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I am a huge proponent of Lean Agile. Not because it is a giant industry buzzword, but because I’ve lived it and witnessed first-hand the benefits Lean Agile can provide in delivering quality, useful functionality quickly to the business. There are several commonalities no matter which flavor of Lean Agile you choose or use.

As I have helped more and more businesses implement a Data Governance Organization Framework, Lean Agile is a great way to quickly establish a sustainable framework.

1. Engage the owner/customer/stakeholders. Include data owners as members of the Data Governance Steering Committee. These should be members of the business side of the organization who are responsible for the accuracy and correctness of data. Make sure stakeholders of governance (owners, stewards, consumers or custodians) receive training and education so they understand why governance is being implemented, as well as their roles and responsibilities. This will help set expectations and provide a consistent message.
2. Capture the work. Using the Data Governance Roadmap, determine what is needed to setup the Data Governance Framework and capture it as stories. You can write formal stories in the “As a <role>, I want <functionality>, so that <benefit>” format, or you can capture it as a high level need. For example:

As a governance stakeholder,
I want a common vocabulary defined for Data Governance,
so that all stakeholders understand the terms and definitions and a consistent vocabulary can be used by everyone

Or

Establish common Data Governance vocabulary

3. Make it visible. Now that work has been captured, make it visible, either via a low-tech way such as sticky notes on a wall in a common team area, or via free on-line software. Make sure members of the DGSC have access to view the stories.
   
4. Have the data governance owner prioritize the work. Identify an owner for governance. This could be a chairperson of a Data Governance Council or Data Governance Steering Committee. This person understands, champions and supports governance and has been empowered by the executives to lead the data governance effort and is available weekly to the team implementing governance. This is not a project manager or scrum master.
5. Work on the highest priority work first. Focus on the pieces of governance that are the highest priority to the company. If audit or exam finding was found that was to be remediated with governance, ensure that work is prioritized first.
6. Implement the Minimally Viable Product (MVP). What is the smallest amount of work that can be done to establish Data Governance within the company? If an exam or audit finding related to Data Quality initiated the need for data governance, focus on the Data Governance Framework that will help implement Data Quality; expand from there as needed in future iterations. To help in discussions about what is needed, have the Data Governance Council or Data Governance Steering Committee identify which stories are “must have”, “should have” or “nice to have”. Focus on the “must have” stories first.
7. Just as you can’t boil the ocean, you can’t implement the Data Governance Framework in one fell swoop. Whether it’s every two weeks or monthly, make sure work is prioritized, committed to, completed and approved in some format on a periodic basis. Keep delivering prioritized pieces of the Data Governance Framework.
8. Continually retrospect and improve. After work is completed and approved, hold a retrospective with the team members involved and see where improvements can be made to the process. Continue these retrospectives throughout the governance implementation.

Have the team answer these three simple questions:

• What went well? These are the things you’ll want to keep doing.
• What didn’t go well? These are the things you’ll want to stop doing.
• What could go better? These are things to consider changing.

By frequently and continuously delivering pieces of your Data Governance Roadmap, you’ll gain support and buy in from governance stakeholders while quickly setting up a working framework that can address data-related issues and challenges that impact your organization.