Intro

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