Data Analytics Chat

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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!

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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!

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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!

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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!

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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!