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Bring processes to create and maintain stat= istical standards within scope of GSBPM, as these processes consume and cre= ate GSIM objects and are core statistical processes of official statistics = offices.
The HLG strategic vision demonstrates, in paragraphs= 26 through 31, the pivotal relationship between GSIM and GSBPM, and how th= ey contribute to the creation of common generic industrial statistics. It g= oes on to explain, in paragraphs 32 and 33, what the official statistics co= mmunity needs to do with these models in the first instance:
To enable statistical organizations to ar= rive at standardised generic industrialised production of statistics, we fi= rst need to find one another at the conceptual level. We have to bring our = concepts within the blue square under the umbrella of the GSBPM and the GSI= M. This is a very high ambition which will take time. A first goal for the = models is to act as a common language. We are lost if we cannot communicate= properly.
It is obvious that the current version of= the GSBPM is only a starting point which needs to evolve further, the same= way common industrial standards evolve. This holds even stronger for GSIM = for which a first version has yet to be established. The HLG-BAS needs to a= ctively promote development of and convergence on these conceptual standard= s.
The brochure describing GSIM provides the clearest articulation of how G= SIM and GSBPM converge. On page 2 of the brochure, the diagram below shows = how =E2=80=9CGSIM models information that flows between these sub- processe= s=E2=80=9D.
This pivot point between GSBPM and GSIM is the point at which the models= must converge. There should be conceptual equivalence between the two mode= ls at the point where they intersect. In other words, the domain of GSIM in= formation objects should be equivalent to the domain of objects that GSBPM = sub-processes consumes and produces. This, to me, is what =E2=80=9Cconverge= nce=E2=80=9D means.
Standards are statistical objects that are used in statistical processes= . Creation of these standards is a core operation of statistical offices. S= tandards creation and maintenance processes often include similar steps to = each other, such as looking at existing standards, looking at current pract= ices, analyzing the statistical need, identifying or creating a proposed st= andard, and consulting with affected statistical stakeholders.
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