From indicators to predictive analytics: A conceptual modelling framework

Nasiri Azadeh, Soroosh Nalchigar, Eric Yu, Ahmed Waqas, Robert Wrembel and Esteban Zimanyi

ULB Institutional Repository from ULB -- Universite Libre de Bruxelles

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.

Keywords: Conceptual modelling; Goal-oriented requirements engineering; Predictive analytics (search for similar items in EconPapers)
Date: 2017
Note: SCOPUS: cp.k
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Published in: Lecture Notes in Business Information Processing (2017) v.305,p.171-186

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Persistent link: https://EconPapers.repec.org/RePEc:ulb:ulbeco:2013/263182

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