In this episode of Data Analytics Chat, we welcome Elena Alikhachkina, a four-time Chief AI and Data Officer with Fortune 500 companies, and a board advisor. Elena shares her journey in data analytics and AI spanning over 25 years, covering key transitions in her career and the importance of blending business acumen with technical expertise.

The discussion focuses on the critical shift from data projects to data products, highlighting the need for product skills in the age of AI. Elena emphasises the importance of understanding the business, building customer-centric solutions, and investing in both technical and soft skills for AI leaders. Her insights provide a roadmap for anyone looking to excel in data and AI roles in today's dynamic business environment.

00:00 Introduction to the Factory Process and Data Science Challenges
01:00 Welcome to Data Analytics Chat with Elena Alikhachkina
01:33 Ena's Career Journey and Insights
04:14 The Importance of Understanding Business Processes
09:17 Shifting from Data Projects to Data Products
11:08 The Need for a Product Mindset in AI
15:06 Challenges in Adopting a Product Mindset
16:39 Encouraging Proactivity and Collaboration
18:08 The Role of Education in Driving Change
22:34 The Need for Reeducation in Technology and Data
22:50 Embedding Data and AI in Business Curriculum
23:14 The Changing Role of Data in Leadership
24:01 The Importance of Soft Skills in Tech
25:31 Trust in Data and Technology
26:30 Essential Skills for the Future
28:20 The Shift to Product-Centric Roles
29:50 Bridging the Gap Between Business and Tech
30:16 Investing in Personal Development
31:19 Understanding the Customer's Needs
34:00 The Value of Product Thinking in AI
35:47 Career Growth Through Business Understanding
41:03 Final Thoughts and Call to Action

Thank you for listening!

In this episode of Data Analytics Chat, we’re joined by Phoenix Pei, SVP, Analytics Manager at Truist, to explore how the roles of Data Scientists and Data Engineers are changing in the age of AI and automation.

Phoenix explains why trust, leadership alignment, and business understanding are becoming more critical than technical skills alone, and how generative AI is reshaping how data teams work. We discuss which skills will matter most in the future, why so many data initiatives struggle to deliver impact, and how organisations should rethink team structure and collaboration to stay relevant.

A practical conversation for leaders and practitioners who want to build data teams that actually deliver value in an AI-driven world.

00:00 Introduction to Scaling Analytics and AI
00:50 The Role of Leadership in Data Science
01:24 Exploring the Future of Data Roles
01:46 Guest Introduction: Phoenix Pei
02:18 Phoenix's Career Journey and Insights
09:34 The Evolution of Data Roles with AI
11:08 The Importance of Business Understanding in Data Science
24:54 Challenges in Data Science Implementation
25:07 Leadership Alignment and Data-Driven Decisions
33:53 Preparing Data Teams for the Future
37:17 Conclusion and Final Thoughts

Thank you for listening!

In this episode of Data Analytics Chat, Ben speaks with Durai Rajamanickam, a senior AI leader, about what it really takes to turn AI ambition into something organisations can trust and scale.

They discuss why many AI initiatives fail long before the technology becomes the issue, the risks of hype-driven decision-making, and the importance of clear goals, strong business ownership, and measurable outcomes.

The conversation explores the tension between speed and governance, when to build vs buy, and why trust and governance are not obstacles to progress, but foundations for sustainable AI adoption.

This is a practical, experience-led discussion for leaders who want to build AI capabilities that last.

00:00 Introduction: The AI Hype and Strategy Revisions
01:09 Guest Introduction: Meet Durai Rajamanickam
02:09 AI Initiatives: Technical Capabilities and Investments
03:46 Build vs Buy: Evaluating AI Tools
05:36 Common AI Missteps: Hype and Misdiagnosis
07:41 Successful AI Implementation: Business and Technical Alignment
09:11 Governance and Trust: The Key to Sustainable AI
09:47 Real-World Examples: When AI Falls Short
12:22 Balancing Speed and Governance in AI
15:22 Decision-Making in AI: Trust and Autonomy
21:03 Final Thoughts: Advice for AI Leadership
23:04 Conclusion: Wrapping Up the Discussion

Thank you for listening!