Data Farming

Abstract

Data farming is a descriptive metaphor that captures the notion of generating data purposefully to maximize the information “yield” from simulation models. Large-scale designed experiments let us grow the simulation output efficiently and effectively. We can explore massive input spaces, uncover interesting features of complex simulation response surfaces, and explicitly identify cause-and-effect relationships. Data farming has been used in the defense community over the past two decades, and has resulted in quantum leaps in the breadth, depth, and timeliness of the insights yielded by simulation models. In this article, we provide an overview of current data farming capabilities and their relationship to emerging techniques in data science and analytics. We use graphics to motivate insight into some of the benefits of a data farming approach. Finally, we share some thoughts about opportunities and challenges for further improving the state of the art, and transforming the state of the practice, in the data farming domain.