While driver telematics has gained attention for risk classification in auto insurance, scarcity of observations with telematics features has been problematic, which could be owing to either privacy concern or adverse selection compared to the data points with traditional features. To handle this issue, we explore multiple data integration approaches and assess their performance both in inference and prediction in a case study. It is shown that one of the approaches, propensity score approach, can efficiently integrate the so-called traditional data and telematics data pre-preprocessed in a tabular format and also cope with possible adverse selection issues on the availability of telematics data compared to other existing approaches. We expect that this research can encourage further discussions and interests on telematics data handling by the ML community.