# Data-quality characteristics 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)[^isoiec25012]. 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site come from, and its terms are the vocabulary data teams use: a value is inaccurate, a record incomplete, a table out of date.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)[^gualo2021]. 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)[^gualo2021] and of the others (Felderer et al. 2019)[^felderer2019]. | 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)[^miller2025]: | 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)[^wang1996]. 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)[^miller2025]. ## Data quality and the methods Each [data-quality method](https://quality.stereobooster.com/data-quality.md) records which of these characteristics it checks, in place of the five software dimensions other methods are classified on. ## Referenced by - [Quality dimensions](https://quality.stereobooster.com/quality-dimensions.md) · Quality dimensions - [Data quality](https://quality.stereobooster.com/data-quality.md) · Data quality - [Statistical integrity tests](https://quality.stereobooster.com/statistical-integrity-tests.md) · Data quality - [Choosing methods](https://quality.stereobooster.com/choosing.md) · Overview ## References [^isoiec25012]: ISO/IEC. 2008. *[ISO/IEC 25012:2008: Software engineering — SQuaRE — Data quality model](https://www.iso.org/standard/35736.html)*. International Organization for Standardization. . [^gualo2021]: Gualo, Fernando, Moisés Rodriguez, Javier Verdugo, Ismael Caballero, and Mario Piattini. 2021. "[Data Quality Certification Using ISO/IEC 25012: Industrial Experiences](https://arxiv.org/pdf/2102.11527)." *Journal of Systems and Software* 176: 110938. . [^felderer2019]: Felderer, Michael, Barbara Russo, and Florian Auer. 2019. "[On Testing Data-Intensive Software Systems](https://arxiv.org/pdf/1903.09413)." 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](https://doi.org/10.1007/978-3-030-25312-7\_6). [^miller2025]: Miller, Russell, Sai Hin Matthew Chan, Harvey Whelan, and João Gregório. 2025. "[A Comparison of Data Quality Frameworks: A Review](https://www.mdpi.com/2504-2289/9/4/93/pdf)." *Big Data and Cognitive Computing* 9 (4): 93. . [^wang1996]: Wang, Richard Y., and Diane M. Strong. 1996. "[Beyond Accuracy: What Data Quality Means to Data Consumers](https://w3.uch.edu.tw/ccwei/PAPER/ERP/data%20quality(JMIS).pdf)." *Journal of Management Information Systems* 12 (4): 5–33. . ## Acronyms - BCBS — Basel Committee on Banking Supervision - IMF — International Monetary Fund