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

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

Antti Syvajarvi (University of Lapland, Finland) and Jari Stenvall (Tampere University, Finland)
Release Date: June, 2010|Copyright: © 2010 |Pages: 448
ISBN13: 9781605669069|ISBN10: 1605669067|EISBN13: 9781605669076|DOI: 10.4018/978-1-60566-906-9

Description

The need for both organizations and government agencies to generate, collect, and utilize data in public and private sector activities is rapidly increasing, placing importance on the growth of data mining applications and tools.

Data Mining in Public and Private Sectors: Organizational and Government Applications explores the manifestation of data mining and how it can be enhanced at various levels of management. This innovative publication provides relevant theoretical frameworks and the latest empirical research findings useful to governmental agencies, practicing managers, and academicians.

Topics Covered

The many academic areas covered in this publication include, but are not limited to:

  • Business network research theories
  • Clinical Data Mining
  • Data mining and knowledge management in project oriented environments
  • Enterprise resources planning
  • Fuzzy decision trees
  • Knowledge discovery in networked environment
  • Management Information Systems
  • Organization research in the public sector
  • Privacy preserving data mining
  • Productivity analysis of public services

Reviews and Testimonials

This book aims to provide some relevant frameworks and research in the area of organizational and government data mining. It will increase understanding how of data mining is used and applied in public and private sectors. Mining of data, information, and knowledge from various locations has been recognized here by researchers of multidisciplinary academic fields. In this book it is shown that data mining, as well as its links to information and knowledge, have become very valuable resources for societies, organizations, actors, businesses and governments of all kind.

– Antti Syvajarvi

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.

– David Mason, Victoria University of Wellington, Online Information Review

Table of Contents and List of Contributors

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Preface

Attempts to get organizational or corporate data under control began more profoundly in the late 1960s and early 1970s. Slightly later on, due to management studies and the development of information societies and organizations, the importance of data in administration and management became even more evident. Since then the data/information/knowledge based structures, processes and actors have been under scientific study. Data mining has originally involved research that is mainly composed of statistics, computer science, information science, engineering, etc. As stated and particularly due to knowledge discovery, knowledge management, information management and electronic government research, the data mining has been related more closely to both public and private sector organizations and governments. Many organizations in the public and private sector generate, collect and refine massive quantities of data and information. Thus data mining and its applications have been implemented, for example, in order to enhance the value of existing information, to highlight evidence-based practices in management and finally to deal with increasing complexities and future demands.

Indeed data mining might be a powerful application with great potential to help both public and private organizations focus on the most important information needs. Humans and organizations have been collecting and systematizing data for eternity. It has been clear that people, organizations, businesses and governments are increasingly acting like consumers of data and information. This is again due to the advancement in organizational computer technology and e-government, due to the information and communication technology (ICT), due to increasingly demanding work design, due to the organizational changes and complexities, and finally due to new applications and innovations in both public and private organizations (e.g. Tidd et al. 2005, Syväjärvi et al. 2005, Bauer et al. 2006, de Korvin et al. 2007, Burke 2008, Chowdhury 2009). All these studies authorize that data has an increasing impact for organizations and governance in public and private sectors.

Hence, the data mining has become an increasingly important factor to manage, with information in increasingly complex environments. Mining of data, information, and knowledge from various databases has been recognized by many researchers from various academic fields (e.g. Watson 2005). Data mining can be seen as a multidisciplinary research field, drawing work from areas like database technology, statistics, pattern recognition, information retrieval, learning and networks, knowledge-based systems, knowledge organizations, management, high-performance computing, data visualization, etc. Also in organizational and government context, the data mining can be understood as the use of sophisticated data analysis applications to discover previously unknown, valid patterns and relationships in large data sets. These objectives are apparent in various fields of the public and private sectors. All these approaches are apparent in various fields of both public and private sectors as will be shown by current chapters.

Data mining linked to organizational and government conditions

The data mining seen as the extraction of unknown information and typically from large databases can be a powerful approach to help organizations to focus on the most essential information. Data mining may ease to predict future trends and behaviors allowing organizations to make information and evidence-based decisions. Organizations live with their history, present activities, but prospective analyses offered by data mining may also move beyond the analyses of past or present events. These are typically provided by tools of decision support systems (e.g. McNurlin & Sprague 2006) or possibilities offered either by information management or electronic government (e.g. Heeks 2006, de Korvin et al. 2007, Syväjärvi & Stenvall 2009). Also the data mining functionalities are in touch with organizational and government surroundings by traditional techniques or in terms of classification, clustering, regression and associations (e.g. Han & Kamber 2006). Thus again, the data needs to be classified, arranged and related according to certain situational demands.

