"Being clear on what you're trying to achieve is incredibly powerful. Ask 'What am I trying to do here? What is the outcome we're trying to achieve? What does success look like?'" — Lydia Monnington [0:30:00]
As a data leader, one of your most crucial decisions is how to structure your team. Should you centralize for consistency, embed for domain expertise, or find a middle ground?
In this episode of More than Numbers Live, Ollie Hughes sits down with Lydia Monnington, Data Science Lead at Google DeepMind, to explore this perennial challenge facing data organizations of all sizes.
With experience leading data teams at Meta, Ocado, Stuart, and now Google DeepMind, Lydia brings practical insights on what works (and what doesn't) when organizing data professionals. She breaks down the evolution of data team structures and provides actionable advice on making the most of your talent—regardless of your current setup.
In our conversation, Lydia shares:
- The natural evolution from centralized to embedded teams and the pitfalls to avoid in each model
- Why a matrix/hybrid model often delivers the best results and how to successfully implement it
- Practical approaches to managing specialized roles like data engineers, analysts and scientists
- The critical importance of establishing strong relationships between your data team and business stakeholders
Whether you're managing a small centralized team, leading embedded analysts, or navigating the complexities of a hybrid structure, this episode offers valuable frameworks to make your data organization more effective.
Brilliant data bits
"Often if you're sat centrally, you don't have that depth of relationship with the people you're working with... so you're kind of like a data factory or data treadmill. You're producing stuff that might not answer what they actually need." — Lydia Monnington [0:12:52]
"The ideal hybrid structure is powerful because you're sat close to the teams and understand what's important, but you report to a data leader, so you have the independence to say, 'Hey, I think our strategy is wrong.'" — Lydia Monnington [0:19:40]
What you'll learn
- [01:10] Lydia's journey from financial modeling at Citigroup to data science at Google DeepMind.
- [03:45] Inside DeepMind's data challenges: from fusion to forecasting typhoons.
- [05:29] How a 15-person central team supports DeepMind's data-intensive organization.
- [07:32] Common misconceptions about "perfect" data in big tech companies.
- [10:56] The evolution of data team structures: from centralized beginnings to embedded specialists.
- [12:08] Why centralized teams often become bureaucratic "data factories" as organizations scale.
- [15:00] The potential pitfalls of fully embedded teams: inconsistent tooling and duplicated work.
- [18:30] Matrix/hybrid structures: getting the best of both worlds with the right relationships.
- [22:20] Practical steps to move toward a hybrid model from either extreme.
- [25:40] How to approach structuring specialized roles like data engineers vs. analysts.
- [29:24] Lydia's career-defining advice: focus on outcomes and the "why" behind your analysis.
"For every two or three data scientists, you should have one data engineer—this ratio helps your team be much more effective." — Lydia Monnington [0:27:07
Downloads & links
👋 Connect with Lydia on LinkedIn
🗞️ Subscribe to Lydia's newsletter
🔗 Learn more about Google DeepMind
🚀 Connect with Ollie Hughes
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