Data Analytics Chat
- Épisodes
- 82
- Parution
- hebdomadaire
- Dernier épisode
- 10 Nov 2026
À propos du podcast
Data Analytics Chat explores how leading organisations are building, scaling and transforming through Data & AI.
Hosted by Ben Parker, Founder of Parker B Associates, a specialist recruitment firm helping organisations hire Data, AI and Technology talent.
Each episode features senior Data, AI and Technology leaders discussing:
• what they’re building
• what’s getting in the way
• what’s working
• what they’ve learned
• what other leaders can apply
Topics include AI adoption, enterprise data, data platforms, transformation, leadership, talent and ROI.
24,000+ downloads
Featuring leaders from AWS, Google, IBM, Oracle and global enterprises.
New episode every Tuesday.
Follow and subscribe for practical conversations with the leaders actually building and scaling Data & AI.
Épisodes 82
What It Takes to Become a CIO Today
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 …
Notes de l’épisode
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 Organisations31:38 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!
Are Companies Wasting Money on Data & AI?
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 …
Notes de l’épisode
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!
Your AI Is Live. But Is It Actually Working?
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 …
Notes de l’épisode
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 PlaceConnect with our guests:
Ryan Stevens: https://www.linkedin.com/in/ryanstevens3/Thank you for listening!
Nestlé’s Chief Data & AI Officer on Scaling AI
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 …
Notes de l’épisode
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 AheadConnect with our guest:
Jessica Matheron: https://www.linkedin.com/in/jmatheron/Thank you for listening!
AI Is Moving Fast. Is Your Data Ready?
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 …
Notes de l’épisode
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!
They Tried 3 Times. Then AI Changed the Enterprise Data Build
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 …
Notes de l’épisode
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 JosephConnect 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!
Why Trust Is the Missing Link Between AI and ROI
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 …
Notes de l’épisode
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 daysConnect 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
From Legacy to AI: The Playbook for TransformationIn this episode of Data Analytics Chat, host Ben Parker interviews Echeyde Cubillo, a veteran technologist with experience from eBay to multiple startups, now …
Notes de l’épisode
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 Timestamps01:47 — 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!
From Data Projects to Data Products: Essential Skills for AI Leaders
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 …
Notes de l’épisode
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 valueThank you for listening!
The Future of Data Science & Data Engineering in the Age of AI
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 …
Notes de l’épisode
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 futureThank you for listening!
How To Make Successful Decisions In AI
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 …
Notes de l’épisode
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 leadersThank you for listening!
What It Really Takes to Adopt Generative AI at Scale
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 …
Notes de l’épisode
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 impactThank you for listening!
Why Most Organisations Aren’t Ready for AI — Even If They Think They Are
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 …
Notes de l’épisode
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 nextThank you for listening!
The Reality of AI Today: Beyond the Hype
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 …
Notes de l’épisode
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 thoughtsThank you for listening!
Why Hiring and Retaining Top AI Talent Has Become Harder Than Ever
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 …
Notes de l’épisode
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 hiringThank you for listening!
The Hidden Cost of Poor Data Quality
With Carol Kim — Executive Director, IBM Can an organisation really be data-driven if its data can't be trusted? In this episode, Ben Parker speaks with Carol Kim about why Data Governance and Data Quality …
Notes de l’épisode
With Carol Kim — Executive Director, IBM
Can an organisation really be data-driven if its data can't be trusted?
In this episode, Ben Parker speaks with Carol Kim about why Data Governance and Data Quality are fundamental to better decision-making and what happens when organisations fail to get them right.
Carol explores how poor-quality data affects far more than technology, creating hidden costs across decision-making, operations and the wider business.
Drawing on her journey from finance into technology and Data and AI leadership, Carol also discusses the importance of curiosity, storytelling and authentic leadership — and the lessons she's learned navigating transformation across different cultures and environments.
You’ll hear practical insights on:
- Why trusted data is essential for better decision-making
- The hidden business costs of poor Data Quality
- Where Data Governance typically breaks down
- How people, process and technology need to work together
- What organisations need to build effective Data Governance
- Why storytelling matters for Data leaders
- The importance of curiosity and continuous learning in leadership
Chapters
00:00 The importance of continuous learning
02:01 Carol's career and leadership journey
07:54 Why storytelling matters in Data
09:59 Leadership and career transformation
15:13 Why Data Governance & Data Quality matter
24:06 Final thoughtsThank you for listening!
