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Computer Science > Machine Learning

arXiv:2205.09337 (cs)
[Submitted on 19 May 2022]

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

Authors:Marc Andreas Schmitt
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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 - 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.

Comments: Submitted to the International Journal of Information
          Management Data Insights, 21 pages, 4 figures
Subjects: Machine Learning (cs.LG); Computational Engineering,
          Finance, and Science (cs.CE); Databases (cs.DB)
Cite as:  arXiv:2205.09337 [cs.LG]
          (or arXiv:2205.09337v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2205.09337
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Marc Schmitt [view email]
[v1] Thu, 19 May 2022 06:28:31 UTC (413 KB)
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