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Data mining methodologies like CRISP-DM are often used as-is, but adaptations for Big Data and business integration are rapidly increasing. Refinements are needed to address deployment challenges in real-world applications.

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Area of Science:

  • Data Science
  • Information Systems
  • Business Analytics

Background:

  • Data mining methodologies (CRISP-DM, KDD, SEMMA) are widely adopted.
  • Understanding practical application and adaptation of these methodologies remains limited.

Purpose of the Study:

  • To investigate how data mining methodologies are used in practice.
  • To identify the extent and nature of adaptations to standard data mining methodologies.

Main Methods:

  • Systematic literature review of 207 peer-reviewed and grey publications.
  • Analysis focused on the context and adaptations of data mining methodologies.

Main Results:

  • Data mining methodologies are predominantly used 'as-is'.
  • Adaptations are increasing, focusing on granular modifications and extensions.
  • Key adaptation drivers include Big Data technologies (technological) and business process integration (organizational).

Conclusions:

  • Standard methodologies often neglect crucial deployment issues for integrating data mining into IT and business processes.
  • Future refinements should integrate data, technological, and organizational aspects to bridge these gaps.