How do you document data ownership for governance?

No one person owns a data asset. You can be better served by governing at the data domain level. Once you determine what you're governing, such as the customer data domain, you can start looking at which functions interact with customer data. If you build your customer data governance organization around those functions, you have the ownership you need, without necessarily using the controversial "owner" term. If you look at the employee data domain, you probably have different people in that data governance organization. There can be overlap, but you want to focus your governance activities. You don't need people involved who have no input to some data domains, so you often have separate data governance organizations per data domain.

Create a list of all datasets in the organization to classify and organize them. Then, establish agreements and policies for governing them.

There are two kind of ownership: the ownership of the business leading the activity creating the data, and the ownership of individuals concerned by the data, what is typically called the data subject(s). Law defines rights of the second type of owner, and obligations of the first type. Thos in terms of finance, the second type is the beneficial owner, with decision power over data, while the first kind is rather a holder, than an owner.

Effective data management requires a thoughtful and well-coordinated process to identify data owners who are crucial for ensuring data accountability and governance within the organization.

With data assets being so pervasive across organizations and typically flowing through most departments; data ownership becomes more practical when we differentiate between Data Owners (source, collect, create) and Data Product Owners (use, aggregate, enrich). For e.g. Customer Marketing Data would be owned by the Marketing Department that collected it through website cookies and the quarterly fundraiser dinner. A Customer Engagement Dashboard is an example of a data product that would be owned by the Business Intelligence department who enriched source data by aggregating it with customer data from different channels.

Data ownership policies are critical in ensuring that the rules to the game of data management are known and that the game is played consistently across the organization. Policies are most effective when they take into account and blend with organizational context, rather than being a superimposition.

A strong data governance policy should define the roles for data governance professionals, data stewards, and other stakeholders, including identification of which roles have "ownership" responsibilities. Since the organization actually owns the data, the use of the phrase "data ownership" can be an issue, and can be replaced with "data management" responsibilities.

Creating data ownership policies requires collaboration from data owners, IT professionals, and other stakeholders. These policies are crucial in promoting responsible data management, establishing data accountability, and safeguarding the organization's data assets.

Empowerment is the difference between energized and focused Data Stewards on the one hand, and frustrated and disengaged Data Stewards on the other.

Assigning data stewards should be a thoughtful and strategic process that aligns with the organization's data governance objectives. It's essential to ensure that data stewards are empowered, accountable, and equipped with the resources and knowledge needed to carry out their responsibilities effectively.

Creating effective data ownership processes involves working together, communicating clearly, and recognizing the worth of data to the organization. This is a continuous endeavor that must be flexible enough to adjust to changing data landscapes and regulatory requirements. Additionally, it is important for organizations to document their data ownership policies and procedures to maintain transparency and accountability.

Consistently monitoring and reviewing data ownership practices is crucial in maintaining data quality, security, and compliance, and promoting a culture of data stewardship in the organization. This practice ensures that data owners are fulfilling their roles efficiently and that data governance initiatives are aligned with business objectives