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Reasons Data Science Projects Fail

Ryohei Fujimaki summarizes some of the reasons why data science projects can fail:

According to Gartner analyst Nick Heudecker, over 85% of data science projects fail.  A report from Dimensional Research indicated that only 4% of companies have succeeded in deploying ML models to production environment.

Even more critical, the economic downturn caused by the COVID-19 pandemic has placed increased pressure on data science and BI teams to deliver more with less. In this down market, organizations are reassessing which AI/ML models they should develop, how to optimize resources and how to best use valuable budget dollars for maximum impact. In this type of environment, AI/ML project failure is simply not acceptable.

That 85% sounds suspiciously like the percentage of failed business intelligence and data warehouse projects, as well as the percentage of failed big data projects. It’s close enough that it makes me want to come up with some overarching idea that projects based on the consolidation of multiple independent data systems across several business units are liable to fail about 5/6 of the time.