Analytics is getting a great deal of attention in both industrial and academic venues. Organizations of all types are becoming more serious about transforming data from a variety of sources into insight, and analytics is the key to that transformation. Academic institutions are rapidly responding to the demand for analytics talent, with hundreds of offerings aimed at producing a broad range of analytical graduates from data scientists to data-savvy managers and functional specialists. Curricula generally provides best practice methods of tackling descriptive, predictive, and prescriptive analytics; but there has been little discussion about the disruptive nature of increasingly robust analytical tools in the academic space. The net effect of astounding tool capability is empowerment of less technically trained people to address analytical complexities heretofore only comprehensible to data scientists with in-depth knowledge of mathematics, programming, and statistics. This article examines skills needed for analytics in industry, academic response, and evolving analytic needs through the lens of disruption theories of Clayton Christensen. We offer a direction of curriculum development by supplementing the disruption theory with three interactive components of problem space mapping: processes, tools, and techniques. The challenge for academicians is a dynamic, adaptable curricula addressing multiple levels of data analyses.