Get your data flywheels in motion!
Adoption = self-sustaining momentum
Regardless what your data strategy looks like (i.e. what flywheels to build, how and in what order), at its core it is all about building data flywheels and get those in motion.
But what is a data flywheel?
Simplified, it is a cycle of data-driven activities that feed and are driven by each other to build self-sustaining business momentum and value. I.e. an operational service generates data that is sent to an analytical system and then used to learn from and that results in a decision (human or computer) that improves a product or process and that leads to more usage (i.e. data) but also value (lower cost or more revenue) and the loop continues.
The interesting thing here is that the data flywheel isn't that different from a physical one! It takes tremendous of effort to start the wheel, but once it's moving it almost keeps going on its own. So, as you keep pushing bit by bit, the wheel speeds up until even a light touch is all it needs to keep going really fast, that is when you know you've established a good data product developer experience.
Data flywheels could be of many different flavors, anything from analyzing and improving a singel operational service to business intelligence or apply machine learning implemented in the operational system. The data flywheels could also be spread across different domains with different requirements and maturity but forming a matrix of data flywheels that you as a data team have to plan for, enable and support.
The bigger the data flywheel is the more effort it takes to put it in motion, but the value is often (but not necessarily) correlated as well. Bigger here is the complexity both in terms of technology but also process and stakeholders involved to close the loop.
Notice that the illustration is a bit rough/early braindump and quite generic. I would love to hear your thoughts on data flywheels and if it is a concept you use to align you initiatives as a data platform team (explicitly or implicitly).