Series 1, Article 6 of 8 — Charity Data Analytics for ICB Commissioners

What is a charity data quality framework and why does it matter to ICB commissioners?

A charity data quality framework is a structured set of rules, checks, and processes that ensure the data underpinning your commissioner reporting is complete, valid, consistent, and traceable back to its source. Data quality is not a technical issue — it is a commissioning risk. Every performance KPI, every PROM outcome score, and every equity indicator covered in this series is only as credible as the data it is built on. ICB commissioners who receive analytically sophisticated reports from charities with poor data governance will identify the inconsistencies quickly — and the credibility damage can be significant.

This is Article 6 of our eight-part series on charity data analytics for ICB commissioners. Previous articles covered performance, productivity, outcomes, impact, and equity.

What are the core data quality KPIs a health charity should monitor?

Completeness measures the percentage of required fields that are non-null across your dataset. Commissioners reviewing equity data expect demographic completeness above 80%. Commissioners reviewing outcomes data expect PROM baseline completion above 90% of accepted cases. Monitoring completeness by field, by clinician, and by referral route reveals where gaps are concentrated.

Duplicate rate measures the percentage of records appearing more than once due to data entry errors. A duplicate rate above 2% is a serious concern — it inflates activity counts, distorts caseload figures, and creates problems with outcome calculations if a service user appears twice with different baseline scores.

Validity checks pass rate measures the percentage of records passing your defined validation rules — for example, that a referral date is before an acceptance date, that an EDE-Q score falls within the valid range, or that a discharge date is after an open date.

Refresh reliability measures whether your reporting data is actually up-to-date. A dashboard drawing on data last refreshed three weeks ago creates false confidence. Monitoring refresh success rate and last refresh timestamp ensures commissioners and managers always work from current data.

Metric definitions register and data lineage are the governance foundations beneath all other quality measures. Every KPI should have a documented calculation rule and should be traceable back to source records in your case management system.

How do poor data quality problems develop in health charities?

Poor data quality almost never develops through negligence — it develops through growth. A service that started with twenty cases managed in a shared spreadsheet reaches a hundred cases before anyone realises the original structure cannot support the reporting demands being placed on it.

The most common failure points are free-text fields used where structured categories are needed, inconsistent date formats across team members, and outcome measures administered at different points in the pathway by different clinicians.

A Power BI data quality dashboard that runs automated validation checks against your live data — flagging completeness gaps, duplicate records, and validation failures in real time — transforms data quality from a quarterly crisis into a continuously managed process. Every Quematics charity analytics engagement begins with a data quality audit.

Frequently asked questions

What is an acceptable data completeness rate for charity commissioner reporting?

For clinical fields essential to KPI calculation, completeness should be above 95%. For demographic fields such as ethnicity and postcode, above 80% is the minimum expected standard for credible equity reporting.

How do you build a metric definitions register for a health charity?

A metric definitions register is a shared document recording the name of each KPI, its calculation formula, the source fields it draws on, the reporting period, and any known limitations. The discipline of maintaining this register forces clarity about what your numbers actually mean.

What is data lineage and why do commissioners care about it?

Data lineage is the ability to trace any reported number back through the calculation chain to the raw source records. A charity that can show exactly which case records contributed to a reported outcome figure demonstrates a level of data governance maturity that builds long-term commissioner confidence.

How often should a health charity run data quality checks?

Automated validation checks should run continuously on every record at the point of entry. Manual audits should be conducted monthly, with a more comprehensive annual audit reviewing the full dataset for historical inconsistencies.