AI Agents: From Hype to Enterprise Value
With Ilya Meyzin — SVP, Head of AI Solutions at Dun & Bradstreet Are AI agents genuinely the next major shift in enterprise AI or are many still sophisticated workflows wrapped around LLMs? In this episode, Ben …
Notes de l’épisode
With Ilya Meyzin — SVP, Head of AI Solutions at Dun & Bradstreet
Are AI agents genuinely the next major shift in enterprise AI or are many still sophisticated workflows wrapped around LLMs?
In this episode, Ben Parker speaks with Ilya Meyzin about what AI agents can actually do today and what it takes to turn experimentation into real business value.
Ilya explores how agents differ from traditional AI systems, where the strongest use cases are emerging, and why many promising pilots struggle when organisations attempt to scale them.
The conversation also examines data quality, enterprise architecture, cross-functional collaboration and whether AI agents will become a fundamental part of how organisations operate.
You’ll hear practical insights on:
- What AI agents can do that traditional LLMs cannot
- How to identify genuinely valuable agentic use cases
- Why AI agent pilots struggle to scale
- The difference between orchestration and genuine intelligence
- Why data quality remains critical
- How teams need to collaborate to deploy agents successfully
- Whether agents will become embedded in enterprise architecture
Chapters
00:00 Introduction
01:14 Ilya's career and leadership journey
14:45 Balancing technical expertise with business understanding
23:18 What AI agents actually are
28:40 Where AI agents create real value
32:14 Building and scaling agents in the enterprise
37:21 Why data quality matters
45:12 The future of AI agentsThank you for listening!
The Power of Personalisation: How AI Influences What We Buy
With Allison Olson — SVP, Analytics Solutions at Merkle How much of what we buy is influenced by AI without us even realising it? In this episode, Ben Parker speaks with Allison Olson about how AI and real-time …
Notes de l’épisode
With Allison Olson — SVP, Analytics Solutions at Merkle
How much of what we buy is influenced by AI without us even realising it?
In this episode, Ben Parker speaks with Allison Olson about how AI and real-time data are transforming personalisation and changing the way brands understand, influence and interact with their customers.
Allison explores the shift from static recommendations to dynamic experiences that adapt to customer behaviour in real time, the data required to make this possible, and where personalisation can cross the line from helpful to intrusive.
The conversation also examines how Generative AI could transform customer experiences further and what the next generation of personalisation might look like.
You’ll hear practical insights on:
- How AI influences the decisions customers make
- The shift from static to dynamic personalisation
- How real-time data creates adaptive customer experiences
- Balancing personalisation with privacy and trust
- The challenges of personalising experiences at scale
- How Generative AI could reshape customer interactions
- What the future of personalisation could look like
Chapters
00:00 Introduction
02:12 Allison's career and leadership journey
08:37 Learning from failure and taking risks
13:45 Lessons in leadership
21:01 How AI is changing personalisation
23:23 Static vs dynamic personalisation
30:42 Balancing personalisation with privacy
35:12 Delivering personalisation at scale
38:16 How Generative AI changes what's possible
43:57 Final thoughtsThank you for listening!
What It Really Takes to Scale Generative AI
With Viji Krishnamurthy — VP & Head of AI and Generative AI at Oracle Why do some organisations turn Generative AI into real business value while others struggle to move beyond experimentation? In this episode …
Notes de l’épisode
With Viji Krishnamurthy — VP & Head of AI and Generative AI at Oracle
Why do some organisations turn Generative AI into real business value while others struggle to move beyond experimentation?
In this episode, Ben Parker speaks with Viji Krishnamurthy about what it really takes to implement and scale Generative AI inside the enterprise.
Drawing on more than two decades across research, startups, Samsung and Oracle, Viji explores the technical, cultural and organisational challenges that emerge as companies move GenAI into production.
The conversation examines how organisations can demonstrate real business value, manage risks such as hallucinations and bias, build trust in AI systems, and prepare for a future where people increasingly work alongside AI agents.
