Directionally Correct: Data 2030

Directionally Correct: Data 2030 is an educational podcast for data and analytics professionals, hosted by Lucas Thelosen, co-founder and CEO of Orion by Gravity.

In this episode, Lucas talks with Frank Tingley, Founder and Principal Advisor at Tome Advisory, about the analyst's path from answering requests to owning data products, why small gains compound over time, and why executives should spend three to five percent of their time engaging with the data. Frank has spent more than a decade in data analytics, across Fortune 500 companies and high-growth direct-to-consumer brands.

What you will learn

• How segmenting products across sales channels lifted dot-com revenue by about five percent
• Why analysts are becoming full-stack
• Why context management matters more as AI automates hard skills like SQL and dashboard building
• What to look for when hiring analysts in the age of AI
• Why the 95th percentile of data work is not good enough

Resource mentioned 95%-ile isn't that good, by Dan Luu https://danluu.com/p95-skill/

Chapters

0:00 Welcome and guest intro

0:31 Career path from finance to analytics and advisory

4:01 Where analytics has driven impact

4:44 A channel segmentation example and how AI shifts the work

8:49 Team structure and the full-stack analyst

13:15 Context management and why hard skills matter less

15:42 How to hire and interview analysts in the age of AI

17:44 Advice for a 25-year-old analyst

18:25 Compounding returns in analytics

23:07 Data-first culture and the urgency of every week

25:40 Why the 95th percentile is not good enough

29:54 What executives should do to engage with data

34:25 Closing thoughts

Directionally Correct: Data 2030 is an educational podcast for data and analytics professionals, hosted by Lucas Thelosen, co-founder and CEO of Orion by Gravity.

In this episode, Lucas talks with Ratheesh Kamoor of Warner Bros. about two decades in tech, from the dot-com bust to building data platforms for direct-to-consumer subscription products. They cover what makes a data foundation ready for AI agents, how to structure data teams, and where data careers are heading.

Ratheesh Kamoor is a technology executive and AI, Data & Analytics leader with 20+ years of experience building and scaling data-driven organizations, platforms, and intelligent solutions for global businesses. He specializes in bridging technology, data science, and business strategy to transform complex data into actionable intelligence, accelerate innovation, and drive measurable business outcomes.

Ratheesh currently serves as an executive at Warner Bros. Discovery, leading Data & AI initiatives that advance customer intelligence, analytics, and AI-powered business transformation. Prior to WBD, he held leadership positions at Disney and multiple startups, building high-performing teams and delivering data and technology capabilities at scale.

A passionate advocate for AI, generative AI, and open-source technologies, Ratheesh is focused on translating emerging innovations into practical business solutions that improve decision-making, unlock growth, and create lasting value.

What you will learn

• Why slow data is almost no data, and how lakehouse and lambda architectures help
• How to extend an existing data and ML platform so agents can run on it
• When a centralized, hub and spoke, or hybrid data team fits best
• How data engineer, analytics engineer, and product engineer roles are changing
• Career advice for early-career technologists in the age of AI

Chapters

0:00 Welcome and guest intro

0:17 From the dot-com bust to building data platforms

4:29 Why streaming was an early home for AI

5:44 Building a data foundation for AI agents

9:51 Centralized data platforms and a single semantic layer

16:24 How data roles are changing in the age of AI

21:32 Business and product skills, and managing agents

23:35 Career advice for early-career technologists

26:30 Is AI making everyone a manager?

28:36 More startups, more building, and bigger productivity gains