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From indicators to predictive analytics: A conceptual modelling framework

Author

Listed:
  • Nasiri Azadeh
  • Soroosh Nalchigar
  • Eric Yu
  • Ahmed Waqas
  • Robert Wrembel
  • Esteban Zimanyi

Abstract

Predictive analytics provides organisations with insights about future outcomes. Despite the hype around it, not many organizations are using it. Organisations still rely on the descriptive insights provided by the traditional business intelligence (BI) solutions. The barriers to adopt predictive analytics solutions are that businesses struggle to understand how such analytics could enhance their existing BI capabilities, and also businesses lack a clear understanding of how to systematically design the predictive analytics. This paper presents a conceptual modelling framework to overcome these barriers. The framework consists of two modelling components and a set of analysis that systematically (1) justify the needs for predictive analytics within the organisational context, and (2) identify the predictive analytics design requirements. The framework is illustrated using a real case adopted from the literature.

Suggested Citation

  • Nasiri Azadeh & Soroosh Nalchigar & Eric Yu & Ahmed Waqas & Robert Wrembel & Esteban Zimanyi, 2017. "From indicators to predictive analytics: A conceptual modelling framework," ULB Institutional Repository 2013/263182, ULB -- Universite Libre de Bruxelles.
    • Handle: RePEc:ulb:ulbeco:2013/263182
    Note: SCOPUS: cp.k
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