It is important for an organization to resolve data quality issues quickly so that information can be trusted and used. For this to happen, individuals that use data must understand the data quality issue resolution process including how issues are reported, who works on the issues and how they are resolved. This blog post will cover the steps of the data quality issue resolution process which is an important part of data governance (or also called data intelligence).
Here is the data quality issue resolution process:
- Enter Issue – It is critical that people know how to report issues. Usually this is done through a ticketing system. Remember that there might not be an issue, just a perception of an issue. Either way it is important to record the issue. Sometime data quality issues automatically come from data quality assessments. The issue points of entry must be known by data users in the organization.
- Acknowledge Receipt – If reported by an individual, say by entering a support ticket, then make sure that the process notifies them that the issue has been received and that they will get a response on resolution.
- Determine Ownership – The data steward needs to be identified who will work on this issue. This could be a general data quality steward, a functional data steward, or a data system steward. It would be helpful if the process had a workflow that automatically assigned and notified the appropriate person.
- Make Initial Determination – There are different action options based on the cause of the issue such as:
- Actual data quality issue
- Business glossary definition problem
- Report, dashboard, or integration issue
- Documentation issue
- Issue on training or lack of understanding of user
- False alert (bad rule or no issue such as perceived issue)
- Valid value change
- Further Assessment and Analysis – Sometimes future assessment and analysis will need to be done before the actual resolution occurs.
- Fixing Issue and Root Cause – Actions to resolve the issue depend on the data quality issue cause but could include:
- Doing data clean-up
- Fixing software, report, dashboard, or integration
- Correcting glossary entry
- Updating documentation
- Improving training
- Creating a new quality rule for issue so that it does not happen again
- Setting up monitoring
- Closure Communication – Make sure that the user who reported the issue is notified that the issue has been resolved and what the resolution was. And make sure that the issue and resolution are documented so that if something similar occurs in the future that the issue can be resolved quickly.
It is important that data users know the process regarding the handling of data quality issues. The organization’s culture should encourage users to report these issues. Remember that data quality issues turn into data quality rules that are included in data quality assessments which will lead to more data quality issues that need to be resolved. And as data quality improves so does the trust in the data. We hope that documenting the process above helps your organization's data quality efforts. Remember to check out the other data quality related resources in this blog post.
IData has a solution, the Data Cookbook, that can aid the employees and the organization in its data governance, data intelligence, data stewardship 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.
Photo Credit: QualityIssuestoQualityRules_BP #B1163
