The use of metadata modelling for the modernisation of information management of statistical classifications

Abstract

As statistical data is becoming more accessible, available in bigger and more complex datasets and can be analysed and interpreted in so many ways, opportunities exist for modernising the development processes for statistical classifications and its responsiveness to emerging user demands. Metadata modelling along with the use of semantic software tools enables significant advances to be explored in the way that traditional statistical classifications are developed, maintained, updated and implemented. The system of economic statistics is one where there is overlap in concepts, definitions, classifications and metadata which often makes search and discovery by non-expert users challenging. New methodologies for managing and describing data, and the categories to which they are classified can benefit from a greater uptake of semantic web technology, such as Simple Knowledge Organisation Systems (SKOS), and Resource Description Frameworks (RDF). This paper explores new approaches to statistical classifications and their role in the future of economic statistics through the use of metadata, conceptual and entity modelling rather than the traditional methodology of hierarchically structured, sequentially code based statistical classifications.