Transforming Data into Opportunities: Metric of the Month – Data Quality Criteria (Continued)
High-quality data is more than a benchmark – it is a strategic necessity for global trust, compliance, and interoperability. In this blog, Zornitsa Manolova, Head of Data Quality Management and Data Science at GLEIF, continues to explore the role of Data Quality Criteria and its critical importance as part of GLEIF's Data Quality Management Framework in ensuring LEI data remains reliable, up-to-date, and fit for global use by financial institutions, regulators, and market participants.
Author: Zornitsa Manolova
Date: 2025-09-05
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In an increasingly interconnected global economy, the ability for organizations to trust and use data effectively is the foundation for innovation, growth, and competitiveness.
A high-quality data ecosystem is a driver of change and innovation that enables organizations to identify and seize new opportunities. At the same time, low data quality can lead to inefficiencies and exposure to regulatory and reputational risks.
GLEIF is committed to optimizing the quality, reliability, and usability of LEI data. Since 2017, it has published dedicated monthly reports to transparently demonstrate the overall level of data quality achieved in the Global LEI System.
To aid broader industry understanding and awareness of GLEIF’s data quality initiatives, this new blog series explores key metrics included within the reports.
This month’s blog continues to examine Data Quality Criteria.
In today’s data-driven economy, the use of high-quality legal entity data is crucial for driving innovation, ensuring compliance, and fostering trust in global markets. GLEIF ensures the highest possible standards through a structured Data Quality Management Framework, which defines twelve distinct, measurable Data Quality Criteria to assess Legal Entity Identifier (LEI) records objectively.
Previous blogs in the Metric of the Month series have explored the following criteria: 'Accuracy', 'Comprehensiveness', 'Consistency', 'Integrity', 'Representation', and 'Validity'. This month's edition highlights the remaining six essential criteria – ‘Accessibility’, ‘Completeness’, ‘Currency’, ‘Provenance’, ‘Timeliness’, and ‘Uniqueness’ – and examines how each is evaluated through structured, measurable checks to help maintain the integrity and global utility of LEI data.
A spotlight on Accessibility, Completeness, Currency, Provenance, Timeliness, and Uniqueness
By ensuring data remains accessible, complete, current, well-sourced, timely, and uniquely identifiable, GLEIF actively maintains a trusted framework that upholds and strengthens data quality standards as the global demand for reliable entity data grows.
Accessibility: Ensure data is reachable and responsibly available
Accessible LEI data empowers a wide range of stakeholders, from regulators to fintech firms, to integrate trusted information into their systems quickly and efficiently.
Accessibility measures the extent to which LEI data is easily and legally obtainable, while ensuring robust protections and controls are in place. In practice, this means that data should be openly available, such as through APIs or downloadable files, while maintaining proper governance over its use.
Accessibility is currently assessed through 12 dedicated checks, achieving an Average Data Quality Score of 99.99 in August. Checks include verifying that local identifiers are provided and plausible, as well as whether branch entities are registered.
Completeness: Capture all mandatory information
Complete data is essential for supporting critical use cases, such as due diligence, entity resolution, and network analysis.
Completeness assesses whether every required data element appears in an LEI record. Verifying that mandatory fields, such as legal name, address, and registration details, are included ensures that each LEI record is fully populated and operationally usable.
With 11 focused checks in place, Completeness consistently achieves an Average Data Quality Score of 99.99, including for August. Example checks include compulsory provision of legal entity category, ensuring a valid entity legal form code is used, and assessing whether ultimate parents have complete relationship information.
Currency: Keep data current and accurate over time
Current data ensures that financial institutions, regulators, and data users can rely on the LEI for timely decision-making in fast-changing markets.
Currency evaluates whether LEI data remains relevant and up-to-date. A ‘current’ data point reflects accurate information at a specific moment, while outdated entries could mislead users.
Currency is assessed through 2 checks to verify the next renewal date for lapsed entities is reasonable, and that both level 1 and 2 data undergo similar renewal cycles. Sustained high-quality performance in August was reflected in its 100.00 score.
Provenance: Track the origin and history of data
Understanding data provenance supports auditability, accountability, and enhances trust in cross-border information exchange.
Provenance focuses on the history or pedigree of each data element. It provides the context of where data originates and how it has changed over time, adding a layer of trust by transparently revealing the lineage of reference data.
Provenance is measured through 11 dedicated checks and has consistently delivered excellent results, achieving an Average Data Quality Score of 99.99 in August. These checks include verifying the correct formatting of entity identifiers, ensuring that plausible legal entity events are reported for retired entities, and confirming that all successors are appropriately listed for completed events.
Timeliness: Make data available when needed
Timely updates of LEI records help prevent reliance on outdated information and reduce operational and reputational risks.
Whereas Currency evaluates whether LEI data is relevant and up-to-date, Timeliness pertains to how promptly data is available for intended purposes. This ensures that when users need LEI information, whether for compliance, due diligence, or analytics, the data is accessible without undue delay.
Timeliness is evaluated through 2 targeted checks, which verify the mandatory provision of effective dates for completed legal entity events and confirm that reported entity creation dates are plausible. It maintains a strong quality performance, reflected in its 99.99 score in August.
Uniqueness: Guarantee distinct and non‑duplicative values
A unique LEI provides a reliable anchor for linking entity data across jurisdictions, datasets, and systems.
Uniqueness ensures that each data element, such as an LEI code, appears only once and is distinctly unique across the Global LEI System. This check ensures duplication and ambiguity are avoided, preserving the integrity and usability of the dataset.
With 8 specific checks in place, Uniqueness demonstrates consistently high performance, earning a Data Quality Score of 99.99 in August. Checks include helping prevent uniqueness violations outside of permitted transfers, detecting redundancies in legal address and alternative language addresses, and ensuring that only one branch record exists per entity per country.
Transforming data into opportunities
Accessibility, Completeness, Currency, Provenance, Timeliness, and Uniqueness continue to serve as foundational pillars in maintaining the integrity, usability, and trustworthiness of LEI data.
GLEIF’s ongoing refinement of these criteria through structured assessments and collaborative consultations reflects its strong commitment to advancing data quality in a meaningful and measurable way. As the role of the LEI expands across sectors, GLEIF remains dedicated to upholding a data quality framework that is not only resilient and forward-looking but centered on the needs of data users worldwide.
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Zornitsa Manolova leads the Data Quality Management and Data Science team at the Global Legal Entity Identifier Foundation (GLEIF). Since April 2018, she is responsible for enhancing and improving the established data quality and data governance framework by introducing innovative data analytics approaches. Previously, Zornitsa managed forensic data analytics projects on international financial investigations at PwC Forensics. She holds a German Diploma in Computer Sciences with a focus on Machine Learning from the Philipps University in Marburg.
