personalisation can cross the line from helpful to intrusive.   The conversation also examines how Generative AI could transform customer experiences further and what the next generation of personalisation might look like.   You’ll hear practical insights on:   How AI influences the decisions customers make The shift from static to dynamic personalisation How real-time data creates adaptive customer experiences Balancing personalisation with privacy and trust The challenges of personalising experiences at scale How Generative AI could reshape customer interactions What the future of personalisation could look like   Chapters   00:00 Introduction 02:12 Allison's career and leadership journey 08:37 Learning from failure and taking risks 13:45 Lessons in leadership 21:01 How AI is changing personalisation 23:23 Static vs dynamic personalisation 30:42 Balancing personalisation with privacy 35:12 Delivering personalisation at scale 38:16 How Generative AI changes what's possible 43:57 Final thoughts Thank you for listening!