Data governance has been the topic of many of the recent posts here on the Data Roundtable. And rightfully so, since data governance plays such an integral role in the success of many enterprise information initiatives – such as data quality, master data management and analytics.
These posts can help you prepare for discussing data governance at your organization. One of the things I've noticed about a lot of data governance discussions is that they often fail to begin with a good working definition of data governance. A few of my favorites follow.
Some data governance definitions
- “Data governance is the business-driven policy making and oversight of corporate data, providing the organizing framework for establishing strategy, objectives, policies and decision rights around the corporate data asset.” — Jill Dyché
- “Data governance is a set of processes ensuring important enterprise data assets are formally managed, guaranteeing data can be trusted by putting people in charge of fixing and preventing issues with data so that the organization has fewer negative events as a result of poor data.” — Steve Sarsfield
- “Data governance is a system of decision rights and accountabilities for data-related processes, executed according to agreed-upon models which describe who can take what actions with what data, and when, under what circumstances, using what methods.” — Gwen Thomas
- “Data governance is the organization and implementation of policies, procedures, roles, and responsibilities, which outline and enforce the rules of engagement, decision rights, and accountabilities for effective data management.” — John Ladley
For me the defining characteristic of data governance is its policies. Data governance policies align data usage with business metrics, establish data stewardship, prioritize data quality issues, and provide a framework for the communication and collaboration of business, data and technical stakeholders. They illustrate the intersection of business, data and technical knowledge spread throughout the organization, revealing how interconnected and interdependent the organization is. The policies also establish an enterprisewide understanding of the roles and responsibilities involved – and the accountability required – to support the organization’s daily business activities.
David Loshin uses the term operational data governance to focus on the operational tasks that data stewards and data quality practitioners perform to ensure compliance with defined data governance policies.
What say you?
Please share your perspective and experience regarding how data governance is defined and implemented within your organization by posting a comment below.