> What the article proposes is like having a development team that has 100% time to pursue their interests and build whatever they like to show to the business

> What the article proposes is like having a development team that has 100% time to pursue their interests and build whatever they like to show to the business to develop new business capability. I didn't get this from the article. My interpretation was that rather than having a DS team with specialized roles sitting outside of product/engineering, companies should locate DS generalists within product teams. I.e., push data competency closest to the teams working with the problems + data, and as the DS learns where ML can be applied, the entire team can deliver. In a DS-as-specialist structure, you'd have to locate several roles on a product team to achieve results (or co-locate them and deal with overhead). The article confuses by claiming "the goal... is not to execute", but in context, it's clear that it's a criticism of process efficiency myopia, not of product delivery. And the word "research" only appears in the article in a negative connotation. “Develop profound new business capabilities” doesn't mean the research of new models, but instead the application of existing techniques to business processes (e.g. a model that can reasonably predict user churn based on sentiment analysis of messages sent on your platform would be profound).