Failure rates for analytics, AI, and big data projects = 85% – yikes!

(Note: this article is updated from time to time as I encounter similar studies and news on this theme.)

It's disturbing just how bad the success rates are for AI, data science, analytics, IOT, big data, and BI projects. Worse, the numbers from multiple annual studies aren't changing much. This post is an active tally of articles and primary sources for these [lousy!] survey numbers.

What's interesting to me about many of these is the common issue around "technology solutions in search of a problem." Companies cannot define precisely what the analysis or data or IOT is supposed to do for the end users, or for the business.

And, it hasn't changed in almost a decade according to multiple sources:

  • July 2019: VentureBeat AI reports 87% of data science projects never make it into production
  • Jan 2019: NewVantage survey reports 77% of businesses report that "business adoption" of big data and AI initiatives continues to represent a big challenge for business. That means 3/4 of the software being built is apparently collecting dust. Ouch.
  • Jan 2019: Gartner says 80% of analytics insights will not deliver business outcomes through 2022 and 80% of AI projects will “remain alchemy, run by wizards” through 2020.
  • Nov. 2017: Gartner says 60% of #bigdata projects fail to move past preliminary stages. Oops, they meant 85% actually. 
  • Nov. 2017: lists 7 sure-fire ways to fail at analytics. “The biggest problem in the analysis process is having no idea what you are looking for in the data,” says Tom Davenport, a senior advisor at Deloitte Analytics (source)
  • May 2017: Cisco reports only 26% of survey respondents are successful with IOT initiatives (74% failure rate) (source)
  • Mar 2015: Analytics expert Bernard Marr on Where Big Data Projects Fail (source)
  • Oct 2008: A DECADE AGO - Gartner's #1 flaw for BI services: "Believing 'If you build it, they will come...'" (source)

There are more failure-rate articles out there.

Couple these stats with failure rates for startup companies and...well, isn't it amazing how much time and money is spent building solutions that are underdelivering so significantly? It doesn't have to be like this.

Go out and talk to your customers 1 on 1. Find a REAL problem to solve for them. Get leadership agreement on what success means before you start building data pipelines and designing interfaces. If you're a leader, then you need to create an environment that fosters cross-department and direct customer engagement throughout the creation of your new data product, AI/ML, or analytics service.

If you skip the strategy part, and you'll just become another one of the statistics above.

If you want to learn how design and design thinking can help you deliver more engaging, useful, and indispensable analytics and enterprise data products, subscribe to my DFA Insights Mailing list. 

Photo courtesy of Tobias Scheck.

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