The term "big data" has first appeared for nearly two decades and has quickly become a symbol for data analytics by helping to solve the most complex problems of research. One of the important applications of big data is to improve the timeliness and accuracy of economic forecasts. Before the advent of big data, policymakers needed to wait for the periodic release of macroeconomic statistics to forecast GDP and inflation. Today, high frequency economic time series allow researchers to make forecasts more frequently, faster, and, in some cases, significantly more accurately than traditional forecasting methods. This paper will examine the use of big data in economic forecasting by providing an overview of empirical studies on this topic. In addition, the article also provides some discussions on challenges and solutions in using big data related to investment in technical infrastructure for analysis and handling of unstructured data, as well as accessibility and privacy when using big data.