Data Quality Key Performance Indicators (Data Quality KPIs)

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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