You’ll hear practical insights on:
- Why Generative AI initiatives struggle to scale
- Where GenAI is creating real business value
- Moving from experimentation into production
- Managing hallucinations, bias and trust
- The cultural changes required for successful AI adoption
- How the role of Data and AI professionals is evolving
- Why AI agents could change how we work
- What enterprise AI could look like over the next 3–5 years
Chapters
00:00 The emerging world of AI agents
02:08 Viji's career and leadership journey
07:41 From scientist to product leader
18:19 How Data & AI roles are evolving
27:43 Staying ahead as AI changes
41:08 Why Generative AI is difficult to scale
44:08 Where GenAI is delivering business value
48:52 Managing hallucinations, bias and trust
52:09 The organisational shift required for GenAI
57:05 AI agents and the future of workThank you for listening!
How to Measure the Real ROI of Data & AI
With Michael Shaw — SVP, Data & AI at Dow Jones How should organisations measure the real value of their Data and AI investments? In this episode, Ben Parker speaks with Michael Shaw about one of the biggest …
Notes de l’épisode
With Michael Shaw — SVP, Data & AI at Dow Jones
How should organisations measure the real value of their Data and AI investments?
In this episode, Ben Parker speaks with Michael Shaw about one of the biggest challenges facing Data and AI leaders: defining what success actually looks like.
Drawing on experience at Dow Jones, Google, Facebook and Instacart, Michael explores how organisations can connect Data initiatives to business objectives, balance quick wins with long-term value, and set realistic expectations with stakeholders.
The conversation also examines why ROI goes beyond financial returns, the importance of strong data foundations and adoption, and how AI is changing expectations around the value Data teams are expected to deliver.
You’ll hear practical insights on:
- How to define success for Data and AI initiatives
- Connecting Data investments to business outcomes
- Balancing quick wins with long-term value
- Why Data Quality and Governance affect ROI
- Driving adoption across the wider organisation
- Measuring value beyond direct financial returns
- How AI is changing expectations of ROI
Chapters
00:00 Introduction
01:58 Michael's career and leadership journey
28:08 Defining success and ROI in Data
32:04 Why strong Data foundations matter
33:05 Balancing stakeholder expectations and quick wins
44:31 Driving adoption and cultural change
54:31 How AI is changing expectations of ROIThank you for listening!
How to Build Effective Data Standards in Pharma
With Priya Gopal — Founder, Inferential Data LLC | Former Takeda & GSK Why are strong data standards so important in pharma and why are they so difficult to implement consistently? In this episode, Ben Parker …
Notes de l’épisode
With Priya Gopal — Founder, Inferential Data LLC | Former Takeda & GSK
Why are strong data standards so important in pharma and why are they so difficult to implement consistently?
In this episode, Ben Parker speaks with Priya Gopal about what it takes to build and implement effective data standards across complex pharmaceutical organisations.
Drawing on her experience across clinical research and major pharma companies including Takeda and GSK, Priya explores the importance of cultural readiness, the challenges of implementing standards across global teams, and how organisations can balance regulatory requirements with internal Data Governance.
The conversation also examines how better data standards can enable innovation, the growing role of AI and automation, and why greater collaboration across the industry could help pharma move faster.
You’ll hear practical insights on:
- Why data standards matter in pharma
- The biggest barriers to implementing standards consistently
- Balancing global regulation with internal Data Governance
- Why culture matters when driving Data transformation
- How stronger standards can enable innovation
- The role of AI and automation
- Why cross-industry collaboration matters
Chapters
00:00 Introduction
01:23 Priya's career and leadership journey
10:20 Lessons from Data transformation in pharma
20:34 Building effective Data standards
28:42 Balancing regulation and Data Governance
34:18 AI and automation in pharma
39:42 Collaboration and the future of pharma DataThank you for listening!
Core Data Principles That Drive Business Value
In this episode of Data Analytics Chat, we welcome Shourabh Mukherji, a data and analytics expert with 25 years of experience at major firms such as Chubb and JPMorgan Chase. Shourabh shares his journey from …
Notes de l’épisode
In this episode of Data Analytics Chat, we welcome Shourabh Mukherji, a data and analytics expert with 25 years of experience at major firms such as Chubb and JPMorgan Chase.
Shourabh shares his journey from starting a small enterprise in India to working with senior executives in the United States. The discussion examines his career milestones, key challenges, and the vital leadership skills required in the industry.
The data topic explores essential data management principles, the importance of aligning data strategies with business objectives, and the role of AI and new technologies in modern enterprises. Shourabh emphasises the significance of building strong data foundations and fostering empathy within teams to drive successful data-driven decisions.
