118. The original aim of the GSBPM was to provide a basis for statistical organisations to agree on standard terminology to aid their discussions on developing statistical metadata systems and processes. However, as the model has developed, it has become increasingly apparent that it can be used for many other purposes, in particular related to modernisation of official statistics. A number of papers describing actual and potential uses of the GSBPM are available on the UNECE wiki platform
The list below aims to highlight some current uses, and to inspire further ideas on how the GSBPM can be used in practice.
Harmonizing statistical production architectures - The GSBPM can be seen as a model for an operational view of statistical computing architecture. It identifies the key components of the statistical business process, promotes standard terminology and standard ways of working across statistical business processes. It is a key enabler of the Common Statistical Production Architecture
Facilitating the sharing of statistical software - Linked to the point above, the GSBPM defines the components of statistical processes in a way that not only encourages the sharing of software tools between statistical business processes, but also facilitates sharing between different statistical organisations that apply the model. It has been used to "classify" software available for sharing in the inventory compiled by the Sharing Advisory Board
Describing which standards are or could be used for different phases of the statistical production process. For example, Annex 2 of the SDMX 2.1 User Guide
explores how SDMX applies to statistical work in the context of a business process model.
- Providing a framework for process quality assessment and improvement - If a benchmarking approach to process quality assessment is to be successful, it is necessary to standardise processes as much as possible. The GSBPM provides a mechanism to facilitate this.
- Better integrating work on statistical metadata and quality - Linked to the previous point, the common framework provided by the GSBPM can help to integrate international work on statistical metadata with that on data quality by providing a common framework and common terminology to describe the statistical business process.
- Providing the underlying model for methodological standards frameworks - Methodological standards can be linked to the phase(s) or sub-process(es) they relate to and can then be classified and stored in a structure based on the GSBPM.
- Providing a structure for documentation of statistical processes - The GSBPM can provide a structure for organizing and storing documentation within an organisation, promoting standardisation and the identification of good practices.
- Providing a framework for building organisational capability - The GSBPM can be used to develop a framework assess the knowledge and capability that already exists within an organisation, and to identify the gaps that need to be filled to improve operational efficiency.
- Providing an input to high-level corporate work planning.
- Developing a business process model repository - Statistics New Zealand has developed a database to store process modelling outputs and allow them to be linked to their statistical business process model. They also plan to develop a Business Process Modelling Community of Practice - i.e. a regular forum to build knowledge of process modelling, to promote their business process model and increase understanding of it, and to discuss process modelling and models as enablers for process improvement.
- Measuring operational costs - The GSBPM can be used as a basis for measuring the costs of different parts of the statistical business process. This helps to target modernisation activities to improve the efficiency of the parts of the process that are most costly.
- Measuring system performance - Related to the point above on costs, the GSBPM can also be used to identify components that are not performing efficiently, that are duplicating each other unnecessarily, or that require replacing. Similarly it can identify gaps for which new components should be developed.