One of the key problems we face with the accumulation of massive datasets (such as electronic health records and stock market data) is the transformation of data to actionable knowledge. In order to use the information gained from analyzing these data to intervene to, say, treat patients or create new fiscal policies, we need to know that the relationships we have inferred are causal. Further, we need to know the time over which the relationship takes place in order to know when to intervene. However, these observational data are often noisy and prone to missing values. In this talk I discuss methods for directly incorporating data uncertainty into the inference process and show how this can be applied to mobile sensor data from people with diabetes.