KPIs for an external data programme

If you cannot measure your external data programme, you cannot defend or improve it. The right KPIs prove value, expose problems early and guide investment. This guide sets out a practical KPI framework.

Why measure the programme

External data is an ongoing investment, and like any investment it needs measurement, to prove return, to manage suppliers and feeds, and to decide where to expand or cut. Good KPIs turn a cost line into a managed capability.

Value KPIs

Measure outcomes, not just inputs: decisions enabled, models improved, revenue or savings attributable, and use across teams. Value KPIs connect data spend to business results.

Quality KPIs

Track the core quality dimensions over time, completeness, accuracy, timeliness, validity, against agreed acceptance criteria, plus the rate of quality incidents and time to remediate.

Cost and efficiency KPIs

Monitor total cost of ownership (underlying data, commission, services), cost per dataset or per use case, and the internal effort saved versus managing sourcing in-house.

Timeliness and reliability KPIs

Measure time from requirement to first usable data, feed availability against SLA, and on-time delivery rates. These show whether the programme is responsive and dependable.

Compliance KPIs

Track the share of datasets with complete provenance and licensing documentation, privacy treatments applied, and any compliance issues. In regulated work, these are as important as value.

Keep the set small

A handful of well-chosen KPIs beats a sprawling dashboard. Pick the few that drive decisions and review them regularly.

Choosing a handful of KPIs

A sprawling dashboard signals confusion, not control. Pick a small set that drives decisions across five areas: value (decisions enabled, revenue or savings attributed, usage across teams), quality (the core dimensions tracked against acceptance criteria, plus incident rate and time-to-remediate), cost (total cost of ownership and cost per use case), timeliness (requirement-to-first-data, SLA availability, on-time delivery), and compliance (share of datasets with complete provenance and licensing). Six to eight well-chosen metrics beat thirty.

Measuring value, not just inputs

The trap is to measure what is easy, volume of data, number of feeds, rather than outcomes. Tie metrics to the decisions and results the programme exists to support, and track which datasets are actually used; retiring unused data is as valuable as acquiring more. Reviewed regularly, value and compliance KPIs turn an opaque cost line into a managed, defensible capability.

Sources & further reading

• DAMA-DMBOK: data governance metrics.
• OECD: measuring the value of data.
• ISO/IEC 25012: data quality dimensions.
• Internal practice: DataSupplier programme metrics.