Most corporations are acquiring massive datasets and treating them like corporate assets. In the context of business intelligence, these assets require continuous maintenance and curation activities to obtain certain degrees of quality and value for dedicated business processes. However, it is interesting to know what happens to the data that is not needed after a particular use and whether other business processes exist that could use it. A particularly obvious situation is in performing data analytics, where the data value and data fitness for specific use requirements should be determined. Woodall, just like in his previous works, recognizes the real potential of data analytics and the obstacles in repurposing data that is collected within corporate activities and from external information repositories. His thought is that after data has been used for its intended purposes, it should not be deleted but used for other purposes instead.
The crucial term in the process of data repurposing is data collection, and Woodall starts his work with insight into the differences between ephemeral data use data collection and self-service data collection; each has its own characteristics explained in a narrative way. Known and affirmed statements on these issues are well presented in the current literature. However, Woodall goes further with a view on the pressures that these two types of data collection have on data quality when the original purpose for data use is changed. He does not provide solutions or a research matrix, but confirms an actual need for new quality requirements when the purpose for data use changes.
Thus, exact data quality problems that businesses face when repurposing data are not defined and there is no brief background on data repurposing in the context of business intelligence and information systems, which would help to define the data repurposing role in modern businesses working with huge collections of datasets (internally or in the cloud). Consequently, this paper asks for further research in identifying solutions to data repurposing challenges and enabling businesses to use collected data not just for its primary purpose, but also for other uses in business activities.