Data Analytics Chat

Ben Parker
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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.  

  1. há 6 dias

    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, 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!

  2. 20 de out.

    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 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!

  3. 13 de out.

    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 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!

  4. 6 de out.

    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 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!

  5. 29 de set.

    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 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!

  6. 22 de set.

    From Legacy to AI: The Playbook for Transformation

    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!

  7. 4 de fev.

    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 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!

  8. 29 de jan.

    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 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!

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Sobre

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.  

Informações

  • Criado por
    Ben Parker
  • Anos de atividade
    2023 - 2026
  • Episódios
    80
  • Classificação
    Explícito
  • Copyright
    © 2026 Data Analytics Chat
  • Site do podcast
    Data Analytics Chat

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