Data quality key performance indicators (Data quality KPIs): A quantitative measure of data quality. A data quality measurement system measures the values for the quality of data at measurement points at a certain frequency of measurement. Data quality key performance indicators operationalize data quality dimensions. One example is the validation of a data element based on business rules.
Source: Otto, Boris; Österle, Hubert: Corporate Data Quality: Prerequsite for Successful Business Models, 2015
| Metric type | Metric | Description | Typical measurement/reporting method |
|---|---|---|---|
| Business value metrics | Impact on strategic goals | Impact of data management on strategic business goals | Assessed qualitatively and visualized by means of dependency graphs or traffic light charts |
| Economic value of data | Financial value of data | Assessed by means of the reproduction cost approach or the use-based approach | |
| Impact on business process related goals | Impact of data management on business process KPIs | Visualized by means of dependency graphs or traffic light charts | |
| Cost/time savings | Cost/time savings due to more efficient data maintenance processes, automated data cleansing/data import processes | Assessed by means of process mining | |
| Satisfaction of external groups | Satisfaction of customers, consumers, or business partners with respect to data excellence (e.g. quality of product catalogs, quality of shared data, adherence to data privacy standards and consents) | Surveyed by means of questionnaires/ interviews | |
| Data excellence metrics | Data quality | Quantitative assessment of data's "fitness for use" (e.g. consistency, completeness, or accuracy) | Measured in terms of conformance of data with respect to certain data quality dimensions |
| DQ Audit findings | Number of corporate data quality related violations during an audit (e.g. ISO 9001:2008) | Measured by reviewing audit results | |
| Data management performance metrics | Cycle/ turn-around time | Time passed from requesting a new master data object (i.e. a new supplier or consumer data record) until this record is available in operational systems (e.g. ERP) | Measured by process mining, workflow logs, or ticketing system logs |
| Internal satisfaction | Satisfaction of company-internal stakeholders such as data requestors and consumers in business processes | Surveyed by means of questionnaires/interviews | |
| Data management progress metrics | Maturity score | Maturity assessment of current capabilities from a strategic, organizational and technical point of view | Surveyed by means of questionnaires/interviews |
| Supported use cases | Percentage of agreed use cases fully supported by data management | Tracked by means of a use case funnel | |
| Rulebooks | Percentage of data domains covered by rulebooks (i.e. definitions, data models, processes, roles, responsibilities, methodologies). | Measured by means of a gap analysis between rulebook and data model | |
| Data records under governance | Percentage of data records covered by detailed rules | Measured by means of a gap analysis between rulebook and data model | |
| Geographical regions/ branches | Percentage of geographical regions/ branches implementing data governance | Measured by means of achieved milestones in rollout plans | |
| Role assignments | Percentage of geographical regions/branches implementing data governance | Measured by means of achieved milestones in rollout plans | |
| Trained people | Percentage of roles assumed by appropriately trained people | Measured by means of achieved milestones in rollout plans |
Pentek (2020). A capability reference model for strategic data management
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