00:00 A Defining Moment: Leap of Faith
01:19 Introduction to Data Analytics Chat
02:08 Shourabh Mukherji's Career Journey
07:07 Challenges and Defining Moments
09:47 Building Trust and Stakeholder Management
15:57 Key Skills for Leadership
22:32 Core Data Management Principles
25:38 Understanding Business Needs Before Data Management
26:42 Centralised vs. Embedded Data Quality Ownership
28:09 The Importance of Needs Analysis in Data Management
31:54 Identifying and Prioritising Business Needs
34:15 Building a Strong Data Solution Framework
40:05 Effective Data Distribution for Decision Making
42:53 Aligning Business Objectives with Data Initiatives
44:42 Evolving Data Management Strategies with AIThank you for listening!
How Hard Is It to Get Things Done in Data Projects
In this episode of Data Analytics Chat, we sit down with Juan Gorricho, a leader in data analytics with experience at top companies like Walt Disney, Visa, and TD Bank. Now the founder of Strategic Data …
Notes de l’épisode
In this episode of Data Analytics Chat, we sit down with Juan Gorricho, a leader in data analytics with experience at top companies like Walt Disney, Visa, and TD Bank.
Now the founder of Strategic Data Transformation, Juan shares his journey in the data field, the challenges of delivering data projects, the importance of aligning data projects with business goals, and managing expectations amid the AI hype.
He also provides leadership lessons, emphasising the value of empathy, listening, and team growth.
00:00 Introduction to Data Analytics Chat
00:07 Guest Introduction: Juan Gorricho Career Journey
01:00 Early Career and Education
01:37 The Intersection of Data, Technology, and Business
06:05 Challenges and Success in Data Projects
11:46 Managing Expectations in the AI Hype Cycle
15:25 Leadership Lessons and Team Management
23:36 Influences and Principles of Leadership
27:22 Breaking into Leadership: Overcoming Fears and Embracing Change
29:56 The Importance of Servant Leadership
30:45 Challenges in Data Projects: Business Alignment and Adoption
34:23 Human and Cultural Roadblocks in Data Projects
43:22 The Role of Communication in Data Project Success
52:03 Overcoming Obstacles and Achieving Success in Data Projects
Thank you for listening!
How Gen AI and Agents Are Reshaping The Game
9In this episode of Data Analytics Chat, we sit down with Dushyanth Sekhar, Head of AI and Data Platforms at S&P Global. Dush shares key insights from his career journey, emphasising the importance of learning …
Notes de l’épisode
9In this episode of Data Analytics Chat, we sit down with Dushyanth Sekhar, Head of AI and Data Platforms at S&P Global. Dush shares key insights from his career journey, emphasising the importance of learning from failures in the AI space.
He discusses his transition from a non-technology background to leading AI and data initiatives, the role of continuous learning, and the significance of setting a vision and promoting a risk-taking culture in leadership.
The conversation highlights the strategic implementation of generative AI and agents in transforming data workflows and enhancing company growth.
00:00 Embracing Failures in AI
01:07 Introduction to the Podcast and Guest
01:50 Career Journey and Early Beginnings
03:03 Transition to AI and Key Learnings
04:19 Defining Moments and Career Decisions
05:33 Moving to the US and Professional Growth
10:16 Leadership Insights and Risk-Taking
17:19 Staying Updated in a Fast-Paced Field
19:36 Excitement for the Future of AI
21:10 Overestimating Risk and Underestimating Opportunity
21:54 The Evolution of Language Models at S&P
23:49 Integrating AI into Workflows
28:31 Enhancing and Creating New Products with LLMs
30:54 Future-Proofing and Optimising with AI
37:19 The Role of Agents in AI
40:19 Lessons Learned from AI Integration
41:40 Conclusion and Final ThoughtsThank you for listening!
How Agentic AI Will Impact Business
In this episode of Data Analytics Chat, we welcome Sundip Gorai, Global Head of Data Science and AI at Global Payments, a Fortune 500 company. Sundip shares insights from his expansive career journey, which …
Notes de l’épisode
In this episode of Data Analytics Chat, we welcome Sundip Gorai, Global Head of Data Science and AI at Global Payments, a Fortune 500 company. Sundip shares insights from his expansive career journey, which spans across various industries like banking and manufacturing, and discusses how agentic AI is revolutionising business.
He offers insight into AI strategy, the challenges of adopting AI, and the importance of striking a balance between technology and ethics and social responsibility. Sundip also reflects on the future of work, the impact of AI on jobs, and the need for continuous learning in this rapidly evolving field.
