What is Data Redundancy

Over the past decade, government has created innovative and complex systems connecting people to information by focusing on Knowledge Management (KM) practices. KM, described as the comprehensive management of an organization’s expertise through collecting, categorizing and disseminating knowledge, leads to knowledge discovery through techniques such as data mining. These developments have transformed traditional access to public services into e-government. Ever increasing demand to access and information has also brought about e-government policy development challenges for integrative KM practices in public services (Riege & Lindsay, 2006). In particular, the size and complexity of governmental structures and the vast data stores have become problematic (Koh, Ryan, & Prybutok, 2005). Because government uses, collects, processes, and disseminates sensitive information containing personal, financial and medical data, it is very easy for organizations to reprocess the information and disseminate it (Hewett & Whitaker, 2002). Ebrahim and Irani (2005) state that the benefits gained by data mining and KM practices are erased when information is not viewed as confidential but instead as a commodity to be bought and sold. Therefore, e-government must uphold a higher standard of ethics in KM practices through continued development of codes of conduct and governance policies for data that build citizen trust and ensure success of e-government services and transactions (Verschoor, 2000). An excellent framework to effectively preserve this trust is a balanced scorecard (BSC), which was first introduced by Kaplan and Norton (1992, 1996a, 1996b). The framework serves to continuously improve the KM process when modified for e-government. Therefore, this chapter describes technological and organizational challenges faced by e-government in KM and retrieval and presents the BSC framework to overcome these challenges.