Data repurposing involves using old data in new ways, that were not foreseen by the people who originally collected the data. Data repurposing comes in the following categories: (1) Using the preexisting data to ask and answer questions that were not contemplated by the people who designed and collected the data; (2) Combining preexisting data with additional data, of the same kind, to produce aggregate data that suits a new set of questions that could not have been answered with any one of the component data sources; (3) Reanalyzing data to validate assertions, theories, or conclusions drawn from the original studies; (4) Reanalyzing the original data set using alternate or improved methods to attain outcomes of greater precision or reliability than the outcomes produced in the original analysis; (5) Integrating heterogeneous data sets (ie, data sets with seemingly unrelated types of information), for the purpose of answering questions or developing concepts that span diverse scientific disciplines; (6) Finding subsets in a population once thought to be homogeneous; (7) Seeking new relationships among data objects; (8) Creating, on-the-fly, novel data sets through data file linkages; (9) Creating new concepts or ways of thinking about old concepts, based on a re-examination of data; (10) Fine-tuning existing data models; and (11) Starting over and remodeling systems.
Berman JJ. Repurposing Legacy Data: Innovative Case Studies. Morgan Kaufmann, Waltham, MA, 2015.
-Jules Berman (copyrighted material)
key words: reanalysis, data science, secondary data, primary data, data integration, jules j berman