Data Mining in Public and Private Sectors: Organizational and Government Perspectives

Citation

Mason, D. (2010), "Data Mining in Public and Private Sectors: Organizational and Government Perspectives", Online Information Review, Vol. 34 No. 6, pp. 983-984. https://doi.org/10.1108/14684521011099441

Data mining might seem an esoteric subject only of interest to computer scientists, but in fact most corporate decisions are based on some form of data mining. What is now referred to as data mining was called Management Information Systems and Decision Support Systems in the 1970s and 1980s. With the advances in computer technology the field of business intelligence took off and is now a part of all mainstream business applications.

It is especially important in public administration, where accurate information is needed to define and evaluate policy. Enormous amounts of data are collected by all branches of government, local and central, and used for everything – predictions of education needs, health care provision and social welfare forecasting. In commerce, similar information manipulation is used to analysis customer behaviour, supply chain performance and supermarket baskets.

The evolution of cloud computing, the exponential increase in data generated and the virtually limitless availability of computer power mean that topics that can be researched have expanded dramatically. From a research point of view this breadth of application has opened many interesting areas, and this book offers a good round‐up of current research.

The articles are presented in four sections. Section 1 has five chapters on the general application of data mining in management and government. They cover topics such as mining results to assess the performance of administration services, data collection in local government and ways to improve productivity. Section 2 focuses on metadata issues: privacy, security and archiving of data and knowledge. Four chapters address issues such as mobile computing data usage, incursions into private communications, and assuring the quality of data collected from public sources. Section 3 is about techniques and algorithms for forecasting. Four chapters cover areas as different as oil market demand prediction and retail behaviour. Section 4 consists of five chapters focussing mainly on theoretical issues arising from the application of data mining processes to specific areas. The chapters include reports from health care, project management and corporate strategy.

The book is well presented and fully referenced. The articles are well written and offer valuable perspectives on interesting research applications. This is an excellent and up‐to‐date contribution to the field.