Statistics Sweden: use of GSIM

.

Statistics Sweden uses GSIM together with the Swedish version of GSBPM (see the GSPBM case study).

For business processes that are not part of the statistical business process Statistics Sweden has developed a process chart, in line with GAMSO.

Statistics Sweden´s process chart:

.

Principles for the central Metadata Repository:

• The metadata is stored when they are created and then re-used where they are relevant.
• The metadata shall be used actively, in the sense that they support metadata-driven production, which means that it is through metadata production is supported and controlled.
• The metadata that is established as common; such as classifications and standardised variables, should have the common metadata layer as the source.
• Common concepts should be used in a uniform manner and based on common definitions, which are documented in a thesaurus.

The central Metadata Repository must meet the following requirements:

• The central metadata repository to support the handling of business processes.
• The metadata shall support version and generation management of data.
• Principles shall be provided for the update, ownership and permissions / security.
• The metadata shall be presented in several languages if required, at least in Swedish and English.

The main way we use GSIM in Statistics Sweden is to adapt our existing information architecture to GSIM. This ensures that the projects which use our information model works in accordance with GSIM and the business doesn´t need to have specific knowledge of GSIM.
Statistics Sweden´s information architecture consists of two levels – object groups and detailed information models. The contents evolve within development projects, which mean that there are no detailed information models for all object groups.

Object groups:

.

Groups of objects

Statistics Sweden´s information architecture has several groups of objects that are used in other processes as well as those shared between several processes like Personnel and Organisation. The circled object groups are specifically used for the statistical production. The object group Structure can be used by other processes.

Highlighted object groups

Context variable

A Context variable is a Represented variable linked to a specific data source and a Population. This new object was created in order to make the link between concepts and structure.

.

GSIM as such is not possible to be used directly of the shelf, it is a conceptual model and it requires a lot of work to adopt it to the NSI-level. It is difficult to connect the different parts of GSIM. As soon as you start the level of details required are very large. This does not mean that it is not useful, quite the opposite, it is extremely helpful in a lot of cases since it provides a solution to existing problems and can therefore be used as a reference. GSIM has proven very valuable as a foundation in discussions regarding a central metadata repository and has provided a common vocabulary in those discussions. It has been a source of inspiration in the efforts of constructing a production system for short term statistics (KLON, mentioned above). During this work a number of lessons has been learned.

Experiences in short:

• The importance of reusing metadata
  For example, allowing the user to use the names and codes of variables and value domains from "Concepts" in "Business" and "Exchange" to configure the design and also what will be delivered to internal and external users.
• Harmonized metadata
  It is not a prerequisite, but significantly facilitates the work. By giving the same code and name to variables and values that by definition conceptually are the same thing.
• Periodicity
  Often there are data with different periodicity available. In order to have a general production system there needs to be a property that describes the periodicity on transformable input and output. The periodicity may be different between input and output. E.g. monthly data can be transformed into both quarterly and yearly data so that the output out of a service can be all three types of periodicity.
• Classify variables
  In order to aid users of the production system, but also in the building of common services that can be used by more than one statistical program, is it useful that variables are classified as different types. A variable can be of a different type depending on the context of its use.
• Traceability - and reproducibility
  Not only for versioning of data, but also for metadata. This is closely tied to versioning of services.
• Codes (status and ”other” codes)
  By using process step as an attribute it is possible to monitor where in the business process a values is used, given that the process flow is defined. This means that a lot of often occurring status codes are not required. What is left are status codes for a specific values, these are considered as generic status codes.
• Exchanging data
  To a large extent data is exchanged between different statistical programs within a NSI. This is often done by hard coded database-to-database solutions. Or even worse, e-mail. One way to avoid that is to store data in the same structure. Harmonized metadata is necessary for this.