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Benefits of Data Governance

Data management programs that have implemented data governance have benefited from features such as:

Common names and definitions: If existing data is not well named, they cannot be found and therefore cannot be shared.[1] In order to determine whether a data object already exists, common names, based on a standard naming convention, speed the analysis. Common names imply that there is a readily understandable business name and an abbreviated short physical name, based in part on a standard abbreviation list.

Consistent data: A consistent business definition of the data is important so that the knowledge worker can determine whether a data object with a name similar to his or her data requirement is in fact the same data object.

Consistent reports: If data attributes are well named or well defined, then the reports resulting from the analysis or use of the elements are apt to be more consistent because the underlying data is consistent.

Less duplication of data: Consistent names and definitions will facilitate the discovery of redundant data. Data modeling normalization is a process for eliminating duplication.

Trust by the business users: Well-executed data governance and data stewardship should improve quality and reliability, which, in turn, should increase accuracy and trust in the data analysis process.

Less data correction: Better managed data should be more accurate and require less correction.

However, the most important feature and benefit of data governance is that the data is being governed and that there are structured, mindful controls and measures in place to manage the data and ensure that its use is in alignment with the organization's overall goals and requirements. In short, the data is being viewed as an asset and is appropriately and meaningfully curated.

The Privacy and Data Governance/Stewardship Connection

Although it is not often articulated this way, data privacy is a key part of data governance for personal information. In this context, privacy engineering is engineering data governance for personal information into the design and implementation of routines, systems, and products that process personal information. An enterprise's privacy policy (including rules, standards, guidelines, etc.) “governs” the processing of personal information by an enterprise (and in Chapter 4, the privacy policy is not only viewed as a governance concept but also the meta-set of personal information data protection use-case requirements for privacy engineering).

Understanding how data management frameworks (such as data governance and data stewardship) fit with privacy frameworks (such as GAPP and the OECD Guidelines) is key to organizational development. Such frameworks and guidelines help to create the necessary roles and responsibilities to build and maintain a privacy-aware and ready enterprise. Such understanding will also help to recognize and understand privacy policies at meta-use-case requirements for privacy engineering.

Although the connection between data governance and privacy frameworks should be very close, the closeness is not often recognized nor leveraged by either domain. Too often data privacy teams sit outside enterprise-wide data governance and stewardship initiatives. This is unfortunate. File this under the opportunity not realized category.

Ultimately both groups should have a shared goal of ensuring data is curated and cared for as an asset whose value is recognized and cultivated within defined parameters.

  • [1] B. Van Halle and C. Fleming, Handbook of relational database design, Addison-Wesley, 1989, p. 16.
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