00:00 Introduction and Initial Thoughts on Leadership
01:04 Welcome to Data Analytics Chat
02:18 Sundip Gorai's Career Journey
08:11 Defining Moments and Challenges in Career
17:17 Leadership Skills and Attributes
19:47 Meditation and Stress Management
24:10 Approaching AI Strategy
28:54 Choosing the Right Tools for AI Strategy
29:04 The Unchanging Layers of AI Strategy
30:01 The Obsession with Tools and Technologies
31:07 Understanding Agent AI
34:07 Industries Adopting Agent AI
39:01 Challenges in Deploying AI
43:15 Impact of AI on Jobs and Skills
49:10 Future of Business and Mankind with AIThank you for listening!
How a Measured Data Stack Can Impact Business Decisions
In this episode of Data Analytics Chat, host Ben welcomes Matthew Paruthickal, Global Head of Data and AI Architecture Engineering at Sanofi, to discuss the evolution of data technology from single-box systems …
Notes de l’épisode
In this episode of Data Analytics Chat, host Ben welcomes Matthew Paruthickal, Global Head of Data and AI Architecture Engineering at Sanofi, to discuss the evolution of data technology from single-box systems to agent AI.
They explore Matthew's career journey, the importance of solving business problems with technology, and the significant moments that shaped his approach to data. Topics include the layered data stack, overcoming technical and organisational challenges, the role of trust and speed in data, and how businesses can leverage AI for better decision-making.
00:00 The Evolution of Data and AI
00:48 Introduction to the Podcast and Guest
01:48 Matthew's Career Journey
02:17 Technological Shifts in Data Management
03:46 The Importance of Trust and Speed in Data
07:40 Defining Moments and Career Setbacks
09:46 Mentorship and Business Value
16:51 Building a Layered Data Stack
19:09 Understanding the Layered Data Stack
20:12 Challenges in Executing a Layered Data Stack
21:14 Top-Down and Bottom-Up Approach
22:07 The Importance of Business Focus
23:53 Aligning Data Stack with Strategic Decision Making
26:30 E-commerce Data Challenges and Solutions
29:14 Common Difficulties in Data Tool Investments
31:17 Evaluating New Data Tools
35:27 Balancing Automation with Real-Time Insights
37:12 Conclusion and Final ThoughtsThank you for listening!
Choosing The Right Signal on Human Intuition or AI Insight
In this episode of the Data Analytics Chat podcast, we welcome Sameer Sethi, Chief AI Officer at Hackensack Meridian Health, to discuss his exciting career journey and the evolving balance between human …
Notes de l’épisode
In this episode of the Data Analytics Chat podcast, we welcome Sameer Sethi, Chief AI Officer at Hackensack Meridian Health, to discuss his exciting career journey and the evolving balance between human intuition and AI-driven decision signals.
Sameer offers his perspective on the maturation of data analytics, the acceleration of technology adoption during the COVID-19 pandemic, and the role of AI in transforming healthcare delivery.
He also examines the risks associated with over-reliance on AI, the value of human-factor engineering in successful AI implementation, and why human judgment remains indispensable in high-stakes environments.
00:00 Introduction to Data and AI Journey
00:29 Impact of COVID on Technology Adoption
01:06 Welcome to the Podcast
01:41 Sameer's Career Journey
03:13 Pivot to Healthcare
06:22 Defining Moments in Healthcare Technology
14:18 Challenges and Setbacks
15:53 Importance of Mentorship and Self-Awareness
20:40 Networking and Experiencing the Field
23:40 Choosing the Right Data Signals
25:08 AI and Job Security: A Balanced Perspective
28:18 Human Intuition vs. AI Signals in Healthcare
29:56 The Importance of Human Factor Engineering
33:50 Ensuring Trustworthy Data Signals
35:32 The Future of AI in Process Automation
38:16 Blending Human Oversight with AI Decision Making
44:09 The Critical Role of Data in AI
46:18 Conclusion and Final Thoughts
Thank you for listening!
Personalised Medicine & Why The Right Data Matters
In this episode of the Data Analytics Chat podcast, host Ben Parker welcomes Steve Labkoff, Vice President for Development and Medical Analytics at Bristol Myers Squibb. Steve shares his incredible career …
Notes de l’épisode
In this episode of the Data Analytics Chat podcast, host Ben Parker welcomes Steve Labkoff, Vice President for Development and Medical Analytics at Bristol Myers Squibb.
