S4 | Ep 28 | Designing and Implementing the Correct Data Operating Model with Pete Youngs, Managing Partner at Ortecha

May 14, 2024

Pete Youngs, Managing Partner at Ortecha, dives into the intricacies of data operating models. He emphasizes their importance over data strategies, citing chaos in organizations lacking a model. Youngs discusses the difference between operating models and team structures and highlights why people are the most crucial element. He also explores trends like centralized versus decentralized models and the need for adaptability in the face of generative AI. Key insights include measuring success through clear metrics and fostering an organizational data culture for effective implementation.

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ANECDOTE

Pete’s Journey From Engineering To Orteca

Pete Youngs moved from automotive engineering into data after a computer science MSc and Oracle work.
He co-founded Orteca after meeting co-founders on a large MOD project.

INSIGHT

Central Plus Federated Is The Common Pattern

Most organizations use a central data team with federated accountability rather than full decentralization.
Size, regulation and tenure of the CDO drive common patterns like central capability plus federated execution.

ADVICE

Build Around Four Pillars

Design your data operating model across four pillars: people, process, data and technology.
Embed roles, responsibilities and tooling into OKRs and daily ways of working to make it real.

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In Episode 28, of Season 4, of Driven by Data: The Podcast, Kyle Winterbottom is joined by Pete Youngs, Managing Partner at Ortecha, where they discuss the key considerations of designing and implementing the correct data operating model and its importance in data and analytics success, which includes;

Why a data operating model is more important than your data strategy
The day-to-day chaos of living without an operating model
The relationship between D&A enablement and your operating model
Defining the MVP data operating model
Data operating models in the absence of data strategies
Why your operating model should be ever-evolving
The confusion between operating models and team structures
Why people are the most important part of your operating model
Why your data operating model can’t conflict with the business operating model
Trends around centralised versus decentralised structures
The relationship between team structure cycles and the tenure of the CDO
The importance of defining role profiles and managing with OKRs
Why size and scale are important factors in defining the best data operating model
Why many data operating models are created by data governance or strategy teams
The relationship between data operating models and delivering value
The importance of a value-driven mindset and having a value-case
Balancing value realisation while building your data operating model
Why not enough focus or time is put into successful implementation and adoption
Why most CEOs would be shocked if they knew how much it actually costs to do it properly
Why your data operating model needs to be rolled out iteratively across the organisation
The symptoms of a successful data operating model and measuring success
Why data products have changed the way data operating models work
Why lack of adoption is often due to a lack of process
The importance of selecting the right partners
The top four pieces of advice to create the right data operating model
The relationship between data operating model and data culture