A data quality program can be any size. Whether large or small, the key thing is to have a plan and to use processes that are predictable and repeatable. A haphazard or unpredictable approach will not inspire trust in your organization’s data. Data users want to know the scope and schedule of the data quality work practiced at their organization. Everyone understands that because resources are limited the level of quality may not be as high as they would prefer, however they find it helpful to rely on the quality work that is performed with the limited resources.
This list of actions or tasks provides starting points for a data quality program at your organization. Select a few items and consistently practice them. Slowly add more items to your list. Soon your small program will grow into an impressively large one! A previous post covered data quality actions around getting started, sources of data errors, people responsibilities and communication. This covers the following actions around data quality rules, data assessments and issue resolution:
- Gather data quality rules from all business units and data systems. Publish as a single, centralized collection.
- Define thresholds of acceptable error rates for data quality rules. Start with the data elements that have the largest impact on strategic decisions.
- Visually document the process used to adopt a data quality rule and share the resulting diagram(s) across the organization.
- Define what is considered high data quality for each domain. Consider completeness, accuracy and timeliness.
- Define a set of relative value labels, such as “Good”, “Average” and “Poor”, that convey the overall quality of data within system, assigning a value to each.
- Perform data quality assessments and publish the results near the access points of data systems. This allows users to understand the relative quality of data they are about to use.
- Visually document the data quality assessment process.
- Annually review the tools used for data quality assessment and replace tools that are seldom used or hard to use.
- Annually review the data quality assessment process and adjust it, as necessary.
- Design and display icons to reflect relative assessment values such as a stoplight with red, yellow, and green dots.
- Implement a system to track the submission and resolution of data quality issues. Ensure that the resolution process contains an easy transition or path to create a data quality rule.
- Visually document the data quality issue submission and resolution process.
- Conduct an annual review of data quality issue submission and resolution process tools. Always try to make the process simpler and easier to use.
- Train necessary staff on data quality issue submission.
Perform these actions to improve data quality to enhance productivity, reputation, decision-making, and relationships.
Feel free to check out our other data quality resources in this blog post. If you need help in implementing data governance and data intelligence including data quality, remember that IData provides data governance services. A data governance solution like the Data Cookbook can help in successful implementation of data governance at an organization or a higher education institution and improving data quality. Feel free to 
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(image credit StockSnap_X1T5YTO2MB_dataqualityactions_BP #1093)