Steve shares his incredible career journey, from starting as a traditional physician to pioneering roles in medical informatics and analytics across various pharmaceutical companies.
The discussion explores the intricacies of personalised medicine, highlighting the importance of identifying the right data and the challenges it presents. Steve also highlights the impact of life experiences on his career, the value of mentorship, and his innovative contributions, including the development of video game simulations for medical training and the establishment of a hospital for AIDS care in Africa.
00:00 Introduction to Data Analytics Chat
00:33 Steve Labov's Career Journey
02:09 Transition to Informatics
03:49 Major Projects and Achievements
09:54 Challenges and Setbacks
12:50 The Role of Mentorship
20:43 Personalised Medicine and Data
24:15 The Future of AI in Healthcare
32:14 Challenges in Data Interoperability
39:08 Conclusion and Final ThoughtsThank you for listening!
Building AI Without Foundations
In this episode of the Data Analytics Chat podcast, we explore the world of data and AI with Linda Powell, former Deputy Chief Data Officer. Linda shares insights from her unique career journey, which spans …
Notes de l’épisode
In this episode of the Data Analytics Chat podcast, we explore the world of data and AI with Linda Powell, former Deputy Chief Data Officer.
Linda shares insights from her unique career journey, which spans roles at the Federal Reserve, the Treasury Department, Citibank, and more. She discusses the differences and similarities between public and private sectors, and the importance of data foundations in AI. She offers advice for those looking to build their careers in data management.
00:00 Public vs. Private Sector: A Personal Insight
01:08 Introducing Linda Powell: Career Highlights
01:52 Linda's Career Journey: From Economics to Data Management
04:20 Public vs. Private Sector: Motivations and Misconceptions
10:57 Defining Moments and Career Setbacks
14:09 The Importance of Education and Curiosity
18:57 Overcoming Challenges and Embracing Leadership
21:56 Learning Leadership Through Books
22:43 The Importance of Kindness in Leadership
23:12 Building a Motivated Team
24:00 Leadership by Example
25:33 Transition to Data and AI
26:06 The Hype and Strategy of AI
27:20 The Speed and Complexity of AI
30:42 The Importance of Data Foundations
36:05 AI in Sensitive Industries
39:34 Balancing Quick Wins and Long-Term AI Strategy
42:29 Starting Points for AI Foundations
44:11 Conclusion and Final ThoughtsThank you for listening!
Why Every Company Needs a Data Platform Strategy
In this episode of Data Analytics Chat, host Ben Parker interviews Manoj Mohan, an engineering executive with experience at Intuit, Meta, Apple, and Cloudera. Manoj shares his journey from software engineering …
Notes de l’épisode
In this episode of Data Analytics Chat, host Ben Parker interviews Manoj Mohan, an engineering executive with experience at Intuit, Meta, Apple, and Cloudera.
Manoj shares his journey from software engineering to data leadership, offering invaluable insights into building resilient data platforms and driving innovation in the world of data and AI.
Manoj discusses pivotal moments in his career, the challenges of moving from technical roles to leadership, and the importance of learning from both successes and failures. He emphasises the need for a data platform strategy over simply assembling a stack of tools, and shares practical advice for newcomers and leaders alike.
The conversation also covers the pitfalls of focusing on quick technology wins, the critical role of governance, and how to future-proof your data strategy in an era of rapid AI advancement.
00:00 Treating Data as a First-Class Citizen
01:19 Welcome & Guest Introduction
01:46 Manoj’s Career Journey & Background
02:45 Transition from Software to Data Engineering
05:06 Moving into Engineering Leadership
07:11 Advice for Newcomers: Learning & Unlearning
09:12 Defining Moments in Manoj’s Career
12:05 Lessons from Career Setbacks
15:03 The Importance of Data Platform Strategy
17:18 Data Stack vs. Data Platform Strategy
21:06 Evaluating and Choosing Data Tools
24:04 Why Companies Buy Tools Instead of Building Strategy
27:13 Real-World Example: Great Stack, Failed Strategy
30:00 Governance and Business-Tech Alignment
33:00 Building a Scalable, Future-Proof Data Platform
36:00 The Role of Data Platforms in AI & Innovation
41:00 Final Thoughts & FarewellThank you for listening!
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