The National GSBPM Glossary: Methodology and Practice

Abstract

This study aims to contribute to the national statistical glossary compilation, namely a glossary supporting the Generic Statistical Business Process Model (GSBPM). The GSBPM is a standard framework that allows statistical authorities to use corporate and harmonized terminology for the statistical business process. In Ukraine, the implementation of the GSBPM in statistical practice puts forward the need for its terminological support. The glossary is a means of understanding among statisticians and contributes to professional vocabulary formation. Statistical terminology today creates an independent terminological system. The system nature of statistical language is related to the professional limits and the interdependence of phenomena within these limits. As a subsystem of the general statistical system, the GSBPM has its vocabulary. We argue a complex, non-linear, and dynamic interrelation between statistical business processes in the GSBPM. The business processes form some synergy and involve professional vocabulary for different business phases, implying a non-mechanical approach to glossary compilation. According to studied international practice, there is no single approach to statistical glossaries construction and presentation. The choice of basiс international standards is crucial to ensure the GSBPM methodology and quality. We formulated several experience-based requirements and principles for the GSBPM glossary. Identified problematic issues may be useful for further work on statistical terminology. The progress in official statistics, both international and national, significantly affects this work's qualitative aspects. An essential factor in bringing national statistical language closer to international standards is undoubtedly the dialogue between the academic community and statisticians-practitioners. The development of glossaries for the General Activity Model for Statistical Organizations (GAMSO) and the Generic Statistical Information Model (GSIM) is outlined as a prospective area of further studies.