Integrated Data Management Processes Expedite Common Data Management Tasks in Autism Research

Frank Farach from Prometheus Research, LLCpresents his poster “Integrated Data Management Processes Expedite Common Data Management Tasks in Autism Research,” at the 25th APS Annual Convention in Washington, DC.

Many researchers engage in disposable data management (DDM) practices: They clean and organize data after a study has been finished, repeating the process for each new analysis. Anecdotal evidence suggests that these DDM approaches are inefficient because they waste money, human resources, and valuable time. In contrast, integrated data management (IDM), is a systematic process for managing data as a reusable resource. We investigated whether our organization’s adoption of IDM practices allowed data analysts to more quickly complete investigators’ data-management requests compared to pre-existing DDM practices at client organizations.

Using two years’ worth of available records logged by time- and request-tracking software, we examined the efficiency with which analysts responded to investigators’ data quality, data analysis, and study-tracking requests. To complete these requests, analysts used the Research Exchange Database (RexDB), an open-source IDM system that can accommodate multiple studies, heterogeneous data types, and multi-site collaboration. We also asked clients to retrospectively estimate the time it took to manage data with DDM practices, as these data predated the data collection period for IDM practices.

We found that using IDM practices allowed analysts to more quickly complete data management requests. On average, analysts completed requests more than twice as fast (144 mins) when compared to clients completing similar requests using DDM approaches (330 mins). Time to completion varied by request type with data quality taking the longest, followed by data analysis and study tracking. Repeat requests were completed more quickly than new requests.

Overall, our findings suggest that implementing IDM process with an IDM system can expedite data management tasks. Data cleaning and organization are likely to remain challenging, as the number of data sources to manage continues to increase. We expect IDM practices will be most valuable when both the cost of data acquisition and the probability of data reuse are high.

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