Assessment of individuals' job performance, personalized health and psychometric measures are domains where data-driven ubiquitous computing will have a profound impact in the near future. Existing work in these domains focus on techniques that use data extracted from questionnaires, sensors (wearable, computer, etc.), or other traits to assess well-being and cognitive attributes of individuals. However, these techniques can neither predict individuals' well-being and psychological traits in a global manner nor consider the challenges associated with processing the often incomplete and noisy data available. In this paper, we create a benchmark for the predictive analysis of individuals from a perspective that integrates physical and physiological behavior, psychological states and traits, and job performance. We develop a novel data mining framework that can extract meaningful predictors from noisy and incomplete data derived from wearable, mobile and social media sensors to predict nineteen constructs based on twelve standardized and well-validated tests. The framework can be used to build a predictive model of outcomes of interest. We validate the framework using data from 757 knowledge workers in organizations across the United States with varied work roles. Our framework and resulting model provides the first benchmark that combines these various instrument-derived variables in a single framework to understand people's behavior. The results show that our framework is reliable and capable of predicting our chosen variables better than the baselines when prediction includes the noisy and incomplete data.