The emergence of smart devices, in particular mobile devices, has widened the potential types of data that can be collected in the social sciences. Given the fact that traditional surveys require more and more effort, new data sources collected via smart devices can potentially improve measurement of variables of interest and representation of the target population. New data sources also bring new challenges. New data sources, such as smartphone sensor data offer less control and coherence than traditional data such as administrative and survey data do, and are therefore often integrated either during, or after data collection. Respondents remain in control and help to resolve inconsistencies, missing data, or provide context. We discuss the consequences of hybrid data collection and methodology needed to reduce errors in smart surveys. Since quantification of the various types of errors is costly and time-consuming, we present a number of practical criteria. These criteria help evaluate whether hybrid data collection may have a positive business case and justify further investments in systems for data collection, data processing, and integration. We apply the criteria to three case studies: the measurement of mobility, consumption, and physical activities. Large-scale field experiments have been conducted for each of the case studies that allow us to evaluate the criteria.