Data Quality in Public Procurement

Procurement data is only as useful as its quality allows. A system that publishes notices with missing contract values, inconsistent supplier names, or incorrect procedure codes produces data that misleads rather than informs. Data quality in public procurement is the discipline of measuring, improving, and communicating the reliability of structured contracting information, and it sits at the foundation of everything that OCDS and open contracting initiatives aim to achieve.

What is Data Quality in Public Procurement?

Data quality in public procurement is assessed across four primary dimensions:

Completeness. Are the fields that should be populated actually populated? A contract award release that omits the awarded value, the supplier name, or the number of bids received is technically a valid OCDS release but is analytically useless for those fields. Completeness is measured by the percentage of records that contain non-null, non-placeholder values in each key field.

Accuracy. Do the published values correctly reflect reality? Accuracy failures include contract values entered in the wrong currency, dates that precede the procurement procedure's legal start, supplier names that do not match official company registers, or CPV codes that misclassify the subject matter. Accuracy is harder to measure than completeness because it requires cross-referencing against authoritative external sources.

Consistency. Is the same information represented in the same way across releases and across publishers? Consistency failures include a supplier appearing under different name spellings in different releases, the same contracting authority using different identifiers across time periods, or currency fields that mix numeric values with formatted strings. Inconsistency makes aggregation and analysis unreliable.

Timeliness. Is data published promptly after the underlying procurement event occurs? A contract award notice published 6 months after the award decision is legally compliant under some national rules but practically useless for a market watcher trying to track the competitive landscape in real time. Timeliness is measured as the lag between the procurement event (deadline, award decision, contract signature) and the publication of the corresponding release.

The Open Contracting Partnership publishes data quality assessments for registered OCDS publishers, using a structured scoring framework that covers these dimensions. The European Commission's eForms standard (mandatory for EU above-threshold notices since October 2023) is designed to improve data quality by requiring structured, validated inputs rather than free-text fields for key procurement attributes.

Why data quality matters for bidders

Data quality is the hidden variable behind every piece of procurement intelligence. When a platform shows that a buyer in Germany awards contracts worth an average of EUR 2.3 million in a given category, that figure is only meaningful if the underlying award data is complete and accurate. If 30% of records for that buyer have missing award values, the true average may be substantially different.

For opportunity discovery, poor data quality creates false negatives: opportunities that exist but are not surfaced because the CPV code was entered incorrectly, the submission deadline was formatted inconsistently, or the estimated value was omitted. For competitive analysis, poor data quality creates false impressions of market concentration: if some award records omit the supplier name, the apparent market share of named suppliers will be overstated.

Recognising data quality limitations by country and publisher helps bidders interpret intelligence outputs correctly. A market where data quality is consistently high (such as Ukraine under Prozorro, or the UK's Find a Tender service for above-threshold contracts) supports confident analysis. A market where data quality is poor requires supplementary research through manual portal review.

Example

An analytics team builds a procurement market map for the Belgian healthcare sector using OCDS data sourced from the federal e-procurement portal. They find that completeness for estimated values at the tender stage is 91% but completeness for actual awarded values at the award stage is only 64%, because many authorities do not consistently publish contract award notices for below-threshold contracts. They adjust their market sizing estimates accordingly, flagging the 36% coverage gap in their methodology documentation, and supplement OCDS data with spot-checks of individual buyer portals.

Frequently Asked Questions

How can I assess the data quality of a specific OCDS publisher?

The OCP's publisher registry includes data quality scores for registered publishers. For publishers not in the registry, you can assess quality directly by downloading a sample of OCDS records and measuring completeness rates for key fields: award value, supplier name, bid count, and submission deadline. Tools such as the OCDS Kit (an open-source validation library) can automate schema validation and completeness checks.

Does eForms improve procurement data quality across the EU?

eForms (Implementing Regulation (EU) 2019/1780) became mandatory for EU above-threshold notices in October 2023. It replaces the older standard forms with a more structured, machine-readable format that enforces validation on key fields and introduces new data fields (including lot-level award data and bid counts). eForms is expected to significantly improve data quality for TED-published notices, though the improvement will be gradual as publishers adapt their systems.

Is poor data quality a legal issue for contracting authorities?

In most European jurisdictions, the legal obligation is to publish the required notice fields, not to meet a data quality standard in the OCDS sense. However, publishing inaccurate or misleading data in a formal notice (for example, a false estimated value or an incorrect deadline) can expose a contracting authority to procurement challenge under Directive 2014/24/EU or national implementing legislation. Red flag analysis tools sometimes identify data anomalies that are the result of deliberate manipulation rather than administrative error, which is a different and more serious matter.