Posted on: May 11, 2016 | 2 min read

Simple Steps for Data Quality Tracking

Introducing the tracking of data quality issues into the middle of an ongoing development project might seem a bit "too little, too late", however it can easily be accomplished and is well worth the effort. By adapting the project's existing defect tracking process through these five steps, you can gain better visibility into your project and identify data quality issues as they arise, avoiding critical project pitfalls.

  1. Report Initial reporting of a defect by analysts, testers, or business staff is provided by entering it into the defect/issue system or log for a project. The process around this is often constrained to issues found with the tool specifically. But data quality issues can easily be reported using the same system and process using a specific flag to indicate it as data quality-related.
  2. Assess Many defects being raised by the business during a project can be traced back as a data quality issues and just need to be classified and tracked as such after initial assessment. While there are specific data quality activities that need to be included (e.g. documenting lineage, providing snapshots of data, and identifying the data steward), much of what needs to be provided and tracked for assessment is still standard issue for defect tracking.
  3. Prioritize Initial priority should be set by the person reporting the issue. The business can then update the priority if it is deemed more or less urgent than it was first categorized. If an issue is out of scope for a project, it can go to a backlog for future prioritization.
  4. Resolve Capture the resolution details into the issue or defect log (e.g. when it was resolved, who resolved it, description of the resolution). Associate data quality resolution scripts back to the issue or defect, and indicate follow up actions to monitor accordingly. In addition, provide the ability to transfer the issue or defect to another team, application owner, or business area for various resolution actions.
  5. Escalate While assessing an issue, the need to escalate to another organization or project may arise. Include attributes to flag a defect for escalation and rationale around the need to escalate so that you can include an escalation process in your Data Quality tracking.

For more insight into data quality control, or how your business could have an impact with data and analytics, contact a data and analytics expert at

Written by CCG, an organization in Tampa, Florida, that helps companies become more insights-driven, solve complex challenges and accelerate growth through industry-specific data and analytics solutions.

Topic(s): Strategy
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