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Within the performance evaluation and calibration communities, test programs are driven by requirements, test duration and cost. A properly conceived data management plan is crucial to being able to optimize data collection in support of requirements verification, and to minimize test duration and cost. A properly constructed data system enables rapid data retrieval and analysis, both during the calibration and test phase of a program and during the long-look analysis. It is imperative that the data outputs of a system are systematically evaluated for quality before a test configuration is altered because mistakes often require re-testing and further cost. Once data has been validated, effective data management permits maximum traceability and rapid retrieval. Analysis time can be significantly reduced by an organized data archive that can be navigated through facilitators (i.e. a database).
Large-scale experimental programs in the field of space technology produce an enormous quantity of data that must be analyzed and reduced efficiently for requirement verification. Programs of this nature typically demand a quick turn-around time between the collection event and the stage known as data validation. Data validation, often described as a “quicklook” of the data, is meant to verify the scientific quality of the data. This step is performed immediately following collection and does not have the goal of producing final analysis results. Rather, its purpose is to give the test director confidence that the data has been collected properly so that he/she can instruct the team to proceed to the next test or break configuration. With each test program literally costing money by the minute, reducing the time to validate data directly translates to a shorter test duration and higher cost savings. |
