Data governance is controlling the data and the rules that define that control.

At the very least, this entails defining the following:

• What is the purpose of the data?
• Who (person or system) will use it?
• What does this data enable?
• Where did the data come from (manual entry, selection from control values, or data transfer)?
• Where data transfer is anticipated, what are the data clean-up requirements?
• Are there control values for this data? If not, why not?
• What is an acceptable entry?
• What is not an acceptable entry?
• What format standardization is possible for this data?
• Who enters data into this field?
• Who needs to approve the data entry?
• When does this data get added and why is that timing important?
• Who is responsible and accountable for the quality of this data?
• Who is authorized to change this data?
• How is the change done?
• Who will approve the change?

In summary, data governance defines ownership, standards, controls, and approvals so data stays usable and trusted across the organization.

Data governance enables confidence in the data and consequently confidence in the reports and analytics. This impact is confidence in the decisions made and the opportunities enabled because of this confidence.

It also reduces rework and confusion because teams do not spend time debating which data is correct, and instead spend time using the data to take action.