BACKGROUND: Several forces are converging to transform ecological research and increase its emphasis on quantitative forecasting in ecology. These forces include (1) dramatically increased volumes of data from observational and experimental networks, (2) increases in computational power, (3) advances in ecological models and related statistical and optimization methodologies, and most importantly, (4) societal needs to develop better strategies for natural resource management in a world of progressive global change. Traditionally, ecological forecasting has been based on process‐oriented models, informed by data in largely ad hoc ways. Although most ecological models incorporate some representation of mechanistic processes, today's models have not taken full advantage of big data and are generally not adequate to quantify real‐world dynamics and provide reliable forecasts with accompanying estimates of uncertainty. To advance ecological forecasting, it is essential to employ hybrid deep learning with process-based modeling. The former can effectively explore information in big data, whereas the latter integrates our process understanding. GOALS AND OBJECTIVES: This symposium will bring together novel methods, such as deep learning, data assimilation, and Earth system modeling, to harness the information and knowledge contained in big data to advance ecological forecasting. IMPORTANCE: Our ability to collect and create data far outpaces our ability to sensibly assimilate it, let alone understand it. Predictive ability in the last few decades has not increased apace with data availability. To get the most out of the explosive growth and diversity of Earth system data, we have to develop and explore various new methods. This symposium is timely as a means to foster exploration of those novel methods for their applications in ecological research. INTEREST TO THE MEMBERSHIP OF ESA: This symposium fits the theme of 2020 ESA annual meeting very well and is timely for ESA membership. The meeting theme is “Harnessing the ecological data revolution”. As stated in the meeting theme, ecology as a scientific discipline is flooded by massive and diverse sources of data. This big data opens up new avenues of research, allows ecologists to address more complex questions, and helps advance ecological forecasting. However, we lack methods to harness the information and knowledge contained in the big data. This symposium will address the thematic issues for the 105th ESA annual meeting on employing novel and integrative approaches to harnessing the data revolution to address pressing issues in ecology.