Data Quality Content

Data quality is an important component of data governance and data intelligence at higher education institutions and organizations.  Data quality includes people, processes and content. This blog post is about the various content (issues, assessments and rules) that is involved in a data quality program. This content should be as transparent as possible and be accessible to those that might need them. It is beneficial if the content is in a data governance solution like the Data Cookbook. 

The time to create data quality content including quality rules is when:

• Data quality issues are reported and resolved
• Reports / dashboards are designed and developed
• Data migrations are done such as a new system implementation
• Major upgrades occur for example, a Colleague SIS SQL migration
• System to system integration is designed, developed or installed
• New policies or business rules are created
• Software development occurs including data entry field changes and field validations
• Data quality assessments are done

The major data quality contents include:

1. Data quality issues and resolutions – This content includes the details of the data quality reported issues and the resolutions to these issues. This content is created when the issues and resolutions occur. View blog post on this type of content.
2. Data quality assessments – This content includes the detailed results of the data quality assessment, whether positive or negative. This content is created on a regular basis, at a minimum once a year. View blog post on this type of content.
3. Data quality rules – This content includes details on data quality rules and should be created when data quality issues are found that have no defined data quality rule. View blog post on this type of content.

• Related to a business glossary (definition) which includes quality attributes
• Related to a business rule or policy which are stand-alone rules and are not data system specific
• Related to a data model item which are rules that are technical in nature, are data system specific and involves database integrity
• Rules that can go beyond typical data quality including rules to: enforce policy, detect fraud, and monitor security

Along with people and processes, content is critical in improving data quality at organizations. The content of data quality rules, data quality assessments and data quality issues/resolutions should be stored in one place (such as Data Cookbook) and be accessible by those that need to view them. This content should be created when data quality issues occur or when other changes occur at the organization. Feel free to let us know how your data quality content is progressing.  Hope this blog helps in clarifying the content necessary for data quality at a higher education institution or any other organization.  Feel free to check out our other data quality resources in our data quality resources blog post.

IData has a solution, the Data Cookbook, that can aid the employees and the organization in its data governance and data quality initiatives. IData also has experts that can assist with data governance, reporting, integration and other technology services on an as needed basis. Feel free to contact us and let us know how we can assist.

(image credit StockSnap_14B430281B_DQContent_BP #1077)