In today's data-driven economy, the integrity and quality of data are paramount. Organizations worldwide rely on accurate information to make informed decisions, manage risks, and maintain regulatory compliance. One crucial element in this ecosystem is the LEI number, a unique identifier that enhances transparency in financial transactions. The Global Legal Entity Identifier Foundation (GLEIF) has established a comprehensive Data Quality Management program to ensure the reliability of Legal Entity Identifier (LEI) data. Central to this program is the concept of "Expected Quality," corresponding to Maturity Level 2 in GLEIF's Data Quality Maturity Model. Achieving this level signifies a robust standard of data quality, encompassing critical checks and validations.

Understanding GLEIF's Data Quality Maturity Model

GLEIF's Data Quality Maturity Model is structured into three distinct levels, each representing a progressive enhancement in data quality:​

  1. Level 1 – Required Quality: This foundational level ensures that all mandatory data elements are present and correctly formatted. It includes basic format checks and validations essential for data completeness and correctness.​

  2. Level 2 – Expected Quality: Building upon Level 1, this intermediate level introduces more sophisticated checks, such as plausibility assessments, business rule validations, and relationship integrity verifications. It ensures that data not only exists but also makes logical and contextual sense.​

  3. Level 3 – Excellent Quality: The pinnacle of data quality, this level incorporates advanced checks, including representation assessments and timeliness evaluations, ensuring data is not only accurate and logical but also up-to-date and optimally presented.​

Each level in this model is cumulative; achieving a higher level necessitates full compliance with the preceding levels. Therefore, attaining Level 2 (Expected Quality) requires that all Level 1 (Required Quality) criteria are met.

Components of Expected Quality (Maturity Level 2)

Maturity Level 2 encompasses several critical data quality checks designed to ensure the reliability and integrity of LEI data:

  • Plausibility Checks: These checks assess whether the data values are reasonable and within expected ranges. For example, verifying that an entity's registration date is not set in the future.​

  • Business Rule Checks: These validations ensure that the data complies with predefined business rules. For instance, confirming that an entity classified as a financial institution possesses the necessary regulatory identifiers.

  • Relationship Integrity Checks: These checks validate the correctness of relationships between data entities. For example, ensuring that parent and subsidiary relationships are accurately represented and that there are no circular ownership structures.​

  • Optional Elements Verification: While Level 1 focuses on mandatory data elements, Level 2 extends to optional elements, ensuring that when provided, they are accurate and valid.​

Collectively, these components ensure that LEI data is not only present but also accurate, logical, and contextually appropriate. ​

Significance of Achieving Expected Quality

Attaining Maturity Level 2 is crucial for several reasons:

  • Enhanced Data Reliability: Plausibility and business rule checks ensure that the data is trustworthy, reducing the risk of errors in decision-making processes.​

  • Improved Risk Management: Accurate and reliable data enables organizations to better assess and manage risks, particularly in financial transactions and compliance reporting.​

  • Regulatory Compliance: Many regulatory frameworks require stringent data quality standards. Achieving Expected Quality ensures compliance with these regulations, avoiding potential penalties.​

  • Operational Efficiency: High-quality data reduces the need for manual corrections and rework, leading to more efficient operations and cost savings.​

Measuring and Reporting Data Quality

GLEIF employs a comprehensive approach to measure and report data quality across the Global LEI System:​

  • Total Data Quality Score (TDQS): This metric represents the weighted average of the individual Maturity Level scores, providing an overall assessment of data quality.​

  • Maturity Level Performance: GLEIF monitors the number of LEI issuers achieving each maturity level, offering insights into the distribution of data quality across the system.​

  • Top Failing Checks: Identifying the most common data quality check failures helps target areas for improvement and provides transparency in reporting.​

For instance, the Global LEI Data Quality Report for November 2024 highlighted a sustained high data quality with an Average TDQS of 99.98%. However, it also noted an increase in the average days to close a challenge, reflecting a higher volume of resolved challenges.

Continuous Improvement Initiatives

GLEIF's commitment to data quality is evident through its continuous improvement initiatives:​

  • Regular Updates to Rule Settings: GLEIF periodically updates its Data Quality Rule Setting to incorporate new checks and validations, adapting to evolving data quality challenges.

  • Collaboration with LEI Issuers: By working closely with LEI issuing organizations, GLEIF ensures that best practices are shared, and common challenges are addressed collectively.​

  • Stakeholder Engagement: GLEIF actively engages with stakeholders to gather feedback and insights, ensuring that the data quality program aligns with user needs and expectations.​

Challenges and Future Directions

While significant progress has been made in enhancing LEI data quality, challenges remain:​

  • Data Volume and Complexity: As the volume of LEI data grows, maintaining high-quality standards becomes increasingly complex, necessitating advanced data management techniques.​

  • Global Coordination: Ensuring consistent data quality across diverse jurisdictions and regulatory environments requires ongoing collaboration and harmonization efforts.​

  • Technological Advancements: Leveraging emerging technologies, such as artificial intelligence and machine learning, can further enhance data quality checks and validations.​

Conclusion

Achieving Expected Quality, or Maturity Level 2, within GLEIF's Data Quality Maturity Model is a testament to an organization's commitment to data integrity and reliability. It ensures that LEI data is not only complete and correctly formatted but also logical, accurate, and contextually appropriate.