Data Quality and Use

Data is used extensively in a variety of use cases falling under the core mandates of financial regulators and supervisors, namely, financial stability, orderly markets, and market integrity. Therefore, data plays a critical role in the day-to-day processes of National Competent Authorities (NCAs), ESMA, and other EU institutions.

Overview

ESMA, together with the NCAs, is committed to ensuring that the quality of the data received under reporting frameworks is accurate, consistent, and reliable in pursuit of the ESMA Data Strategy’s objectives of improving the availability and quality of data for evidence-based policy development, supervision, and risk assessment.

To this end, ESMA and NCAs have established Data Quality Engagement Frameworks (DQEFs) across various reporting regimes to assess the quality of the reported data, identify potential issues, prioritise resolution, and require remedial actions by the reporting entities. The DQEFs are usually composed of:

a Non-Technical Document, outlining the process and tasks allocation between ESMA and the NCAs, and
a Technical Document, which includes the data quality tests carried out and the timelines of execution.

ESMA also publishes an annual Report on Quality and Use of Data that i) explains how ESMA and its major stakeholders on the regulatory arena make use of the regulatory data under ESMA’s remit, and ii) provides insights on the work and outcomes related to the monitoring, assessment, and improvement of data quality. The report, initially focused on EMIR and SFTR data, gradually expanded over the years to cover most of the regulatory data in ESMA remit.

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Data quality dimensions (DQDs) assessed by ESMA in DQEFs

Quality dimension	Description
Accuracy	Refers to the faithful representation of the data against the reality it shall reflect
Completeness	Refers to the actual provision of the data both at record level and for individual fields within the records (completeness of specific reference data fields)
Consistency	Refers to the inner consistency of the data as provided to the ESMA systems. A problem of inconsistency amongst the information provided by a unique entity reveals in general a problem of accuracy, but as the methods to detect it differ, they are considered in a separate dimension
Timeliness	Refers to the availability of up-to-date information according to the requirements to ensure that both NCAs and ESMA can rely on data that properly reflects the latest developments
Uniqueness	Refers to the requirement that each entity exists only once within a dataset, ensuring there are no unintended duplicate records representing the same real-world object or concept
Validity	Validity is maintained through validation rules at IT submission level for each report, ensuring that data failing to meet the required format and standards, as well as the internal consistency and accuracy in the reporting are rejected. ESMA staff monitors both acceptance and rejection rates