The field of ecology has been quickly transformed into a data-rich scientific enterprise due to (1) development and implementation of environmental sensors and (2) continuous measurements of ecological processes by research networks such as LTER and AmeriFlux. The implementation of NEON (National Ecological Observatory Network) will dramatically increase the amount of ecological time-series data collection. There is a rapidly growing demand to process massive data into ecologically meaningful information products to support decision making for resource management and climate change mitigation. Forecasting generally means a capability to project future states of the system by modeling the evolution of the system as a function of its state at an initial time. To forecast dynamics of ecological systems, we need models to represent ecological systems. Data are required to accurately define model parameters, characterize the initial state of the system, and observe its evolution over time. The latter is particularly important in systems with complex or chaotic dynamics, where accurate parameters and initial conditions are insufficient to model time evolution. The goals of this symposium are (1) to prepare ESA for a data-rich, NEON-type era and (2) to catalyze transformation of ecological research to data processing, data-model assimilation, and ecological forecasting. Traditional ecological research is usually focused on data collection. A graduate student or a researcher typically collects a dataset and analyzes it for publication. In the data-rich, NEON era, national networks of sensors generate millions of data points every day. Ecologists have not been prepared for processing of such massive datasets. It is critical to build up a capacity to process the massive data sets and to generate data products that will enable the development, rigorous testing and application of models, lead to fast advancement of the science, and provide support of decision making for resource management and climate change mitigation.