It is fundamental to know how data mining can answer organizational information needs that otherwise might be too complex or unclear. The information that is needed, for example, should usually be more future-orientated and quite frequently somehow combined with possibilities offered by the information and communication technology. In many cases, the data mining may reveal such history, indicate present situation or even predict future trends and behaviors that allow either public policies or businesses to make proactive and information driven decisions. Data mining applications may possibly answer organizational and government questions that traditionally are too much resource consuming to resolve or otherwise difficult to learn and handle. These viewpoints are important in terms of sector and organization performance and productivity plus to facilitate learning and change management capabilities (Bouckaert & Halligan 2006, Burke 2008, Kesti & Syväjärvi 2010).

Data mining in both public and private sector is largely about collecting and utilizing the data, analyzing and forecasting on the basis of data, taking care of data qualities, and understanding implications of the data and information. Thus in organizational and government perspective, the data mining is related to mining itself, to applications, to data qualities (i.e. security, integrity, privacy, etc.), and to information management in order to be able to govern in public and private sectors. It is clear that organization and people collect and process massive quantities of data, but how they do that and how they proceed with information is not that simple. In addition to the qualities of data, the data mining is thus intensely related to management, organizational and government processes and structures, and thus to better information management, performance and overall policy (e.g. Rochet 2004, Bouckaert & Halligan 2006, Hamlin 2007, Heinrich 2007, Krone et al. 2009). For example, Hamlin (2007) concluded that in order to satisfy performance measurement requirements policy makers frequently have little choice but to consider and use a mix of different types of information. Krone et al. (2009) showed how organizational structures facilitate many challenges and possibilities for knowledge and information processes.

Data mining may confront organizational and governmental weaknesses or even threats. For example, in private sector competition, technological infrastructures, change dynamics and customer-centric approaches might be such that there is not always space for proper data mining. In the public sector, the data or information related to service delivery originates classically from various sources. Also public policy processes are complex in their nature and include, for example, multiplicity of actors, diversified interdependent actors, longer time spans and political power (e.g. Hill & Hupe 2003, Lamothe & Dufour 2007). Thus some of these organizational and government guidelines vigorously call for better quality data, more experimental evaluations and advanced applications. Finally because of the absence of high-quality data and easily available information, along with high-stakes pressures to demonstrate organizational improvements, the data for these purposes is still more likely to be misused or manipulated. However, it is evident that organizational and government activities confront requirements like predicting and forecasting, but also vital are topics like data security, privacy, retention, etc.

In relation to situational organization and government structures, processes and people, the data mining is especially connected to qualities, management, applications and approaches that are linked to data itself. In existing and future organizational and government surrounding, electronic-based views and information and communication technologies also have a significant place. In current approach of data mining in public and private sectors, we may thus summarize three main thematic dimensions that are data and knowledge, information management and situational elements. By data and knowledge we mean the epistemological character of data and demands that are linked to issues like security, privacy, nature, hierarchy and quality. The versatile information management refers here to administration of data, data warehouses, data-based processes, data actors and people, and applied information and communication technologies. Situational elements indicate operational and strategic environments (like networks, bureaucracies, and competitions, etc.), but also stabile or change-based situations and various timeframes (e.g. past-present-future). All these dimensions are revealed by present chapters.

The book structure and final remarks

This book includes research on data mining in public and private sectors. Furthermore, both organizational and government applications are under scientific research. Totally eighteen chapters have been divided to four consecutive sections. Section I will handle data mining in relation to management and government, while Section II is about data mining that concentrates on privacy, security and retention of data and knowledge. Section III relates data mining to such organizational and government situations that require strategic views, future preparations and forecasts. The last section, Section IV, handles various data mining applications and approaches that are related to organizational scenes.

Hence, we can presuppose how managerial decision making situations are followed by both rational and tentative procedures. As data mining is typically associated with data warehouses (i.e. various volumes of data and various sources of data), we are able to clarify some key dimension of data mined decisions (e.g. Beynon-Davies 2002). These include information needs, seeks and usages in data and information management. As data mining is seen as the extraction of information from large databases, we still notice the management linkage in terms of traditional decision making phases (i.e. intelligence, design, choice and review) and managerial roles like informational roles (Minztberg 1973, Simon 1977). In relation to the management, it is obvious that organizations need tools, systems and procedures that might be useful in decision making. Management of information resources means that data has meaning and further it is such information demands of expanded information resources to where the job of managing has also expanded (e.g. McNurlin & Sprague 2006).

