Historically urban planners have been educated and trained to work in a data poor environment. Urban planning students take courses in statistics, survey research and projection and estimation that are designed to fill in the gaps in this environment. For decades they have learned how to use census data, which is comprehensive on several basic variables, but is only conducted once per decade so is almost always out of date. More detailed population characteristics are based on a sample and are only available in aggregated form for larger geographic areas. But new data sources, including distributed sensors, infrastructure monitoring, remote sensing, social media and cell phone tracking records, can provide much more detailed, individual, real time data at disaggregated levels that can be used at a variety of scales. We have entered a data rich environment, where we can have data on systems and behaviors for more frequent time increments and with a greater number of observations on a greater number of factors (The Age of Big Data, The New York Times, 2012; Now you see it: simple visualization techniques for quantitative analysis, Berkeley, 2009). Planners are still being trained in methods that are suitable for a data poor environment (J Plan Educ Res 6:10–21, 1986; Analytics over large-scale multidimensional data: the big data revolution!, 101–104, 2011; J Plan Educ Res 15:17–33, 1995). In this paper we suggest that visualization, simulation, data mining and machine learning are the appropriate tools to use in this new environment and we discuss how planning education can adapt to this new data rich landscape. We will discuss how these methods can be integrated into the planning curriculum as well as planning practice.