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.
Government information systems have proved to be an efficient way to facilitate communication, provide information and deliver services to citizens. While database management systems lead to better data capture, storage, processing and sharing capabilities, extracting useful information from the data presents adversities for government (Robertson & Powell, 1999).
Electronic data capture and storage for later retrieval are major expenses associated with successful data mining applications (Robertson & Powell, 1999). Knowledge capture results from the use of data sources to find intelligent patterns in the data. The increase in the number existing government e-commerce sites that have the means to capture citizen data greatly reduces the cost of starting a data mining application (Tan, Steinbach, & Kumar, 2006).
Data mining software applies complex algorithms to massive data stores important for discovering relationships (Turban, Leidner, McLean, & Wetherbe, 2006). Within the public sector, data mining methods yield powerful information about the interrelationships between data elements (Robb & Coronel, 2006). Likewise, rich opportunities exist for uncovering “new” intelligence in government information.
While data mining technology advances, the potential impacts of mining citizens’ private data present legal, social and ethical questions (Taipale, 2007). In addition to privacy, inherent risks of accuracy and integrity can result when data is merged from multiple sources. Safeguards for addressing privacy and integrity risks include formal codes of ethics, written ethics policies mandated employee training, and even audits that flag unauthorized access to data (von der Embse, Desai, & Desai, 2004). More specifically, legislators have enacted laws and regulations to protect citizen data (Glover & Owen, 2004). These include the Gramm-Leach-Bliley Act for financial data, Children’s Online Privacy Protection Act for use/collection of child data online, the Electronic Communications Privacy Act, Privacy Act, Cable Communications Policy Act, and HIPAA regulations regarding medical information.
Key Terms in this Chapter
Data Redundancy: The same data is stored in more than one place.
Knowledge Pull: Knowledge pull taps into the intellectual strengths of its users, who share, seek, and create knowledge efficiently and effectively at the optimal cost.
Knowledge Management (KM) Strategy: A knowledge management strategy identifies the key needs, issues, and components within an organization and provides a framework for addressing these. Specifically, knowledge management strategy components include how an organization creates, captures, stores and uses knowledge.
Data Mining: The use of algorithms to automatically search through massive data stores from different sources in order to find unknown patterns and interrelationships that ascribe meaning to the data.
Knowledge Push: Knowledge push is typically associated with a traditional top-down approach to managing knowledge. In other words, the knowledge delivery is not initiated by the receiver and lacks collaborative potential of knowledge pull.
Balanced Scorecard: The Balanced Scorecard (BSC) was developed by Kaplan and Norton in 1992 to examine how an organization is using the activities and processes to meet its strategies, using the four key perspectives of finance, customers, internal processes, and training growth.
Knowledge Management: The comprehensive management of an organization’s expertise that consists of collecting, categorizing and disseminating knowledge.