Data Mining: A Tool for Knowledge Management in Human Resource
Lipsa Sadath
Ms. Lipsa Sadath, Department of Computing, Muscat College, Muscat Oman
Manuscript received on 15 April 2013 | Revised Manuscript received on 22 April 2013 | Manuscript Published on 30 April 2013 | PP: 154-159 | Volume-2 Issue-5, April 2013 | Retrieval Number: E0702042413/13©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Competitiveness is a company’s ability to maintain gain and reputation in its respective market or industry. Human Resource Management (HRM) plays a lead role in determining this competitiveness and effectiveness for better survival. The HRM generally refers to the policies, practices and systems influencing employee behavior, attitude and performance. Companies consider HRM as “people practices”. So it becomes the responsibility of the HRM to mine the best talents at the right time, train them, observe their performance, reward them and ultimately keep them happy in a company. It is simply because of the reason that every strategy of an organization is directly or indirectly related to the talents of the same. To gain and sustain a competitive advantage, knowledge management (developing, sharing and applying knowledge) within the organization becomes essential. But then how is HRM connected to Knowledge Management (KM) becomes a very relevant question. When employees are evaluated from their performance, different methods can be used for mining the best knowledge out of them. This paper is an attempt to study and understand the potential of Data Mining (DM) techniques for automated intelligent decisions from rich employee data base for predictions of employee performance implementing the finest KM strategies, thus achieving stable HR system and brilliant business.
Keywords: Data Mining, Knowledge Management, Human Resource Management, Talent Management, Classification, Prediction.
Scope of the Article: Data Mining