Data Analytics Chat

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!

With Nayan Paul — Managing Director & Chief Architect, Generative AI at Accenture
 
Why are so many organisations experimenting with Generative AI, but so few turning it into meaningful business impact?
 
In this episode, Ben Parker speaks with Nayan Paul about what it really takes to move GenAI from experimentation into production and scale.
 
They explore why successful adoption isn't simply a technology challenge. It requires business ownership, the right operating model, strong data foundations and a clear approach to governance.
 
The conversation also examines how organisations can move quickly without sacrificing trust and responsibility and what separates AI experimentation from genuine business transformation.
 
You’ll hear practical insights on:
 
Why GenAI pilots struggle to reach production 
Moving from experimentation to measurable business value 
Why business ownership matters as much as technology 
Building the foundations for GenAI at scale 
Creating an effective AI operating model 
Balancing speed, governance and responsibility 
Turning GenAI from an experiment into an organisational capability 
 
Chapters
 
00:00 Why scaling Generative AI is difficult
01:08 Meet Nayan Paul
05:08 Early GenAI experiments and lessons
07:02 Moving from experimentation to business value
10:14 Driving adoption across the business
16:15 Building the foundations for AI at scale
29:24 Balancing speed with responsibility
34:09 Moving from curiosity to impact
Thank you for listening!

With Sujit Narapareddy — Head of Data & Analytics, AWS Sales
 
What separates organisations experimenting with AI from those actually changing how work gets done?
 
In this episode, Ben Parker speaks with Sujit Narapareddy about what it really takes to embed AI into an organisation and why technology is only part of the challenge.
 
Sujit explores the importance of human judgement, strong data foundations and leadership alignment, alongside the organisational changes required to move from AI experimentation to real adoption.
 
The conversation also examines how AI could reshape everyday work by embedding intelligence directly into workflows, helping people move faster from insight to action without removing the need for human judgement.
 
You’ll hear practical insights on:
 
 Why organisations underestimate what AI adoption really requires 
 What separates AI experimentation from real adoption 
 Why strong data foundations still matter 
 How AI can complement people rather than simply replace roles 
 How leaders should rethink teams and decision-making 
 Why human judgement becomes more important, not less 
 What organisations should be doing now to prepare 
 
Chapters
 
00:00 The challenge of AI transformation
01:42 Meet Sujit Narapareddy
02:32 Sujit’s journey from technology to leadership
09:21 How AI changes human roles
16:17 Why organisations struggle to integrate AI
28:28 Preparing organisations for what comes next
Thank you for listening!