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Big data projects and business benefits: empirical evidence from extensive data survey

Author

Listed:
  • Chiara Francalanci
  • Paolo Giacomazzi
  • Barbara Pernici
  • Lucia Polidori
  • Gianmarco Ruggiero
  • Philip Carnelley
  • Gabriella Cattaneo
  • Mike Glennon
  • Richard Stevens
  • Arne-Jørgen Berre
  • Todor Ivanov
  • Ivan Martinez Rodriguez
  • Tomas Pariente Lobo

Abstract

There is an emerging stream of literature that is aimed at the identification of general drivers of business benefits for big data and analytics. This paper is positioned in this research, towards the definition of a general, high-level model incorporating the variables that are empirically confirmed as drivers of business benefits. We adopt the taxonomy of big data usage that makes a general distinction between descriptive, predictive and prescriptive analytics, as the sequence of steps to be taken towards a full exploitation of big data and consequent acquisition of business benefits. We put forward a set of hypotheses framing the idea that business benefits grow as companies take these three steps and test them by surveying the opinion of managers from a cross-section of over 700 European companies. Results partly confirm our hypotheses, suggesting that the timely availability of integrated data to decision makers is perceived as the main driver of business benefits, even if it is obtained with simple descriptive analytics.

Suggested Citation

  • Chiara Francalanci & Paolo Giacomazzi & Barbara Pernici & Lucia Polidori & Gianmarco Ruggiero & Philip Carnelley & Gabriella Cattaneo & Mike Glennon & Richard Stevens & Arne-Jørgen Berre & Todor Ivano, 2025. "Big data projects and business benefits: empirical evidence from extensive data survey," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 48(4), pages 500-521.
    • Handle: RePEc:ids:ijbisy:v:48:y:2025:i:4:p:500-521
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