Organizations struggle to communicate the insights in all the information they’ve amassed. Here’s why, and how to fix it.
Despite heavy investments to acquire talented data scientists and take advantage of the analytics boom, many companies have been disappointed in the results. The problem is that those scientists are trained to ask smart questions, wrangle the relevant data, and uncover insights—but not to communicate what those insights mean for the business. To be successful, the author writes, a data science team needs six talents: project management, data wrangling, data analysis, subject expertise, design, and storytelling. He outlines four steps for achieving that success: (1) Define talents, not team members. (2) Hire to create a portfolio of necessary talents. (3) Expose team members to talents they don’t have. (4) Structure projects around talents.
Buy CopiesCompanies responded to the analytics boom by hiring the best data scientists they could find—but many of them haven’t gotten the value they expected from their data science initiatives.
For an analytics project to create value, the team must first ask smart questions, wrangle the relevant data, and uncover insights. Second, it must figure out—and communicate—what those insights mean for the business. The ability to do both is extremely rare—and most data scientists are trained to do the first, not the second.
A good data science team needs six talents: project management, data wrangling, data analysis, subject expertise, design, and storytelling. The right mix will deliver on the promise of a company’s analytics.
Data science is growing up fast. 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. It’s working—to a point. Data has begun to change our relationship to fields as varied as language translation, retail, health care, and basketball.
