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Software Quality

Data-quality characteristics

The 15 characteristics of ISO/IEC 25012, the model this site uses for the quality of data, and how other data-quality models compare.

Data quality is the quality of the data a system holds and produces, judged apart from the program that holds it. This site describes it with ISO/IEC 25012, which models it in 15 characteristics (ISO/IEC 2008)1. The standard is the data-quality counterpart of ISO/IEC 25010, the software-quality model the five dimensions on this site come from, and its terms are the vocabulary data teams use: a value is inaccurate, a record incomplete, a table out of date.

The characteristics

The standard classifies each characteristic by point of view. Inherent quality is the potential the data itself has to satisfy implicit needs; system-dependent quality is the degree to which an information system achieves and preserves it, which depends on the technology the data is used in (Gualo et al. 2021)2. Some characteristics take both points of view.

Each definition is the standard's, shortened: in full, each is "the degree to which data has attributes that …" in a specific context of use. Published quotations of the standard supply the definitions of the inherent characteristics (Gualo et al. 2021)2 and of the others (Felderer et al. 2019)3.

Characteristic Point of view Data has attributes that …
Accuracy inherent correctly represent the true value of the concept or event they describe
Completeness inherent give values for all expected attributes and related entity instances of an entity
Consistency inherent are free from contradiction and coherent with other data
Credibility inherent users regard as true and believable
Currentness inherent are of the right age
Accessibility both let it be accessed, particularly by people who need supporting technology or special configuration because of a disability
Compliance both adhere to standards, conventions or regulations relating to data quality
Confidentiality both make it accessible and interpretable only by authorized users
Efficiency both let it be processed at the expected performance with appropriate resources
Precision both are exact or provide discrimination
Traceability both provide an audit trail of access to the data and of changes made to it
Understandability both let users read and interpret it, in appropriate languages, symbols and units
Availability system-dependent let authorized users and applications retrieve it
Portability system-dependent let it be installed, replaced or moved to another system with its quality preserved
Recoverability system-dependent let it keep a specified level of operations and quality even when a failure occurs

Other models

ISO/IEC 25012 is one of several models of data quality, which differ in the domain they were written for and in the dimensions they name (Miller et al. 2025)4:

Model Written for Dimensions
ISO 8000-8 enterprise master data accuracy, completeness, consistency, timeliness, uniqueness, validity
Total Data Quality Management (MIT) organizations in general Wang and Strong's 15 dimensions, in four categories: intrinsic, contextual, representational, accessibility
Data Quality Framework of the British government the public sector completeness, uniqueness, consistency, timeliness, validity, accuracy
European Statistical System quality assurance framework European official statistics relevance; accuracy and reliability; timeliness and punctuality; coherence and comparability; accessibility and clarity
IMF Data Quality Assessment Framework statistics countries report to the IMF prerequisites of quality, assurances of integrity, methodological soundness, accuracy and reliability, serviceability, accessibility
BCBS 239 risk data in banks accuracy, adaptability, clarity, completeness, integrity, timeliness
ALCOA+ data integrity in regulated medicine manufacturing attributable, legible, contemporaneous, original, accurate, complete, consistent, enduring, available
WHO Data Quality Assurance data from health facilities completeness, timeliness, internal consistency, external consistency, consistency with population data

Wang and Strong derived their dimensions from the people who use data rather than defining them: surveys of data consumers produced 179 attributes of data quality, which factor analysis and sorting studies grouped into the 15 dimensions (Wang and Strong 1996)5.

The models name one dimension in different words: what ISO/IEC 25012 calls currentness, ISO 8000 and the British framework call timeliness. Mapped onto one shared vocabulary, accuracy, completeness, consistency and timeliness appear in every model a 2025 review compared. ISO/IEC 25012 covers 15 of the 19 shared dimensions, Total Data Quality Management 11 and the WHO framework 5. Domain models add dimensions ISO/IEC 25012 lacks, such as governance and semantics, and only Total Data Quality Management counts the quantity of data (Miller et al. 2025)4.

Data quality and the methods

Each data-quality method records which of these characteristics it checks, in place of the five software dimensions other methods are classified on.

Referenced by

References

  1. ISO/IEC. 2008. ISO/IEC 25012:2008: Software engineering — SQuaRE — Data quality model. International Organization for Standardization. https://www.iso.org/standard/35736.html. ↩

  2. Gualo, Fernando, Moisés Rodriguez, Javier Verdugo, Ismael Caballero, and Mario Piattini. 2021. "Data Quality Certification Using ISO/IEC 25012: Industrial Experiences." Journal of Systems and Software 176: 110938. https://doi.org/10.1016/j.jss.2021.110938. ↩↩

  3. Felderer, Michael, Barbara Russo, and Florian Auer. 2019. "On Testing Data-Intensive Software Systems." In Security and Quality in Cyber-Physical Systems Engineering, edited by Stefan Biffl, Matthias Eckhart, Arndt Lüder, and Edgar Weippl. Springer. https://doi.org/10.1007/978-3-030-25312-7_6. ↩

  4. Miller, Russell, Sai Hin Matthew Chan, Harvey Whelan, and João Gregório. 2025. "A Comparison of Data Quality Frameworks: A Review." Big Data and Cognitive Computing 9 (4): 93. https://doi.org/10.3390/bdcc9040093. ↩↩

  5. Wang, Richard Y., and Diane M. Strong. 1996. "Beyond Accuracy: What Data Quality Means to Data Consumers." Journal of Management Information Systems 12 (4): 5–33. https://doi.org/10.1080/07421222.1996.11518099. ↩