Transforming Data into Opportunities: Metric of Month – Maturity Level 2 - Expected Quality
High-quality data is more than a benchmark – it is a strategic necessity. Investing in data quality can transform risks into opportunities and inefficiencies into advantages. In this new series, Zornitsa Manolova, Head of Data Quality Management and Data Science at GLEIF, explores key metrics in the Global LEI System. In this blog, Zornitsa examines the Maturity Model defined within GLEIF’s Data Quality framework, focusing on the crucial role played by Maturity Level 2 – or the Expected Quality of data – in strengthening data integrity and ensuring trust across the global financial ecosystem.
Author: Zornitsa Manolova
Date: 2025-03-07
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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.
This means that data quality is now more than a benchmark – it is a strategic necessity. 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 – highlighting how investing in data quality can transform risks into opportunities and inefficiencies into advantages.
This month’s blog examines the Maturity Level 2.
Understanding Maturity Levels in LEI Data Quality
Data maturity is a key measure that reflects how effectively available data resources are utilized to maximize their value and reliability. To maintain the highest standards of LEI data quality, GLEIF employs a Data Maturity Model.
In this three-level model, every data quality check is assigned to one single Maturity Level. This systematic methodology reinforces data reliability and trust by ensuring a progressive and stepwise approach to continuous improvement, making the Global LEI System a dependable source for financial and regulatory ecosystems worldwide:
Maturity Level 1 - Required Quality is the foundation for data quality, ensuring that basic validation checks are consistently applied. At this stage, data undergoes format checks to ensure it is structured correctly and mandatory element checks to confirm that all required fields are properly completed.
Maturity Level 2 - To strengthen data reliability, Expected Quality introduces advanced quality assurance measures, including:
Plausibility checks: verifying that data entries are logical and credible.
Business rule checks: ensuring compliance with established governance standards.
Relationship integrity checks: confirming the accuracy of relationships between entities.
Optional element checks: assessing the correctness of non-mandatory yet valuable data fields.
Maturity Level 3 - Excellent Quality represents the highest standard of LEI data quality, ensuring that data is not only accurate but also timely and well-maintained. At this stage, representation checks confirm that entity data is consistent across records, and timeliness checks ensure that information is regularly updated and remains current. Lifecycle and legacy record checks validate that historical and active LEI records are properly maintained.
At GLEIF, we compare the three-level Data Maturity Model to the cabin classes of a flight. Required Quality represents the fundamental expectation that minimum standards are met - akin to reaching your destination safely. Expected Quality reflects an enhanced experience, much like the added comfort and services of a business-class ticket. Excellent Quality delivers the highest level of reliability and sophistication, comparable to the luxury of first-class travel.
Why does Maturity Level 2 (Expected Quality) matter?
By introducing advanced quality assurance measures, Expected Quality plays a crucial role in strengthening data integrity and ensuring trust within the financial ecosystem.
Here's why it matters:
Prevents data errors
A structured quality framework eliminates erroneous, incomplete, or inconsistent entity data from global financial systems.
Ensures regulatory compliance
Achieving the Expected Quality Level indicates that LEI data meets essential quality benchmarks, making it suitable for regulatory reporting, financial risk assessment, and informed business decision-making.
Drives continuous improvement
The progressive structure of Maturity Levels drives ongoing advancements in data accuracy and validation methodologies. For instance, LEI issuers achieving the Expected Quality Rate in February 2025 have shown consistent performance since December 2024. This steady progress reflects continuous efforts toward higher data quality and reliability. In 2024, we stabilized at 50% of LEI issuers, consistently achieving the required quality, which is a significant leap to 2020 and 2021, when only 22% and 28% of issuers met this maturity level, respectively.
Lays the foundation for excellence
The Expected Level is a prerequisite for reaching the Excellent Level, the highest standard of data quality. Without first achieving the Expected Level, full data integrity and trustworthiness in the LEI system would be unattainable.
Transforming data into opportunities
As a key benchmark in LEI data excellence, Maturity Level 2 supports a structured, continuous improvement approach, empowering LEI Issuers to enhance data quality and elevate trust, accuracy, and transparency across the financial ecosystem. This benefits organizations through enhanced operational efficiencies and improved risk mitigation while ultimately helping to bolster economic growth by increasing market transparency and enabling seamless international trade.
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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.
