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Deep Learning in Business Analytics: A Clash of Expectations and Reality

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  • Marc Andreas Schmitt

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 - by a mixture of content analysis and empirical study - that the adoption of deep learning is not only affected by computational complexity, lacking big data architecture, lack of transparency (black-box), and skill shortage, 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.

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  • Marc Andreas Schmitt, 2022. "Deep Learning in Business Analytics: A Clash of Expectations and Reality," Papers 2205.09337, arXiv.org.
    • Handle: RePEc:arx:papers:2205.09337
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    Cited by:

    1. Marc Schmitt, 2022. "Deep Learning vs. Gradient Boosting: Benchmarking state-of-the-art machine learning algorithms for credit scoring," Papers 2205.10535, arXiv.org.

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