Transforming Data into Opportunities: Metric of the Month – Data Quality Rule Setting
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, highlights how a structured, rules-based approach to Data Quality Checks helps ensure the integrity of LEI reference data across the Global LEI System.
Author: Zornitsa Manolova
Date: 2025-08-07
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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. 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 Rule Setting.
In today’s interconnected financial ecosystem, the reliability of legal entity reference data is paramount. Recognizing this, the Global Legal Entity Identifier Foundation (GLEIF), in close collaboration with the Regulatory Oversight Committee (ROC) and LEI issuing organizations, has spearheaded efforts to enhance data quality standards across the Global LEI System.
In support of its mission to uphold exceptional data standards, GLEIF has introduced a structured Data Quality Management Framework, which defines a transparent and measurable set of Data Quality Criteria. These criteria define what constitutes high-quality data and serve as an objective benchmark for assessing the integrity of LEI reference data.
GLEIF’s Data Quality Checks are meticulously designed to ensure compliance of LEI reference data with the latest State Transition and Validation Rules, which describe the business rules and their technical implementation for issuance, update, management, and publication, under the Common Data File (CDF) format.
This blog examines how Data Quality Checks are defined by Data Quality Rule Setting and how this contributes to a more trustworthy and efficient global financial infrastructure.
What is Data Quality Rule Setting?
Data Quality Rule Setting is a structured and systematic approach that governs how each Data Quality Check is defined, interpreted, and applied across the Global LEI System. This mechanism ensures that each Data Quality Check is designed with a specific structure, incorporating four key components:
Maturity level - defines the evolution of improvements in processes associated with what is measured.
Quality criterion - how well data aligns with one of the established quality principles (e.g., Accuracy, Validity).
Check intention - defines the goal or rationale behind the rule.
Formalized logic - expressed as a combination of a precondition and a condition.
The checks follow a logical "if-then" format: If a certain precondition (X) is met, then a specific condition (Y) must also be satisfied.
For example, if a legal entity is marked as “retired” (precondition), then a corresponding legal entity event must be present in the record (condition). If a record does not meet the prerequisite, the check is considered not applicable. If a record meets the precondition but fails the condition, the result is a failed check. If both are satisfied, the result is a pass.
By precisely defining the logic for each Data Quality Check, Rule Setting ensures reproducibility where every datapoint is calculated the same way, every time, across the Global LEI System. This enables the transparent, consistent, and scalable evaluation of data quality across millions of LEI records, facilitating continuous improvement and alignment with global standards.
To support even greater transparency and operational flexibility, each Data Quality Check is also assigned tags that reflect its status in the implementation lifecycle. These tags help categorize the checks and streamline their monitoring, consultation, and reporting. They include:
Preview - The check has been fully developed, tested, and released by GLEIF. Results are visible to LEI-issuing organizations but are not yet utilized in any public reporting. This phase provides early access and supports preparatory work before formal consultation.
Consultation - The check is under active consultation with LEI issuing organizations. During this stage, feedback is gathered to fine-tune the check logic, impact, and applicability.
Report - The check has passed consultation and is officially adopted. Its results now contribute to public-facing outputs such as the Global LEI Data Quality Report and LEI issuer-specific Data Quality Reports.
Annual Rule Setting Consultation 2025
Ultimately, Rule Setting is the foundation on which the key performance indicators (KPIs) featured in this Metric of the Month series are built, from the Total Data Quality Score (TDQS) to Maturity Levels and individual Quality Criteria results. This means that continuous improvement, in collaboration with ecosystem participants, is imperative to promote trust and transparency.
On 3 July 2025, GLEIF and the LEI issuing organizations opened the 2025 consultation period for Rule Setting. Over the next six weeks, stakeholders will review 22 new and 24 updated checks before they are implemented in production later in 2025, providing LEI issuers with a clear runway to prepare their systems and processes. Together, these additions and updates are expected to enhance standardization and reliability throughout the Global LEI System.
Highlights include:
AI-powered LENU Check - LENU (Legal Entity Name Understanding), an open-source machine-learning model co-developed by GLEIF and Sociovestix Labs, now proposes Entity Legal Form (ELF) codes in 22 jurisdictions, slashing manual mapping efforts and boosting consistency worldwide.
Richer pattern validation - enhanced regex rules raise the bar for postal codes and local identifiers, capturing more edge-cases and reducing false positives.
Five new code lists - these curated lists bring extra structure, covering situations such as:
Entities that cannot have parents or children
Expected Registration Authorities for specific ELF codes
Appropriate legal forms for government bodies
Authorities that do not issue identifiers
After the consultation concludes, relevant checks are categorized under the ‘Report’ tag, making them visible across both the reports section and the dashboard. Within the Data Quality Reports, these checks are showcased under the ‘Top 5 Failing Checks’, providing a high-level snapshot of critical areas that require attention.
July spotlight: Check C000438
Each entry into the Data Quality Reports outlines the number of LEI issuers impacted by failed checks and identifies the organization with the highest failure ratio. For July, check C000438 surfaced as the top failing check. This check ensures that every direct parent relationship reported by an LEI issuer aligns consistently with the disclosed ultimate parent. It notifies LEI issuers of any discrepancies in the relationship structure, following the direct parent chain, against the ultimate parent of the corporate structure. In total, 22 average failures were recorded during July, representing a decrease from 64 average failures in June. This provides valuable insight into how issuer data quality evolves over time.
Transforming data into opportunities
GLEIF and the ROC are steering several specialized working groups, including the Data Quality Working Group. These teams anticipate emerging needs and translate them into practical improvements, from new Data Quality Checks to updated technical standards. This collaborative, multi-disciplinary approach keeps the LEI ecosystem fit for purpose in an increasingly digital and interconnected economy.
By uniting rigorous standards, transparency, and community feedback, GLEIF continues to transform high-quality data into a strategic advantage, unlocking efficiencies, mitigating risk, and opening doors to innovation for market participants 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.
