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Journal Article

The Modernisation of Statistical Classifications in Knowledge and Information Management Systems  pp126-144

Andrew Hancock

© Jun 2017 Volume 15 Issue 2, Editor: John Dumay, pp59 - 146

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Abstract

As technology transforms knowledge and information management systems, statistical data is becoming more accessible, available in bigger and more complex datasets and is able to be analysed and interpreted in so many different ways. Traditional approaches to the development, maintenance and revision of statistical classifications no longer support or enable description of data in ways that are as useful to users as they could be. The ability to search and discover information in ways that were previously not possible means that new methodologies for managing and describing the data, and its associated metadata, are required. The development of structured lists of categories, often hierarchic in nature, based on a single concept, limited by the constraints of the printed page, statistical survey processing system needs, sequential code structures or narrow user defined scopes results in statistical classifications neither dynamically reflecting the real world of official statistics nor maintaining relevance in a fast changing information society. Opportunities exist for modernising the developmental processes for statistical classifications by using, for example, semantic web technology, Simple Knowledge Organisation Systems (SKOS), and Resource Description Frameworks (RDF), and for better describing metadata and information within and across multiple, interconnected information and knowledge management systems. These opportunities highlight the difficulties that come with using traditional approaches to statistical classification development and management, and encourage new thinking for different and more flexible options for developers and users. This paper explores the need to dispense with traditional practices for developing statistical classifications as cornerstones of metadata, knowledge and information management, and comments on the need to change the underlying methodology within statistical classification theory, best practice principles and how they can be used in associated information management systems.

 

Keywords: Classification Theory, Metadata, Statistics, Taxonomy

 

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Journal Issue

Volume 15 Issue 2 / Jul 2017  pp59‑146

Editor: John Dumay

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Editorial

 

Keywords: SECI model, digital transformation, folk song, knowledge conversion, Knowledge Sharing, Cross-Functional, NPD Teams, New Product Development, Knowledge Management, Information and Communication Technology, Construction Industry, Competitiveness, Learning, dynamic system, local context, MNE subsidiary, knowledge sharing barriers, educational management, educational institutions’ administrative processes, Classification Theory, Metadata, Statistics, Taxonomy

 

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