The purpose of developing a data science process in the context of specific problems is to find synergies across application domains with respect to the data science process and use. The ultimate goal is to develop a disciplined process of identifying data sources, preparing them for use, and then assessing the value of these sources for the intended use(s).

Understanding how to approach fitness for use starts with considering the modeling and analyses that will use the data. Modeling depends on the research questions and the intended use of the data to support the research hypotheses. Fitness assessment should be about the fitness of the data for the modeling, from straight forward tabulations to complex analyses. Therefore,fitness is a function of the modeling, data quality needs of the models, and data coverage (representativeness) needs of the models. Finally, fitness should characterize the information content in the results.