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Data Mining and Business Intelligence Trends

In: Data-Driven Decision Making

Author

Listed:
  • S. Shyni Carmel Mary

    (Loyola Institute of Business Administration)

  • C. Joe Arun

    (Loyola Institute of Business Administration)

Abstract

In the evolving landscape of data-driven world, integration of data mining and the business intelligence is an emerging concern. Data explosion and the development of analytic tools led to the growth of the data mining field. With the use of data mining, we can uncover hidden information that is useful for decision-making in the field of business, engineering, research, and agriculture. This chapter provides an overview of the topic by covering the historic development and the tools and current applications. It also brings out the relationship between data mining and business intelligence. The applications covered include service providers, retail, crime agencies, e-commerce, supermarkets, banking sector, credit risk analysis, customer relationship management (CRM), strategic management, and product recommender system. The classification, clustering, regression, pattern recognition, and association rule mining are the tools covered. The significations of integrating data mining and business intelligence help the organization to empower decision-makers to steer their organizations towards success in an increasingly complex business landscape. The conceptual explanation of models used in different business application development is established with the recent trends for the future development.

Suggested Citation

  • S. Shyni Carmel Mary & C. Joe Arun, 2024. "Data Mining and Business Intelligence Trends," Springer Books, in: Jeanne Poulose & Vinod Sharma & Chandan Maheshkar (ed.), Data-Driven Decision Making, chapter 0, pages 107-138, Springer.
    • Handle: RePEc:spr:sprchp:978-981-97-2902-9_5
    DOI: 10.1007/978-981-97-2902-9_5
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