Abstract
Data mining involves searching through databases for potentially useful information such as knowledge rules, patterns, regularities, and other trends hidden in the data. In order to complete these tasks, the contemporary data mining packages offer techniques such as neural networks, inductive learning decision trees, cluster analysis, link analysis, genetic algorithms, visualization, and so forth (Hand, Mannila, & Smyth, 2001; Wang, 2006). In general, data mining is a data analytical technique that assists businesses in learning and understanding their customers so that decisions and strategies can be implemented most accurately and effectively to maximize profitability. Data mining is not general data analysis, but a comprehensive technique that requires analytical skills, information construction, and professional knowledge.
Key Terms in this Chapter
Business Intelligence: Business intelligence is a new concept in the public sector that uses advanced analytical tools such as data mining to provide insights into organization trends, patterns, and decision making.
Customer Relations Management (CRM): CRM is a conglomeration of technologies and management strategies used by organizations to control the operational side of the business, and this is the domain for human resources and financial systems.
Enterprise Resource Planning Systems (ERPS): This is a system built on software that integrates information from different applications into a common database.
Data Mining: This is a process of sifting through the mass of organizational data to identify patterns critical for decision support.
Interoperability: This is the ability of a computer system and/or data to work with other systems using common standards.
Data Quality: This refers to the accuracy and completeness of data.
Public administration: The term may apply to government, private-sector organizations and groups, or individuals.