Data Analytics Chat

In this episode of Data Analytics Chat, we welcome Lior Barak, Author & Founder of Impact Operations. After 15+ years in data organisations, Lior now helps data teams and their leadership treat data and AI as a capital allocation decision, so teams can show what their work returns, not just what it costs.

Lior is the author of What Data Really Costs, a business fable about the hidden cost of data: the "zombie taxes" and "FTE debt" that organisations pay every year when business teams spend their time checking, rebuilding and second-guessing data. Listeners can read the first two chapters of the book here: https://liorbarak.com/what-data-really-costs

We discuss how leaders should decide what is worth funding, when to stop a project, how to escape the POC trap, and how technical teams can stay focused on work that creates real business value.

00:00 Cold Open
01:24 Introduction to Data Analytics Chat
02:15 Introducing Lior Barak and His Background
03:29 Should Technical Teams See the Commercial Value and Cost of Their Work?
09:19 Where the Most Money and Time Is Wasted Across Data & AI
12:15 Why Companies Invest in Projects That Don't Create Business Impact
15:51 Questions Leadership Should Ask Before Funding a Data or AI Initiative
18:50 Building the Business Case Before the Team Starts Building
24:00 Communicating Value Effectively to Senior Stakeholders
27:29 What Are We Actually Getting Back for All This Data & AI Spend?
32:09 Deciding Which Projects to Continue, Deprioritise or Stop
34:19 The POC Trap: What Makes a Project Production-Worthy?
38:29 How Technical Teams Can Work on Problems That Matter, Not Just Tickets
39:52 What a High-Performing Data & AI Team Looks Like
45:22 What Leaders Should Audit First on Their Roadmap

Connect with our guests:
Lior Barak, Author & Founder, Impact Operations: https://www.linkedin.com/in/liorbarak/

Thank you for listening! 

In this episode of Data Analytics Chat, we welcome Ryan Stevens, Senior Director of Applied Science at Ramp, the financial technology company helping businesses save time and money across cards, bill pay, expense management, travel and more. Ryan leads a team of machine learning engineers and product data scientists, and before Ramp he worked on ads ranking at Meta. He also holds a PhD in economics.

Ryan shares how Ramp evaluates AI once it is live, from structured outputs like invoice OCR to unstructured chat agents like Stack, Ramp's AI accounting agent. The discussion examines why labels sit at the heart of every evaluation problem, how synthetic data can stand in before launch, and why so many teams reach for an agent before they actually need one.

The episode explores the three layers of production monitoring (errors and latency, business outcomes, and adoption), how much evaluation can be handed to LLM-as-a-judge versus human reviewers, and how leaders can tie evaluation back to business value by starting with the task and weighing its value against the cost of solving it.

00:00 Cold Open
01:02 Introduction to Data Analytics Chat
01:37 Meet Ryan Stevens and Ramp
02:59 How Do You Know Your AI Is Actually Working?
08:29 Accuracy, Quality, Safety or Business Outcomes?
11:26 Why Is AI Evaluation So Hard?
14:29 Has Generative AI Made Measurement Harder?
16:42 Good Evaluation Before Going Into Production
19:13 You Probably Don't Need an AI Agent Yet
20:01 Evaluation at Enterprises vs Startups
23:00 How Evaluation Changes Once a System Is Live
25:39 How Often Should Teams Check Performance?
27:05 Early Warning Signs Your AI Is Failing
28:11 Can AI Really Evaluate AI?
30:12 Using AI Agents to Test and Monitor Other AI
32:11 Your AI Works Technically. But Does It Create Value?
34:39 What a Strong Evaluation Process Looks Like
37:20 The First Thing to Put in Place

Connect with our guests:
Ryan Stevens: https://www.linkedin.com/in/ryanstevens3/

Thank you for listening! 

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! 

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! 

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! 

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!