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LEI Data | GLEIF Data Quality Management

Questions and Answers



The questions and answers below provide detailed information on the principles applied to monitor, assess and continuously improve the level of data quality within the Global Legal Entity Identifier (LEI) System based on clearly defined quality criteria developed by the Global Legal Entity Identifier Foundation (GLEIF) in close dialogue with the LEI Regulatory Oversight Committee and the LEI issuing organizations.

How is the LEI Total Data Quality Score calculated?

The total data quality score of the data quality criteria takes the average of the individual quality scores. This average is not weighted by data quality criteria, meaning that each data quality criteria contributes equally to the total data quality score. The LEI Total Data Quality score (\(TQ_s\)) is therefore:

$$TQ_s=\frac{\sum_{s=1}^{N}Q_s}{N}$$

Where:

  • \(TQ_s\) is the total data quality score.
  • \(s\) in the summation is an index representing individual quality criteria.
  • \(Q_s\) is the quality score for each respective quality criterion.
  • \(N\) is the number of quality criteria for which there are checks implemented.

For more details please see chapter 2 in the Global LEI Data Quality Report Dictionary.

What is the definition of each of the data quality criteria applied to measure the level of data quality in the Global LEI System?
Accuracy The extent to which the data are free of identifiable errors; the degree of conformity of a data element or a data set to an authoritative source that is deemed to be correct; and the degree to which the data correctly represents the truth about real-world objects.
Completeness The degree to which all required occurrences of data are populated.
Comprehensiveness All required data items are included - ensures that the entire scope of the data is collected with intentional limitations documented.
Integrity The degree of conformity to defined data relationship rules (e.g., primary/foreign key referential integrity).
Representation The characteristic of data quality that addresses the format, pattern, legibility, and usefulness of data for its intended use.
Uniqueness The extent to which all distinct values of a data element appear only once.
Validity The measure of how a data value conforms to its domain value set (i.e., a set of allowable values or range of values).
How are the top 5 best performing LEI issuers identified with the Global LEI Data Quality Reports calculated?

The 'Top 5 Best Performing LEI Issuers' section ranks the LEI issuers by their total quality score achieved with their latest source file as of the the reporting period. For an LEI issuer to be listed, they must manage at least 100 LEI records with a registration status of either ISSUED, PENDING_TRANSFER or LAPSED.

$$TQ_{Issuer}=\frac{\sum_{s=1}^{N}Q_{s, Issuer}}{N}$$

Where:

  • \(TQ_{Issuer}\) is the total data quality score for a given LEI issuer.
  • \(Q_{s, Issuer}\) are the individual quality criteria scores for a given LEI issuer.
  • \(N\) is the number of quality criteria for which there are checks implemented.
How are the top 5 countries identified with the Global LEI Data Quality Reports calculated?

The 'Top 5 Countries' section ranks the five countries with 30 or more LEI records whose legal entities provided the overall data with the highest quality.

$$TQ_{Country}=\frac{\sum_{s=1}^{N}Q_{s, Country}}{N}$$

Where:

  • \(TQ_{Country}\) is the total data quality score for a given country.
  • \(Q_{s, Country}\) are the individual quality criteria scores for a given country.
  • \(N\) is the number of quality criteria for which there are checks implemented.
What do the quality maturity levels express?

Maturity levels define the evolution of improvements in processes associated with what is measured. Therefore, the total maturity level score is aggregated differently from the total data quality score: While the scoring rules for the individual maturity levels apply in the same fashion, the scores for higher maturity levels will only contribute to the total score if the previous maturity level is fully reached (i.e. 100% score).

The following maturity levels apply:
Level 1 – ‘Required Quality’ (must be 100 percent for all data records).
Level 2 – ‘Expected Quality’ (should be 100 percent).
Level 3 – ‘Excellent Quality’ (the higher the better).

Does GLEIF make available specific documentation, which details the principles governing the data quality management program?

Yes. The technical documentation, which outlines the quality criteria applied, checks performed as well as the calculation models, is available here.