Data science is too often discussed as a technical discipline, rather than a social and cultural one. But the role of data science is to both inform and automate decision-making processes, which require, in turn, humans to collaborate and communicate with each other and humans to collaborate with machines, both of which have key cultural and social dimensions. Why do so many executives feel that so little of the data work in their organizations actually delivers returns? How can we reduce friction in factoring the process of turning business questions into business answers through the intermediaries of data questions and data answers? What provisions need be in place to make sure that everybody is speaking enough of the same data languages to excel at their jobs? How do we promote data literacy throughout organizations while getting the job done? This talk is aimed at data professionals (and anybody else) who want to figure out how to establish healthy and productive data cultures in the workplace. I’ll conclude by interrogating the example of establishing the culture of modern distributed data science work in organizations and all the moving parts that need to be in place for it to function.