In organizational and government surroundings, it is valuable to notice that data mining is popularly referred to knowledge and knowledge discovery. Knowledge discovery is about combining information to find hidden knowledge (e.g. Papa et al. 2008). However, again it seems to be important to understand how “automated” or convenient is the extraction of information that represents stored knowledge or information to be discovered from large various clusters or data warehouses. For example, Moon (2002) has argued that information technology has given possibilities to handle information among governmental agencies, to enhance internal managerial efficiency and the quality of public service delivery, but simultaneously there are many barriers and legal issues that cause delays. Consequently one core factor here is the security of data and information. The information security in organizational and government context means typically protecting of information and information systems from unauthorized access, use, disclosure, modification and destruction (e.g. Karyda, Mitrou & Quirchmayr 2006, Brotby 2009). Organizations and governments accumulate a great deal of information and thus the information security is needed to study in terms of management, legal informatics, privacy, etc. Finally the latter has profound arguments as information security policy documents can describe organizational and government intentions with information.

Data mining is stressed by current and future situations that are changing and developing rather constantly both in public and private sectors. Situational awareness of past, present and future circumstances denote understanding of such aspects that are relevant for organizational and government life. In this context data mining is connected to both learning and forecasting capabilities, but also to organizational structures, processes and people that indeed may fluctuate. However, preparing and forecasting according to various organizational and government situations as well as structural choices like bureaucratic, functional, divisional, network, boundary-less, and virtual are all in close touch to data mining approaches. Especially in the era of digital government organizations simply need to seek, to receive, to transmit and finally to learn with information in various ways. As related to topics like organizational structures, government viewpoints and to the field of e-Government, thus it is probably due to fast development, continuous changes and familiarity with technology why situational factors are progressively more stressed (e.g. Fountain 2001, Moon 2002, Syväjärvi et al. 2005, Bauer et al. 2006, Brown 2007). In case of data mining, it is important to recognize that these changes deliver a number of challenges to citizens, businesses and public governments. As a consequence, the change effort for any organization is quite unique to that organization (rf. Burke 2008). For instance, Heeks (2006) assumes that we need to see how changing and developing governments are management information systems. Barrett et al. (2006) studied organizational change and concluded what is needed is such studies that draw on and combine both organizational studies and information system studies.

As final remarks we conclude that organizational and government situations are becoming increasingly complex as well as data has become more important. Some core demands like service needs and conditions, ubiquitous society, organizational structures, renewing work processes, quality of data and information, and finally continuous and discontinuous changes challenge both public and private sectors. Data volumes are still growing, changing very fast and increasing almost exponentially, and are not likely to stop. This book aims to provide some relevant frameworks and research in the area of organizational and government data mining. It will increase understanding how of data mining is used and applied in public and private sectors. Mining of data, information, and knowledge from various locations has been recognized here by researchers of multidisciplinary academic fields. In this book it is shown that data mining, as well as its links to information and knowledge, have become very valuable resources for societies, organizations, actors, businesses and governments of all kind.

Indeed both organizations and government agencies need to generate, to collect and to utilize data in public and private sector activities. Both organizational and government complexities are growing and simultaneously the potential of data mining is becoming more and more evident. However, the implications of data mining in organizations and government agencies remain still somewhat blurred or unrevealed. Now this uncertainty is at least partly reduced. Finally this book will be for researchers and professionals who are working in the field of data, information and knowledge. It involves advanced knowledge of data mining and from various disciplines like public administration, management, information science, organization science, education, sociology, computer science, and from applied information technology. We hope that this book will stimulate further data mining based research that is focused on organizations and governments.

Author(s)/Editor(s) Biography

Antti Syväjärvi is a Professor of Administrative Science at the Lapland University in Finland and the Head of Research Methodology Department. Professor Syväjärvi’s academic work is concentrated especially to the fields like public information management, leadership, human resource management, organizational information technology, electronic government and change in public administration. He is currently leading some academic research projects that are related to the abovementioned thematic areas. Professor Syväjärvi has numerous national and international academic publications in refereed science forums. In addition to Finnish Universities, Professor Syväjärvi has been teaching and doing research at the Aston University and at the Cardiff University that are both located in United Kingdom.
Jari Stenvall is a Research Professor at the University of Tampere in Finland. Professor Stenvall has done, evaluated and conducted several assessments and research projects related to public administration reforms and applied public information technology. His research has included topics like change management, trust, organizational reforms, service innovations, and the use of information technology in organizations. Professor Stenvall’s scientific production contains numerous national and international publications. In addition to Finland, Professor Stenvall has been lecturing at the Kaunas University of Technology in Lithuania and the Queen’s University in the United Kingdom.