A data quality Service Level Agreement (SLA) specifies an organization’s expectations for response and remediation for data quality issues in each system. Data quality inspections as scheduled in the SLA help to identify issues to fix, and over time, reduce the number of issues. While enabling the isolation and root cause analysis of data flaws, there is an expectation that the operational procedures will provide a scheme for remediation of root causes within an agreed timeframe. Having data quality inspection and monitoring in place increases the likelihood of detection and remediation of a data quality issue before a significant business impact can occur. Operational data quality control defined in a data quality SLA includes:
- Data elements covered by the agreement
- Business impacts associated with data flaws
- Data quality dimensions associated with each data element
- Expectations for quality for each data element for each of the identified dimensions in each application or system in the data value chain
- Methods for measuring against those expectations
- Acceptability threshold for each measurement
- Steward(s) to be notified in case the acceptability threshold is not met
- Timelines and deadlines for expected resolution or remediation of the issue
- Escalation strategy, and possible rewards and penalties
The data quality SLA also defines the roles and responsibilities associated with performance of operational data quality procedures. The operational data quality procedures provide reports in conformance with the defined business rules, as well as monitoring staff performance in reacting to data quality incidents. Data stewards and the operational data quality staff, while upholding the level of data quality service, should consider their data quality SLA constraints and connect data quality to individual performance plans.
When issues are not addressed within the specified resolution times, an escalation process must exist to communicate non-observance of the level of service up the management and governance chain.
Given the set of data quality rules, methods for measuring conformance, the acceptability thresholds defined by the business clients, and the service level agreements, the Data Quality team can monitor compliance of the data to the business expectations, as well as how well the Data Quality team performs on the procedures associated with data errors.
