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Every organization needs to effectively manage its data collection and analysis process in order to efficiently collaborate on a global scale. This paper describes a model for standardizing the data collection and analysis process and specifically deals with two challenges in this regard: 1) A method for standardization of the nomenclature of different physical parameters measured during a typical engine test. This is essential for processing data from facilities spread across the globe to run them through a standard set of calculations. The process of storing and performing a given set of complex processes on the data while allowing analysts to view the steps of the processing in a transparent intuitive manner is also described in the paper. 2) Building on the first point, the paper also describes a process for performing a standard set of data quality checks on data as it is being collected. This allows for detection of issues in the data on a real-time basis.

Typical software that is used for engine test result analysis include spreadsheet (MS Excel) and statistical software (Minitab, MS Excel), data modelling applications (MS Excel, Matlab), and post processing tools for plots, tables etc. Open source software has come a long way since its inception, and is now at a stage where it can provide a suitable alternative to many commercial software packages for data analysis. The paper will describe the areas of concern and the inherent benefits to using open source software. It evaluates the feasibility of incorporating these open source tools into data analysis packages for Engine Development by breaking down the process of data analysis into specific software requirements and then evaluating each of the open-source alternatives against these requirements. The paper will conclude by describing a practical strategy when converting an existing data analysis process over to its open-source equivalent.