The design of data analytic solutions is often done using well-defined methodologies similar to those used in traditional systems analysis and design (SA&D). The major element of each of those methodologies is that it is a process composed of a set of related tasks that must be completed in a specific order, regardless of the size or scope of the project.  It is the purpose of this paper to compare and contrast traditional SA&D methodologies with those of data analytic solutions, in terms of their steps, to find optimal practices so as to develop a hybrid methodology that features the best of both frameworks.