title: Data mining in pharma sector: benefits.
abstract: OBJECTIVE
  The amount of data getting generated in any sector at present is enormous. The information flow in the pharma industry is huge. Pharma firms are progressing into increased technology-enabled products and services. Data mining, which is knowledge discovery from large sets of data, helps pharma firms to discover patterns in improving the quality of drug discovery and delivery methods. The paper aims to present how data mining is useful in the pharma industry, how its techniques can yield good results in pharma sector, and to show how data mining can really enhance in making decisions using pharmaceutical data.
  
  
  METHODS
  This conceptual paper is written based on secondary study, research and observations from magazines, reports and notes. The author has listed the types of patterns that can be discovered using data mining in pharma data.
  
  
  RESULTS
  The paper shows how data mining is useful in the pharma industry and how its techniques can yield good results in pharma sector.
  
  
  CONCLUSIONS
  Although much work can be produced for discovering knowledge in pharma data using data mining, the paper is limited to conceptualizing the ideas and view points at this stage; future work may include applying data mining techniques to pharma data based on primary research using the available, famous significant data mining tools.
  
  
  CONCLUSIONS
  Research papers and conceptual papers related to data mining in Pharma industry are rare; this is the motivation for the paper.
authors.affiliation: Information Management and Technology Area, Institute of Management Technology, Ghaziabad, India. jranjan@imt.edu
authors.forename: Jayanthi
authors.initials: J
authors.lastname: Ranjan
mesh_terms: D003660:Decision Support Systems, Management / Q000458:organization & administration; D004345:Drug Industry / Q000458:organization & administration*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*