They say decision makers misunderstand or oversimplify their analysis and expect them to do magic, to provide the right answers to all their questions. Data teams know they’re sitting on valuable insights but can’t sell them. In my work lecturing and consulting with large organizations on data visualization (dataviz) and persuasive presentations, I hear both data scientists and executives vent their frustration. In a question on Kaggle’s 2017 survey of data scientists, to which more than 7,000 people responded, four of the top seven “barriers faced at work” were related to last-mile issues, not technical ones: “lack of management/financial support,” “lack of clear questions to answer,” “results not used by decision makers,” and “explaining data science to others.” Those results are consistent with what the data scientist Hugo Bowne-Anderson found interviewing 35 data scientists for his podcast as he wrote in a 2018 HBR.org article, “The vast majority of my guests tell that the key skills for data scientists are….the abilities to learn on the fly and to communicate well in order to answer business questions, explaining complex results to nontechnical stakeholders.” Efforts fall short in the last mile, when it comes time to explain the stuff to decision makers. Even well-run operations that generate strong analysis fail to capitalize on their insights. Data has begun to change our relationship to fields as varied as language translation, retail, health care, and basketball.īut despite the success stories, many companies aren’t getting the value they could from data science.
Over the past five years companies have invested billions to get the most-talented data scientists to set up shop, amass zettabytes of material, and run it through their deduction machines to find signals in the unfathomable volume of noise.
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