In this rapidly changing world, improving the capacity to predict future dynamics of ecological systems and their services is essential for better stewardship of the earth system. Prediction relies on models that describe our understanding of the major processes that underlie system dynamics and data about these processes and the present state of ecosystems. Prediction becomes more effective when models are well informed by data. A technological revolution in the capacity to collect data now provides very different opportunities to test hypotheses and project future dynamics than when many standard statistical tests were first developed. Data assimilation is an emerging statistical approach to combine models with data in a rigorous way to constrain model parameters and system states, identify model error, and improve ecological prediction. In this paper, we illustrate how data assimilation can improve ecological prediction to support decision-making by reviewing applications of data assimilation across four different research fields: (1) emerging infectious disease, (2) fisheries, (3) fire, and (4) the terrestrial carbon cycle. Across these fields, data assimilation substantially improves prediction accuracy, highlighting its important role in enabling predictive ecology. Data assimilation with regional and global models faces major challenges, such as the large number of parameters to be estimated, high computational demands, the need to integrate multiple and heterogeneous data sets, and complex social-ecological interactions. Nevertheless, data assimilation provides an important statistical approach that has great potential to enhance the predictive capacity of ecological models in a changing climate.