We frequently hear about advanced analytics (AA) success stories. There are high expectations — McKinsey predicts AA and AI will deliver between $9.5 trillion and $15.4 trillion in annual economic value — so it is only natural that many will want to shine a spotlight on progress whenever possible.
However, practitioners will be all too aware that it’s not all success in advanced analytics. For every impressive case study or exciting headline, there are dozens of projects which have failed to deliver on their potential. The exploratory, counterintuitive and technical nature of advanced analytics projects are typically quoted as reasons for the challenges that each project faces. What else lies behind these failures?
Over the last few months we have interrogated our own experience and discussed with advanced analytics leaders and practitioners across multiple industries, asking what causes unsuccessful analytics projects. The following five cross-cutting failure modes, agnostic to industry, emerged as common themes that projects will need to overcome in order to deliver full potential.
The rest of the article can be found at QuantumBlack Medium page where I discuss the topic together with my co-authors Dan Feldman, Justin Hevey and Cris Cunha.
This article reflects my personal views and opinions only, which may be different from the companies and employers that I am associated with.