Graduate and undergraduate students involved in research projects that generate or analyze extensive datasets use several software applications for data input and processing subject to guidelines for ensuring data quality and availability. Data management guidelines are based on existing practices of the associated academic or funding institutions and may be automated to minimize human error and maintenance overhead. This paper presents a framework for automating data management processes, and it details the flow of data from generation/acquisition through processing to the output of final reports. It is designed to adapt to changing requirements and limit overhead costs. The paper also presents a representative case study applying the framework to the finite element characterization of the magnetically coupled linear variable reluctance motor. It utilizes modern widely available scripting tools particularly Windows PowerShell® to automate workflows. This task requires generating motor characteristics for several thousands of operating conditions using finite element analysis.