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How to Define a Data Quality Indicator?

Jürgen Stausberg1, Sonja Harkener1

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|July 1, 2025
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Summary
This summary is machine-generated.

This study introduces a structured approach for publishing data quality indicator definitions, enabling unambiguous meaning and use. This framework facilitates independent comparison of indicator results across different entities.

Keywords:
Data qualityhealth researchquality indicatorsregistries

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Area of Science:

  • Data Science
  • Information Management

Background:

  • Data quality indicators require clear conceptualization, algorithmic expression, and unambiguous structural definitions for effective use.
  • Existing proposals for indicator structures necessitate updates based on comprehensive literature reviews.

Purpose of the Study:

  • To develop and publish a standardized structure for data quality indicator definitions.
  • To enhance the comparability of data quality indicator results calculated by independent actors.

Main Methods:

  • A comprehensive literature review was conducted to inform the update of an existing proposal.
  • The IDEFIM project facilitated the development of a new structure for publishing indicator definitions.

Main Results:

  • A robust structure for publishing data quality indicator definitions has been developed.
  • The structure ensures unambiguous meaning and facilitates consistent application of indicators.

Conclusions:

  • The developed structure supports the publication of data quality indicator definitions.
  • This standardization enables reliable comparison of indicator results from diverse sources, improving data quality assessment.