This Appendix describes Data Validation and Preparation measures that should be taken prior to loading data into dTIMS. Although dTIMS can perform some validation on data entry, it is often quicker and easier to perform such checks in a simple spreadsheet or database package. And, given that dTIMS is an enterprise data repository, then if data gets loaded into dTIMS, it becomes available to other users – data should be validated prior to data entry to prevent accidental use of invalid data by others.
The validation checks described here are typical checks that should be performed against any data, given typical data specifications. However, additional checks might also have to be performed, depending on the individual data specifications. Any unit collecting data should produce a Quality Management Plan that describes in detail the data collection procedures and the steps that are taken to ensure quality data. The Data Specifications and Quality Management Plan might impose other restrictions on data and, if so, the validation procedures described here should be updated.
If the data collection team has produced and followed proper quality management procedures, then the validation checks described here should simply demonstrate that the data is valid. If the data does not pass the validation checks, then it probably indicates issues in the Quality Management Plan and/or the following of the Quality Management Procedures, which should be addressed with the data collection and processing teams.
This Appendix also contains a checklist of tests that should be performed on data. This checklist should be filled in and kept in a structured format for every data file received, to prove or demonstrate to management or stakeholders that proper validation of data in the RMS has taken place.
