Performance data offers a powerful tool for system condition assessment and health monitoring. In most applications, a host of various types of sensors is employed and data on key parameters (describing the system performance) is compiled for further analysis and evaluation. In ensuring the adequacy of the data acquisition process, two important questions arise: (1) is the complied data robust and reasonable in representing the system parameters; and (2) is the duration of data acquisition adequate to capture a favorable percentage (say for example 90%) of the critical values of a given system parameter? The issue related to the robustness and reasonableness of data can be addressed through known values for key parameters of the system. This is the information that is not often available. And as such, methods based on trends in a given system parameter, expected norms, the parameter's relation with other known parameters, and simulations can be used to assure the quality of the data. This paper discusses those methods and explains that statistical analysis of trends and correlations can be used as a means to assure the quality of the compiled data. Specifically, the paper provides answers to the two questions raised. And as such, the paper also offers reasonable estimates for the duration of data acquisition time period depending on the type of application desired. The suggested methods for the data quality assurance and data acquisition time periods can be implemented both within the data recording system (at the site) or after the data is compiled and are expected to optimize the data acquisition process.