Deep learning in business analytics : a clash of expectations and reality
Schmitt, Marc (2023) Deep learning in business analytics : a clash of expectations and reality. International Journal of Information Management Data Insights, 3 (1). 100146. ISSN 2667-0968 (https://doi.org/10.1016/j.jjimei.2022.100146)
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Abstract
Our fast-paced digital economy shaped by global competition requires increased data-driven decision-making based on artificial intelligence (AI) and machine learning (ML). The benefits of deep learning (DL) are manifold, but it comes with limitations that have – so far – interfered with widespread industry adoption. This paper explains why DL – despite its popularity – has difficulties speeding up its adoption within business analytics. It is shown that the adoption of deep learning is not only affected by computational complexity, lacking big data architecture, lack of transparency (black-box), skill shortage, and leadership commitment, but also by the fact that DL does not outperform traditional ML models in the case of structured datasets with fixed-length feature vectors. Deep learning should be regarded as a powerful addition to the existing body of ML models instead of a “one size fits all” solution. The results strongly suggest that gradient boosting can be seen as the go-to model for predictions on structured datasets within business analytics. In addition to the empirical study based on three industry use cases, the paper offers a comprehensive discussion of those results, practical implications, and a roadmap for future research.
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Item type: Article ID code: 83893 Dates: DateEvent30 April 2023Published8 December 2022Published Online1 December 2022AcceptedSubjects: Science > Mathematics > Electronic computers. Computer science
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Technology > ManufacturesDepartment: Faculty of Science > Computer and Information Sciences Depositing user: Pure Administrator Date deposited: 27 Jan 2023 10:08 Last modified: 27 Sep 2024 16:09 URI: https://strathprints.strath.ac.uk/id/eprint/83893