Data Analytics Chat

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!