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

I'd be curious to understand why this is the case. It seems that businesses generally have highly network style data -- think of your average system for a "widget" business... it ranges from orders to suppliers to manufacturing processes, advertising, all the way to orders from customers. All stored in different systems with disparate join keys.

Most of the hard part of BI I have seen is joining these pieces of information together that are otherwise uncorrelated -- e.g which clicks corresponded to conversion is a tracking problem, then correlating them with actual business actions (e.g running a marketing campaign or improving a process)

It's more about data joining and data aggregation than "ML"

PDF: https://arxiv.org/ftp/arxiv/papers/2205/2205.09337.pdf

"...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..."

This is an incredibly shallow paper. GNNs are mostly used on structured data today and are a part of the Deep Learning family, as are transformers.

The authors seem to be unaware of that and are pegging deep learning as a very narrow implementation.

Very few businesses have the Google/Meta level of data collection. In the low-data environments, SVMs (as an example), more often than not, outperform corresponding DL models.

And here I am mostly doing t-tests...