Data Quality Work Group DAMA-NL
The Data Quality work group has carried out research into definitions of dimensions of data quality. It has collected definitions from various sources and compared them with each other. The working group also tested the definitions against criteria derived from a standard for concepts and definitions: ISO 704
Dimensions of Data Quality
New in data land is that all dimensions are linked to a ‘data concept’ such as data file, attribute, record and data value. This makes it easier to distinguish between, for example, the completeness of records and the completeness of data values. These data concepts are also provided with standardized definitions.
This research has led to a list of 60 dimensions of data quality and 20 data concepts with standardized definitions. From this list, 12 common dimensions have been selected.
The results of the research are presented in a practical guide in the publication:
- How to Select the Right Dimensions of Data Quality – Including 60 dimensions of data quality and their standardized definitions. A guide for data management professionals that can be used in data quality improvement processes.
A summary of the results of the research is available in:
- Dictionary of Dimensions of Data Quality (3DQ). It summarises the results of both following papers.
A complete description of the research and the results is available in:
- Dimensions of Data Quality (DDQ) – Research paper. This paper contains as a research result a list of 60 dimensions of data quality with standardised definitions.
- Data Concept System (DCS) – Research paper. This paper contains a coherent set of standardized data concepts that are applied in the definitions of dimensions of data quality (concept system).
Tables containing all the concepts and their definitions are online:
- AirTable. This table contains all dimensions of data quality and a number of views on this table.
Code for Information Quality
Please contact the members of the working group for feedback. It is our aim that our products are relevant and usable for data management & data governance professionals.