|Czech Statistical Office||2. Modelling the Information and Processes of a Statistical Organization (Czech Statistical Office)|
1.1 Metadata strategy
A redesign of the Statistical Information System (SIS) was launched in the CZSO in 2004. The project has been centrally managed and monitored by the top management of the CZSO.
The first important step was design of a new SIS architecture. The architecture copes with increasing users´ requirements, both on a national and international level. It allows effective acquiring and completing of statistical data and metadata.
Major goals for Redesign SIS:
- reducing response burden and boosting respondent motivation;
- optimising production of statistical information in the CZSO;
- designing a conceptual model of SIS and Statistical Metainformation System (SMS);
- Defining a unified architecture of statistical tasks; **
- improving quality of statistical information;
- increasing users' comfort.
The SIS model encompasses a statistical business process (SBP) in all its phases, starting from assessment of users' requirements up to the dissemination of statistical information. The basis for the model has been a life cycle of statistical tasks. Recently the model was compared and updated in accordance with the Generic Statistical Business Process Model (GSBPM).
Core principles for Redesign SIS are as follows:
- systematic assessment and evaluation of statistical data requirements,
- increasing share of administrative data,
- increasing use of data modelling,
- implementation of SMS,
- implementation of statistical data warehouse,
- freeze of statistical surveys for 2-3 years,
- avoiding redundancy in statistical surveying.
The global architecture of SIS (GASIS) is composed of three components:
1.Content component of SIS
There is a significant shift in the content component from the statistical survey approach towards statistical object oriented approach.
The content component identifies data sources, links between surveys of different periodicity and different purposes; defines modelling methods, stratification of samples etc. The following three types of statistical variable are distinguished:
- fundamental variable (used for calibration and/or modelling),
- standard variable (predefined set of the statistically most important variables),
- complementary variable (supporting fundamental and standard variables).
2. Metainformation component (SMS)
Systematic use of metainformation inside and outside the SIS as a tool for internal and external integration. SMS is focused on the SBP. The model used for definition of a statistical variable ensures its standard description from the beginning to the end of SBP. Also the model and metadata description of statistical task's components has been designed.
3. ICT component
Software and hardware support for SBP. Standardisation of application software used in all stages of SBP. Tools for mathematical models and mathematical and statistical methods. Tools for data approval, release and dissemination. Statistical data warehouse and public database.
Statistical task - is a set of statistical activities needed to fulfil a user's request for statistical information. The statistical task can be composed of one or more statistical surveys.
Statistical survey - is a set of activities connected with the proposal of statistical questionnaire, preparing a sample, printing and distributing questionnaires, collecting completed questionnaires, data entry (including electronic collection of data) and data validation. Statistical survey is always a part of statistical task.
1.2 Current situation
The CZSO management in early 2005 approved the SMS strategy.
SMS is composed of mutually interlinked subsystems. In time being of updating this case study, the following subsystems have been tested under the pilot project of annual labour statistics:
- statistical classifications,
- statistical variables,
- statistical tasks,
- statistical quality.
Global architecture of SMS defines principles, obligatory for all SMS subsystems.
In years 2005 – 2010 design and implementation of SMS subsystems
(a.) Statistical classifications (CLASS)
(b.) Statistical variables (VAR) and
(c.) Statistical tasks (TASKS)
were carried out.
In time being the CLASS and VAR subsystems have been in regular operation, the TASK subsystem has been in semi-production operation.