Buildings have a significant impact on global sustainability. During the past decades, a wide variety of studies have been conducted throughout the building lifecycle for improving the building performance. Data-driven approach has been widely adopted owing to less detailed building information required and high computational efficiency for online applications. Recent advances in information technologies and data science have enabled convenient access, storage, and analysis of massive on-site measurements, bringing about a new big-data-driven research paradigm. This paper presents a critical review of data-driven methods, particularly those methods based on larger datasets, for building energy modeling and their practical applications for improving building performances. This paper is organized based on the four essential phases of big-data-driven modeling, i.e., data preprocessing, model development, knowledge post-processing, and practical applications throughout the building lifecycle. Typical data analysis and application methods have been summarized and compared at each stage, based upon which in-depth discussions and future research directions have been presented. This review demonstrates that the insights obtained from big building data can be extremely helpful for enriching the existing knowledge repository regarding building energy modeling. Furthermore, considering the ever-increasing development of smart buildings and IoT-driven smart cities, the big data-driven research paradigm will become an essential supplement to existing scientific research methods in the building sector.