With this milestone often comes some complacency, as if there is a magic button to press that "initiates" the data governance program on autopilot. Consider it from a personal perspective: you sacrificed time from your regular role to dedicate months (or more!) to the implementation. The kick-off meeting with stakeholders left you inspired, and you wrapped up the implementation with the best intentions. All of a sudden meeting conflicts arise, your newly appointed data governance responsibilities are competing with your other initiatives, and you notice commitment to the program by others has stalled as well. The loss of momentum is one of the biggest threats to a data governance program, rendering it a failed project before it ever produced business value. One of the easiest ways to mitigate this threat is to apply some agile principles and tools to your data governance program:
Backlog - creating and grooming a backlog of data governance initiatives will help keep the organization aligned on what are the priority items to address. Maintaining a backlog allows stakeholders to see where things left off if for some reason a grooming session has to be canceled, and it also allows the stakeholders to take a look back and see what all has been accomplished through the program.
Size and Commit - building upon the backlog grooming process is estimating level of effort for granular initiatives and committing to a subset (similar to sprint or iteration planning). This keeps the organization accountable and committed to solving data governance-related issues on a regular cadence. Depending on the size of the organization, data governance backlog items may trickle down to operational agile teams like data warehouse or web operations.
Roadmap - keep the big picture in mind with themes and epics on a roadmap. I typically like to split data governance into five themes: data governance policy/operations, data quality, metadata, data security, and education/training. Within each of these theme categories, the roadmap can be populated by chevrons representing the epics that represent the data governance backlog, varying in length (representing duration of effort) and color (representing complexity). Complementary to the backlog, the roadmap will allow data governance stakeholders to take a longer view on the position and direction of the program.
Retrospective - reflect on what has been working and what hasn't been working in the data governance program by holding a stakeholder retrospective, with a mixture of senior leadership and end users. I find these to be particularly useful there often will be two completely different opinions of how things are going from those two groups. In this example, the retrospective will uncover some learnings for senior leadership in that their prioritized data governance initiatives are not resonating with the rest of the organization. In other situations, the retrospective may identify strengths and weaknesses or other tendencies in the organization. In another example, it could be that the organization realizes that they've been facing a serious data quality issue and need to look at investing in a more robust, enterprise-wide solution.
Using any combination of these agile concepts to maintain your data governance program's momentum is a good start in committing to the long-term success of data governance at the organization. As the program momentum grows and gains more buy-in from the organization, data governance stakeholders will have more freedom to enhance, innovate, and scale the data governance program to serve the organization's progress and growth.