High-quality data is a necessity for successful research and development endeavors. In this article, we review relevant literature for data quality (DQ) assessment methods in different domains and discuss the possibilities, challenges and constraints of applying them to mobile sensing. We identify DQ dimensions directly applicable to sensor data: believability (comparison with the correct operating bounds), completeness (missing values), free-of-error (erroneous values), consistency (over time), timeliness (delay), accuracy (deviation from true value) and precision (granularity of readings) are core aspects of high-quality sensor data. We also emphasize that sensor data must be representative of the originating type of sensor. We propose an altruistic approach to DQ assessment for sensor data that facilitates aggregating and sharing of domain knowledge through a community-contributed library of DQ assessment methods organized by sensor type.