Transforming Data into Opportunities: Metric of the Month – Average Total Data Quality Score
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 the first blog of our new series, Zornitsa Manolova, Head of Data Quality Management and Data Science at GLEIF, explores key metrics in the Global LEI System – starting with the Average Total Data Quality Score (TDQS). This score quantifies 12 quality criteria, ensuring LEI reference data meets rigorous standards for reliability, interoperability, and usability across global digital ecosystems.
Author: Zornitsa Manolova
Date: 2025-02-07
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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 Average Total Data Quality Score.
What is the Average Total Data Quality Score?
GLEIF’s Average Total Data Quality Score (TDQS) aims to represent data quality as a single key performance indicator (KPI).
The TDQS reflects the outcomes of all data quality checks across the entire LEI repository, weighted by significance. It quantifies 12 different quality criteria – including the accuracy and completeness of LEI reference data – ensuring rigorous standards for reliability, interoperability, and usability across global digital ecosystems are met.
The Data Quality Dictionary provides full transparency into the methodology used to determine the TDQS.
Why does TDQS matter?
TDQS is more than a metric. It reflects the commitment to continuous improvement that underpins the Global LEI System. By promoting transparency and consistency, it supports data-driven decision-making and enhances confidence in organizational identification worldwide by:
Providing a single, clear performance benchmark
Aggregating and expressing data quality as a single score means the TDQS cuts through complexity. It provides a clear benchmark of the overall condition of LEI data assets – simplifying the assessment and communication of data quality.
Enabling continuous monitoring
Consistently monitoring the TDQS allows LEI issuers and data users to identify trends, maintain alignment and compliance with established standards, and detect emerging opportunities and issues.
For example, month-on-month comparisons offer stakeholders and data users insight into whether data quality initiatives are delivering results, enabling targeted improvements. Additionally, GLEIF can detect fluctuations in data quality and implement appropriate measures.
Changes in the TDQS are also important leading indicators that prompt deeper analysis, enabling stakeholders to pinpoint and address data quality opportunities and concerns. This includes further examination of other KPIs such as the Maturity Level (which we will explore in future blogs in this series), as well as Quality Scores for each criterion and individual LEI records.
Charting an evolving system
To ensure that it continues to offer transparent and objective insight, GLEIF continuously refines data quality checks.
As these enhancements take effect, temporary fluctuations in the TDQS signal the adaptation and long-term evolution of the Global LEI System. For example, the TDQS dipped to 99.96 in October 2024 following updates focused on improving data elements dedicated to translated / transliterated values and ensuring more accurate relationship information reporting. The TDQS has now rebounded, directly demonstrating the impact to global data users.
Transforming data into opportunities
As we have seen, the TDQS is indicative of GLEIF’s commitment to improving the availability of open, accurate, and relevant entity identification data for everyone.
Promoting harmonized data standards and ensuring open access to trusted data infers powerful benefits to businesses of all sizes – from the largest corporates to small and medium enterprises (SMEs). Hardwiring verifiable organizational identity into every business relationship through high quality data promises to address the fragmentation and inconsistencies across borders that have traditionally inhibited global trade and economic growth – unlocking transformative new opportunities.
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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.
