GSIM as a communication tool
26. A major barrier to effective collaboration within and between statistical organizations has been the lack of common terminology.
27. A 'survey' in Statistics Canada, for example, is a 'survey instance' according to the UNECE, a 'collection cycle' according to Australian Bureau of Statistics, and a 'study' according to the external research community. These examples are just the tip of the iceberg.
28. This has made it difficult to communicate clearly within and between statistical organizations and without a common statistical language, there is no foundation for in-depth collaboration, standardization, or sharing of tools and methods.
29. GSIM, as the unifying 'common language' for official statistics, will enable rationalization, cooperation and collaboration within and between statistical organizations.
Box 1. Other industries use information models
The Official Statistics industry is not alone in recognizing the need for a 'common language' to underpin standards-based modernization.
Travellers, for example, can make their own arrangements to travel anywhere in the world by selecting and booking available flights. Their travel is enabled by a standardized aviation information model, which support streamlined flight planning and in-flight navigation. These models also support air-traffic control to reliably handle increased traffic, reduce fuel use and coordinate on-time arrivals and on-ground services. These standardized information models have promoted the adoption of an industry-standard 'common language'.
GSIM, as an information model, provides the equivalent common, unifying language for the official statistics industry.
30. GSIM provides staff with simple, easy to understand views of complex information. By describing statistical information in a consistent way, statistical organizations become able to communicate unequivocally and collaborate more closely.
31. Information can be a vague concept. Often when staff are asked to describe the information that is the input to and output from a statistical process, this can be a difficult task. It is difficult because most people don't know where to start.
32. GSIM can be used to educate staff. It gives them a framework for thinking about information. It provides a common terminology to describe the processes and their inputs and outputs that are used to generate official statistics. At the simplest level, staff could look at Figure 2 (below) and start to identify whether these information objects are relevant to them.
33. The layered documentation of GSIM means that staff can find the appropriate level of detail for them. For some, this level is an overview of the information objects. Others will want to see all the information objects, their attributes and relationships.
Figure 2. Simplified view of GSIM information objects
34. By providing these simple views on statistical information, GSIM can be used as a way to help staff understand what technical standards such as Data Documentation Initiative (DDI) and Statistical Data and Metadata eXchange (SDMX) describe. If staff can relate these standards to more familiar terms, they become easier to understand and use. For example, knowing that in DDI a 'Universe' is the same as the Population information object in GSIM helps.
35. Using GSIM as a common language will increase the ability to compare within and between statistical organizations. All processes that lead to the production of statistics can be described in this one integrated model. This includes the analysis of business needs, the establishment of statistical programs, the development and management of statistical methods, the design of production processes and their cyclical execution.
36. For example, GSIM can be used by staff (at all levels) to share and compare the concepts used in their work. It is also agnostic of subject matter, so it can be used to compare statistical production across subject matter departments.