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Area of Science:

  • Risk Analysis
  • Data Science
  • Decision Science

Background:

  • The application of big data in risk analysis is increasing but its novelty and benefits remain under discussion.
  • Traditional risk analysis methods may not fully leverage the potential of large, complex datasets.

Purpose of the Study:

  • To define big data within the scope of risk analysis.
  • To propose that big data attributes (scale, speed, structure) and methods should be discussed concurrently.
  • To differentiate big data risk analysis from traditional approaches and clarify its contribution to risk assessment.

Main Methods:

  • Conceptual clarification of big data in risk analysis.
  • Illustrative examples comparing big data and traditional risk analysis.
  • Distinction between the conceptual definition of risk and its measurement.

Main Results:

  • Big data risk analysis requires a unified approach considering data attributes and analytical methods.
  • Explicitly accounting for the strength of knowledge is crucial for effective big data risk analysis.
  • Big data offers enhanced risk assessment capabilities when properly integrated with risk concepts.

Conclusions:

  • Big data risk analysis necessitates a holistic perspective, integrating data characteristics with analytical techniques.
  • The strength of knowledge is a critical factor in the validity and reliability of big data risk assessments.
  • Further research should focus on developing methodologies that seamlessly combine big data approaches with established risk assessment principles.