The potential value intrinsic in Big Data represents an opportunity for companies and organisations, which invest their resources in search of a return on investment capable of guaranteeing efficiency in production and procurement processes, cost reduction and support to decision-making processes through targeted strategies. However, the implementation of Big Data-driven strategies often does not generate the expected value, recording a failure rate of over 80 per cent. Such percentages lead to think of a systemic error, probably inherent in the management models used. For these reasons, we analysed the major Big Data frameworks discussed in the literature and their respective characteristics, specialising them into three classes. By comparing these frameworks with those used in software engineering and IT projects, on which they are based, it was possible to understand the differences between the two generations of models and identify the critical aspects in Big Data initiatives. So, the analysis led to the definition of a first model for the implementation and management of Big Data driven strategies, highlighting what requirements a modelling framework should necessarily have to support companies and organisations in the transformation of the Big Data Potential Value in Big Data Business Value.
Unveiling the Roots of Big Data Project Failure: a Critical Analysis of the Distinguishing Features and Uncertainties in Evaluating Big Data Potential Value
Gervasi M.;Totaro N. G.;Specchia G.;Latino M. E.
2023-01-01
Abstract
The potential value intrinsic in Big Data represents an opportunity for companies and organisations, which invest their resources in search of a return on investment capable of guaranteeing efficiency in production and procurement processes, cost reduction and support to decision-making processes through targeted strategies. However, the implementation of Big Data-driven strategies often does not generate the expected value, recording a failure rate of over 80 per cent. Such percentages lead to think of a systemic error, probably inherent in the management models used. For these reasons, we analysed the major Big Data frameworks discussed in the literature and their respective characteristics, specialising them into three classes. By comparing these frameworks with those used in software engineering and IT projects, on which they are based, it was possible to understand the differences between the two generations of models and identify the critical aspects in Big Data initiatives. So, the analysis led to the definition of a first model for the implementation and management of Big Data driven strategies, highlighting what requirements a modelling framework should necessarily have to support companies and organisations in the transformation of the Big Data Potential Value in Big Data Business Value.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
