We found that driving true business impact is amazingly hard, plus it is difficult to isolate and understand the connection between efforts on modeling and the observed impact

(written by lawrence krubner, however indented passages are often quotes). You can contact lawrence at: lawrence@krubner.com, or follow me on Twitter.

This is what I’ve seen as well, large investments in what is described as AI or Machine Learning or Data Science, which break down into two things:

1.) people are actually talking about standard statistics and ordinary Business Intelligence

2.) people are talking about Machine Learning, but the results are difficult to connect to actual business needs.

Interesting:

We found that driving true business impact is amazingly hard, plus it is difficult to isolate and understand the connection between efforts on modeling and the observed impact… Our main conclusion is that an iterative, hypothesis driven process, integrated with other disciplines was fundamental to build 150 successful products enabled by machine learning.

In case you’re tempted to stop reading at this point, please don’t interpret that quote as saying that investing in machine learning isn’t worth it. On the contrary, developing an organisational capability to design, build, and deploy successful machine learned models in user-facing contexts is, in my opinion, as fundamental to an organisation’s competitiveness as all the other characteristics of high-performing organisations highlighted in the State of DevOps reports. (And by the way, wouldn’t it be wonderful to see data confirming or denying that hypothesis in future reports!).

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