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Definitions: Data Governance and Data Domains

Two terms are commonly used when talking about data at Rochester: Data governance, and data domains. Explore definitions for these terms on this page. If you’re looking for additional definitions or more support, our Data Resources page can help.

What is data governance?

Here at the University of Rochester, we will be crafting our institution’s version of a definition of data governance that fits our culture and our activities around data. In the meantime, we’ve developed a starting-point definition to help you think about data governance.

Data governance is the discipline which provides all data management practices with the necessary structure, strategy, and support needed to ensure that data are managed and used as a critical University asset.

There are a multitude of data governance definitions. They are all very similar, but none are universally used. Nearly all involve some statements about decision rights and accountabilities regarding data, as well as make some reference to data management activities. Often, data governance definitions will mention types of artifacts (policies, procedures and standards) or types of activities (planning, monitoring, and enforcement).

Important data governance distinctions

A few points in these drop-downs help specify and clarify what data governance does not cover here at Rochester.

Data governance is not strictly about compliance

It is important to have rules and to follow them. But the reason for the rules and the benefits they provide are more important than having boxes to check. The data governance program at the University of Rochester will be about providing guidance and frameworks to help people do the right thing with data instead of focusing on making sure no one ever does the wrong thing. Responsibility and accountability are very important parts of data governance but so are opportunity and efficiency. The balance of understanding who has the authority to do what with data with the insight of what can be achieved when more people have access and understanding of more data is a prime goal for the data governance program.

Data governance is not about technology

Technology is a key enabler to all data activities but successful data governance requires the separation of the data content from the platform or system where it resides. The concept of “employee” exists whether data about employees is in a payroll system or somewhere else. Consistent definitions for key university data elements need to be created and used where ever the data live so data governance conversations will focus on the content not the platforms.

Data governance does not apply equally across all data

All data need to be governed but not all data need the same type or level of governance. Key data assets for the university that underlie critical business processes will have stricter controls and more tightly focused definitions than local data that are created for occasional ad hoc analytics. Research data require a different kind of governance entirely but they do need governance.

What are data domains?

All data are not created equal: they have different purposes, uses, and origins. Therefore, governance may be different for different data.

One way to think about these different data is through the lens of different domains—basically, ways in which we group data together for the purposes of applying policies and standards.

Here at Rochester, we have two different types of data domains: Topical data domains, and functional data domains. See a full list of these domains below, or use our Domain Explorer tool to learn more.

Topical data domains

Data governance standards and protocols apply University-wide, including the following topical domains of University data:

  • Alumni and Advancement
  • Facilities and Space Services
  • Faculty
  • Finance
  • Human Resources
  • Master & Reference
  • Research Administration
  • Research Products and Outputs
  • Student

Functional data domains

Functional domains help support functions that cross-cut multiple spheres of University activities or knowledge. The functional domains are:

  • Analytics
  • Audit
  • Communications
  • Diversity
  • External Relations
  • Global Engagement
  • Information Security
  • Institutional Research and Academic Administration
  • Legal
  • Library and Collections
  • Technology
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