Agile and Lean took the IT industry by storm starting in the early 2000s. My first introduction was in the mid-2000’s when my boss told me we were moving from waterfall to Agile and I was to help another co worker accomplish it. Through a lot of training, education, consultation with experts, trials, tribulations, and growing pains, we did it. Not only did we make the business happier with more frequent releases of quality software functionality, but we learned a lot!
After initially using Agile in the software development realm, we determined that with some adaptions, it was applicable to establishing our Data Governance Organization framework and implementing governance within our Enterprise Data Warehouse (EDW) projects.
Consider Lean Agile Data Governance in two realms 1) establishing the Data Governance Organization framework and 2) implementing actionable data governance within your projects.
The Data Governance Organization framework is the foundational piece of governance that establishes the Data Governance Council (DGC), Data Governance Steering Committee (DGSC) and establishes the vision, charter, policies and procedures, training and education, and roles and responsibilities needed for sustainable governance across the organization or company; including IT, the business and end consumers of data. Each of these artifacts or activities can be written as stories, prioritized, verified, completed and usable within an established iteration or period of time (most frameworks take several iterations to complete).
Actionable data governance is the governance artifacts and activities that must be planned and executed within on-going projects. For example, if you are bringing a new data source into your EDW, actionable governance ensures the data governance stakeholders are known, as well as their system(s) of record and uses of that data. The established Data Governance Organization Framework is in place to help make enterprise-wide decisions on any data issues found during project execution.
As your data governance effort matures and you move governance into other data management functions (as defined by the Data Management Association or DAMA), new policies, procedures and standards will be needed, along with updates to existing policies and procedures to include any new governance-related artifacts or activities. Again, each of these should be stories that are prioritized, verified, completed and usable within an established iteration or period of time.