Joseph Cazier
Joseph Cazier, CAP
Clinical Professor and Associate Director of the Center for AI and Data Analytics at Arizona State University
Track: Analytics Leadership
Leading in Analytics: The Seven Critical Tasks for Executives to Master in the Age of Big Data
Now more than ever firms are investing in AI and Analytics efforts. However, the reality is that in spite of a few very big headline-making wins, most AI and analytics projects fail. It’s not that they don’t produce a potentially useful algorithm or result, it’s that they fail where it really matters: They fail to produce the significant financial benefit that the organization expected. When that is the case, in the eyes of the sponsors and leaders the project will be considered a failure. Shockingly, the present failure rate is in the 80% to 90% range (Ransbotham et. al., 2020).
However, it does not need to be this way. Most of the major causes of project failure are known. In fact, they were written about by Karl Kempf (2018) in Chapter 2. of the INFORMS Analytics Body of Knowledge (ABOK). Most of the reasons for failure can be managed with good analytics leadership, hence Kempf named them Manageable Tasks. This presentation reviews and expands these major causes of analytics failure and shares best practices for managing them based on interviews with highly successful professionals.
