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 month's 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: 2025-07-07
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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, while 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 objectively assess Legal Entity Identifier (LEI) records.
A spotlight on Accuracy, Comprehensiveness, and Representation
Accuracy, Comprehensiveness, and Representation are critical components of a resilient LEI data framework that supports both regulatory needs and seamless global interoperability. Together, they ensure that LEI data is not only technically correct and complete but also presented in a structured, consistent manner.
Accuracy: Reflect real-world facts, reliably
Accurate data strengthens confidence in the LEI system, enhances regulatory compliance, and supports transparent transactions between organizations.
Accuracy assesses how closely LEI reference data corresponds to legal entity information from authoritative sources, estimating the extent to which data is free from identifiable errors. It can be one of the more intricate criteria to evaluate, as it often depends on external reference data, adding a layer of complexity to checks. However, while absolute precision may be challenging to guarantee, GLEIF is committed to achieving the highest possible standards of accuracy through ongoing improvements.
Accuracy is currently evaluated through 14 individual checks, which collectively achieved an Average Data Quality Score of 99.99 in June. Checks include verifying that the legal form of a fund is categorized correctly, that there are reasonable combinations for parent exceptions, or that postal codes match the country's prescribed format.
Comprehensiveness: Ensure all required fields are present
Comprehensive LEI records enable accurate risk analysis and support well-informed regulatory and commercial decision-making.
Comprehensiveness refers to the completeness of LEI reference records, ensuring that no mandatory fields are missing. In addition to verifying that records are technically valid per Common Data File (CDF) and XML schema definition (XSD) rules, these checks go a step further, ensuring that each record contains all essential and meaningful data, including entity names, addresses, registration details, and relationship records where applicable.
With 14 focused checks in place, comprehensiveness consistently upholds a high standard, achieving an Average Data Quality Score of 99.99 in June. Examples of these checks include validating that the legal name of an entity is plausible and not duplicated, ensuring that retired records are properly linked to the corresponding legal entity event, and confirming that additional information is provided when a registration authority or legal form code is not yet available in the official code lists.
Representation: Follow standardized formats
Consistent representation across LEI records improves system interoperability, enhances readability, and ensures seamless integration into regulatory systems, risk models, and automated workflows.
Representation evaluates whether LEI data elements are presented in a consistent and standardized way. Key aspects include adherence to patterns, appropriate character encoding, formats, language tagging, and compliance with code standards such as ISO 20275, which defines the Entity Legal Form (ELF) codes.
Assessed through 14 checks, the representation sustained near-perfect quality performance, reflected in its 99.99 score in June. Examples of these checks include assessing the consistent application of character sets, such as ensuring that legal names or addresses contain the appropriate language tag and that successor information contains valid entries.
Transforming data into opportunities
Accuracy, comprehensiveness, and representation play a critical role in ensuring that LEI data is not only correct and complete but also consistently structured for seamless global use. These dimensions work together with other Data Quality Criteria to create a robust foundation for data reliability that underpins the Global LEI System, enabling confident decision-making, streamlined compliance, and trusted digital interactions.
GLEIF's dedication to enhancing the Data Quality Management Framework and exploring additional quality criteria reflects our unwavering commitment to Total Data Quality. This places the data user at the center of all quality efforts and is a testament to our ongoing efforts to further increase the usability, transparency, and trustworthiness of LEI reference data across the global marketplace.
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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.
