How to Make a Success Story of your Data Science Team

Data science resounds throughout every industry and has reached the mainstream media. I no longer have to explain what I do for a living as long as I call it AI  —  we are at the peak of data science hype!

As a consequence, more and more companies are looking towards data science with big expectations, ready to invest into a team of their own. Unfortunately, the realities of data science in the enterprise are far from a success story.

NewVantage published a survey in January 2019 which found that 77% of businesses report challenges with business adaptation. This translates into ¾ of all data projects collecting dust rather than providing a return on the investment. Gartner has always been very critical of the data science success and they haven’t gotten more cheerful as of late: According to Gartner January 2019, even analytics insights will not deliver business outcomes through 2022, what’s the hope then for data science? It’s apparent that for some reasons making data science a success is really hard!

Me scaring Execs about their data science investments at the Data Leadership Summit, London 2019.

Regardless of whether you manage an existing data science team or are about to start a new greenfield project in big data or AI, it’s important to acknowledge the inevitable: the Hype Cycle.

Luc Galoppin on Flickr

The increasing visibility of data science and AI comes hand in hand with a peak of inflated expectations. In combination with the current success rate of such projects and teams we are headed straight for the cliff edge towards the trough of disillusionment.

Christopher Conroy summarised it perfectly in a recent interview for Information Age: the renewed hype around AI simply gives a false impression of progress from where businesses were years ago with big data and data science. Did we just find an even higher cliff edge?

Thankfully, it’s not all bad news. Some teams, projects and businesses are indeed successful (around 30% according to the surveys). We simply need a new focus on the requirements for success.


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