Several recent reviews summarize common missing value analysis methods. However, none of them provide a systematic and in-depth summary of the analytical challenges and solutions for dealing with missing values. For the purpose of guiding the handling of missing values, this review aims to consolidate current developments in novel missing-value research methodologies. In particular, we comprehensively investigated cutting-edge missing value solutions and methodically studied the main challenges associated with missing values analysis (missing mechanisms, missing patterns, and missing rates). Furthermore, we reviewed 63 publications that compare different strategies for deleting and imputing missing values. Then we investigated data characteristics, highlighted three main problems when analyzing missing values, and analyzed the performance of missing value solutions in these studied papers. Moreover, we conducted comprehensive experiments on 9 public datasets using typical missing value processing methods and provided a simple guided decision tree for handling missing values. Finally, we described current Research hotspots and open challenges, which give potential research topics.