The importance of a data analysis request system/pipeline
Hi all – Rob here.
Today I wanted to write about processing data analysis requests. For a small organization with maybe one analyst/statistician a formal system for handling requests may not be necessary. However, as the organization grows, multiple investigators with multiple lines of research may need several different analyses from several different data sets. Managing these requests can be time-consuming and problematic. The risk is that an analysis is not done in time for a deadline or other reason. Therefore, your analysis team should develop a consistent process/workflow that is understood both the team and the investigators requesting analyses.
An example of a workflow might be:
- The investigator submits a written request for analysis that includes all of the pertinent information required to complete the analysis.
- The analysis team approves or rejects the request based on its content. Rejection may be because the request is incomplete or additional items need to be considered.
- The analysis team conducts the analysis and generates relevant statistics, output, and figures (essentially the method and results sections of a publication).
- The investigator receives the output of the analysis which is then used to write a manuscript for publication.
There may be other elements of this process – for instance some organizations may require a senior member of the investigator team to approve all analysis requests in addition to the approval process by the analysis team.
This process can easily be implemented on paper using printed request forms but could also be implemented via a web-application. In fact, for the CTRSC, this is exactly what we have done with our Investigator Research Tool (IRT). The goal of the IRT is to provide an electronic method for making an analysis request and receiving the output. The advantage of using this system over paper is that we will be able to more easily collect statistics on the requests we have received. This will allow us to identify bottlenecks in our process and improve efficiency.
We have just rolled out the IRT, so I don’t have statistics yet. In the spring I will write again about how things have fared with our system and what problems we encountered.
Until next time . . .