Data quality became significant with the emergence of data warehouse systems. While accuracy is intrinsic data quality, validity of data presents a wider perspective, which is more representational and contextual in nature. Through our article we present a different perspective in data collection and collation. We focus on faults experienced in data sets and present validity as a function of allied parameters such as completeness, usability, availability and timeliness for determining the data quality. We also analyze the applicability of these metrics and apply modifications to make it conform to IoT applications. Another major focus of this article is to verify these metrics on aggregated data set instead of separate data values. This work focuses on using the different validation parameters for determining the quality of data generated in a pervasive environment. Analysis approach presented is simple and can be employed to test the validity of collected data, isolate faults in the data set and also measure the suitability of data before applying algorithms for analysis.