Abstract: Big data and relevant technologies have not only provided unprecedented amounts of data related to the macroeconomy and the whole society,formalizing a big data “ecology”,but also influenced and reshaped the process of public policy making and operation.Meanwhile,intensive attention from the field of economic research,particularly from the perspective of central banks all over the world,has been paid to big data and relevant analytical methodologies.

We know that the main functions of a central bank are,within the framework of a country's monetary policy operations,to use conventional monetary policy tools (such as open market operations,discount-window loans,and required reserve ratios),or unconventional monetary policy tools,to adjust interest rates and money supply,to achieve the mandates of monetary policy,such as full employment and price stability.In different stages of monetary policy operations,including before,during,and after,the central bank's daily work includes:collecting a large amount of data,conducting regular data analysis,macroeconomic forecasting,and economic cycle analysis; releasing regular monetary policy reports and communicating with the public (traditional press conferences along with widespread use of social media such as Twitter and Facebook overseas,and Weibo and WeChat in China); and conducting micro-financial supervision and macro-prudential supervision based on a large amount of financial data,and so on.It's worth noting that,especially after the 2008 Great Recession,central banks around the world have paid more attention to macro-prudential supervision,closely monitoring real-time dynamics in specific financial markets,such as shadow banking,systemically important financial institutions,and the real estate market,through big data analysis.Therefore,in the current  big data era,from the perspective of central banks,we want to address the following questions through a review of the literature:With the emergence of big data and related technologies,what new changes have occurred in data collection and analysis by central banks,particularly in the field of macro finance research and analysis,and in which specific areas of macro finance?Alongside new granular micro financial data and new analytical tools,what interesting new predictions and analysis results have emerged?Have new applications arisen in the fields of monetary policy communication,macroeconomic forecasting,and macro-prudential supervision?In comparison to traditional data and analytical methods,what advantages do big data analytics have,and has it also brought new problems,risks,and challenges?

This paper conducts a comprehensive review of recent heuristic efforts in applying big data analytics to macro finance,offering contributions of review as follows.Firstly,the review focuses on a central bank's perspective.Secondly,it covers diverse data types such as textual data and emerging economic indicators (e.g.,electronic payments,mobile data,satellite images).Thirdly,it employs varied analytics like Bayesian dynamic factor models for real-time economic trend estimation.Lastly,it provides insightful suggestions for future research and application,particularly concerning China.The review identifies three key literature domains.First,researchers extract structural insights from various central bank communication channels,including press releases and media sentiment.Second,big data enables more accurate macroeconomic forecasting,even narrowing the gap between the current and most recent data (nowcasting).Third,big data bolsters macro-prudential policies by offering indicators for policy framework enhancement,supervision,crisis prediction,and market trends.While big data's potential is acknowledged,unexplored avenues persist,especially in China.Recommendations include analyzing media sentiment regarding monetary policies,leveraging China's data-rich environment for better nowcasting,and exploring central bank digital currency (CBDC) applications in big financial data collection and analysis.