Data Analytics Chat

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!

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!

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!

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!

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!