The policies and procedures of data governance are valuable within the organization because they ensure that the quality of enterprise data is maintained at the levels to support successful business activities. The operational procedures are often spelled out within a data quality service level agreement (DQ SLA), which is an agreement between data providers and data consumers about the expected performance levels for data quality. The DQ SLA details the business data quality requirements along all the processing stages in a business process flow, and assertions that can be used to validate the data.

However, when errors in the data are identified, the data stewards responsible for the data must take action. This paper reviews the pieces of that immediate action plan: the triage and analysis tasks performed by data quality analysts or data stewards when an issue is identified and logged in the data quality incident tracking system. This includes:

• Evaluating and assessing the issue and determining the scope and extent of the problem from both a business impact perspective and from an operational perspective
• Reviewing the information process map to determine the likely locations for the source of introduction of the problem
• Determining strategies for correcting the problem
• Researching strategies for eliminating its root cause
• Planning and applying operational aspects, including data correction, monitoring, and prevention

Evaluating criticality, assessing the frequency and severity of discovered issues, and prioritizing tasks for remediation are all part of the data steward’s role. Formalizing the different tasks to perform when issues of different levels of criticality occur will reduce the effort for remediation while speeding the time to resolution.

The Data Quality Service Level Agreement

An emerging trend in the data quality arena is the concept of a DQ SLA, which provides a valuable link between the IT and business sides throughout a data quality or data governance effort. A DQ SLA is a contract between a data provider and a data consumer that specifies the data provider’s responsibilities with respect to different measurable aspects of what is being provided, such as availability, performance, response time for problems, as well as reasonable expectations for response and remediation when data errors and flaws are identified.