The incident tracking system will collect performance data relating to issue resolution, work assignments, volume of issues, frequency of occurrence, as well as the time to respond, diagnose, plan a solution, and resolve issues. These metrics can provide valuable insights into the effectiveness of the current workflow, as well as systems and resource utilization, and they are important management data points that can drive continuous operational improvement for Data Quality Control.
Incident tracking data also helps data consumers. Decisions based upon remediated data should be made with knowledge that it has been changed, why it has been changed, and how it has been changed. That is one reason why it is important to record the methods of modification and the rationale for them. Make this documentation available to data consumers and developers researching code changes. While changes may be obvious to the people who implement them, the history of changes will be lost to future data consumers unless it is documented. Data quality incident tracking requires staff be trained on how issues should be classified, logged, and tracked. To support effective tracking:
- Standardize Data Quality Issues and Activities: Since the terms used to describe data issues may vary across lines of business, it is valuable to define a standard vocabulary for the concepts used. Doing so will simplify classification and reporting. Standardization also makes it easier to measure the volume of issues and activities, identify patterns and inter-dependencies between systems and participants, and report on the overall impact of data quality activities. The classification of an issue may change as the investigation deepens and root causes are exposed.
- Provide an Assignment Process for Data Issues: The operational procedures direct the analysts to assign data quality incidents to individuals for diagnosis and to provide alternatives for resolution. Drive the assignment process within the incident tracking system by suggesting those individuals with specific areas of expertise.
- Manage Issue Escalation Procedures: Data quality issue handling requires a well-defined system of escalation based on the impact, duration, or urgency of an issue. Specify the sequence of escalation within the data quality Service Level Agreement. The incident tracking system will implement the escalation procedures, which helps expedite efficient handling and resolution of data issues.
- Manage Data Quality Resolution Workflow: The data quality SLA specifies objectives for monitoring, control, and resolution, all of which define a collection of operational workflows. The incident tracking system can support workflow management to track progress with issues diagnosis and resolution.
