In a previous post, I outlined how designing data governance programs can deliver business value. In this post, we’ll look in greater detail at the life cycle of a typical program.
Data governance programs thrive on established and clearly articulated goals, identified owners for specific program components and mapping of priorities to corporate objectives. Modern data governance programs include the retention and protection of data, data literacy for organizational enablement and compliance obligations specific to your industry and location.
- Define. This stage focuses on identifying key organizational gaps, prioritizing them for near term focus and identifying owners for researching possible solutions, deciding on approach and rolling out the new required capabilities.
- Design. This phase is focused on the design of process changes, measurements and technology necessary to address the priorities identified during the define phase.
- Build. As we move beyond design, we focus on implementation of the changes identified in the design stage. This stage can often be the most complicated for sequencing as we manage the lead time to develop new technologies, train staff on their implementation and usage.
- Transition. Oftentimes, complex organizational changes must be rolled out over an extended period of time. Utilizing older and newer methods in parallel while staff gain access and capabilities in new environments and operating models. This phase is that overlapping period where we often accept a period of instability or complexity knowing the reward on the other side of this phase is greatly improved capabilities and execution.
- Measure. This phase is focused on ensuring that the anticipated outcomes identified in the define phase are realized and the organization retrospectively evaluates the process and decisions to improve on future iterations and program execution.