The weak maturity of data governance projects necessitate the implementation of good practices and feedback loops to constantly monitor and verify the validity of management rules on your data asset.
The following articles explain the characteristics of a data governance labeled as agile in order to:
1. Be as close as possible to your enterprise’s operational reality.
2. Adapt to your enterprise’s context and not the other way around.
3. Accurately reflect your data assets.
4. Unify and involve your collaborators.
5. Respond to changes quickly.
The implementation of data governance must not take the form of a five-year plan where deliverables hardly see the day. It must avoid the Big Bang effect and adopt an approach influenced by “agile” methods used in the software development sector.
The enterprise must adopt an iterative approach concerning the implementation of data governance. This approach rests on the concept of validity verification, experimentation, and iterative design.
We think that a data governance project must start by curating of data assets, in a cross-functional way. By adopting the Pareto principle, collect, document, and manage the 20% of data that will generate 80% of business value within your organization.
By gradually increasing its reach across your different data segments, by redefining the roles and responsibilities within your organization, and the rules of data management, you will begin to seek a satisfying governance.
This flexibility also encourages the emergence of a strong data culture within your organization.