Data Analytics Chat

In this episode of Data Analytics Chat, we welcome Saadia Khilji, Global Chief Information Officer at Coverys, a medical professional liability insurer that has served the industry for more than 40 years. Based in Boston, Saadia has spent more than 20 years in IT across financial services and insurance, including Travelers, The Hartford and MassMutual, leading different parts of the technology organisation before stepping into the global CIO role.
Saadia shares how her ambition to become a CIO took shape once she became an executive, and why a risky two-to-three-year move out of IT to shape C-suite strategy gave her an edge. She explains why IT should not be an order taker, why more than half of her time goes on work beyond running technology, and why CIOs will be held accountable not just for implementing AI, but for the ROI it delivers.
The discussion explores the capabilities future CIOs will need, letting go of the hero mentality, data-driven prioritisation, balancing quick wins with long-term foundations, building the right team, staying calm under pressure, and the one habit that will make aspiring CIOs stand out: connecting the dots.
00:00 Cold Open
00:59 Introduction to Data Analytics Chat
01:25 Introducing Saadia Khilji and Her Background
02:29 Did Saadia Always Want to Reach the C-Suite?
04:19 What People Often Misunderstand About the CIO Role
06:34 How Expectations on Senior Technology Leaders Have Changed
08:39 What AI Now Requires From a CIO
10:36 Stepping Outside IT to Learn How the Business Works
13:43 The Capabilities That Matter Most for Future CIOs
16:10 When to Stop Being the Expert in the Room
19:52 Where a CIO Should Focus and What Good Prioritisation Looks Like
22:00 Balancing Quick Wins With Long-Term Foundations
26:25 Building and Leading the Right Team as AI Reshapes Tech Organisations
31:38 The Hardest Part of Being a CIO
32:17 How to Stay Calm Under Pressure
36:01 One Thing Aspiring CIOs Should Start Doing Now

Connect with our guests:
Saadia Khilji, Global Chief Information Officer, Coverys: https://www.linkedin.com/in/saadia-khilji-executivecio/
Thank you for listening!

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!