1. The topic of data editing attracts considerable interest in the context of modernising official statistics, because it is traditionally one of the most expensive and time-consuming parts of the statistical production process and is prone to be influenced by innovative procedures like machine learning. The exchange of ideas, experiences and good practice for improving the efficiency of data editing together with well described processes is therefore a priority for the international statistical community.
2. The Generic Statistical Data Editing Model (GSDEM) was developed under the High-Level Group for the Modernisation of Official Statistics (HLG-MOS). It is intended as a reference for all official statisticians whose activities include data editing.
3. The GSDEM is designed to be consistent with other standards and models related to statistical modernisation, in particular, the Generic Statistical Business Process Model (GSBPM) and the Generic Statistical Information Model (GSIM). It should be seen as part of the coherent toolkit of models and standards promoted by the HLG-MOS under the “ModernStats” initiative.
4. The GSDEM is envisaged as a standard reference for statistical data editing, in the same way, from a methodological point of view, as the suite of standard models and methods for survey estimation. By providing standard terminology and models, the GSDEM aims to facilitate understanding, communication, practice, assessment and development in the field of statistical data editing.
5. The GSDEM describes the background and introduces fundamental definitions, followed by a description of the most common methods and functions using a generic terminology to explain the data editing functions (review, selection, treatment). The description of the metadata needed to define and describe the data editing functions and therefore fostering automation and a better control of the data editing process leads naturally to the development of the statistical data editing flow representing the sequencing of the different process steps. General proposals of most common flow models are provided with the aim to help users to develop, assess and understand statistical data editing processes.