24. Since the release of the GSBPM, several models have been developed under the auspices of the HLG-MOS to support the modernisation of official statistics. Collectively, these are called the “ModernStats” models. The following paragraphs outline the ModernStats models that have a strong link to the GSBPM.

GAMSO

25. The GAMSO 1 describes and defines activities that take place within a typical statistical organisation. It extends and complements the GSBPM by adding activities needed to support statistical production (i.e. activities in the areas of strategy and leadership, capability development and corporate support). In the GSBPM v5.0, some of these activities were included as overarching processes. Activities that are not directly related to the production of statistics and/or are managed at a corporate or strategic level are now included in the GAMSO (e.g. human resource management, quality management activities that are carried out at the corporate level such as development of a quality framework).

26. The GAMSO describes activities – that is, what statistical organisations do. It includes high level descriptions of these activities. On the other hand, the GSBPM focuses on the production process – it describes in more detail how statistical organisations undertake the activity of statistical production.

27. Like the GSBPM, the GAMSO aims to provide a common vocabulary and framework to support international collaboration activities. Greater value will be obtained from the GAMSO if it is applied in conjunction with the GSBPM.

GSIM

28. The GSIM 2 is a reference framework for statistical information, designed to help modernise official statistics at both national and international levels. It enables generic descriptions of the definition, management and use of data and metadata throughout the statistical production process. It provides a set of standardised, consistently described information objects, which are the inputs and outputs for GSBPM sub-processes. The GSIM helps to explain significant relationships among the entities involved in statistical production, and can be used to guide the development and use of consistent implementation standards or specifications.

29. Like the GSBPM, the GSIM is one of the cornerstones for modernising official statistics and moving away from subject matter silos. It identifies around 130 information objects, examples include data sets, variables, statistical classifications, units, populations as well as the rules and parameters needed for production processes to run (e.g. data editing rules).

30. The GSIM and the GSBPM are complementary models for the production and management of statistical information. As shown in Figure 2 below, the GSIM helps to describe the GSBPM sub-processes by defining the information objects that flow between them, that are created in them, and that are used by them to produce official statistics. Inputs and outputs can be defined in terms of information objects and are formalised in the GSIM.

Figure 2. The GSIM information objects as input and output of the GSBPM sub-process

31. Greater value will be obtained from the GSIM if it is applied in conjunction with the GSBPM. Likewise, greater value will be obtained from the GSBPM if it is applied in conjunction with the GSIM. Nevertheless, it is possible (although not ideal) to apply one without the other.

32. Applying the GSIM and the GSBPM together can facilitate the building of efficient metadata driven systems and help to harmonise statistical computing infrastructures.


  1. UNECE Statistics Wikis - GAMSO (https://statswiki.unece.org/display/GAMSO)     
  2. UNECE Statistics Wikis - GSIM (https://statswiki.unece.org/display/GSIM)     

  • No labels