There is no unified definition of data anomalies, which refers to the specific data operation mode that may destroy the consistency of the database. Known data anomalies include Dirty Write, Dirty Read, Non-repeatable Read, Phantom, Read Skew, Write Skew, etc. In order to improve the efficiency of concurrency control algorithms, data anomalies are also used to define the isolation levels, because the weak isolation level can improve the efficiency of transaction processing systems. This work systematically studies the data anomalies and the corresponding isolation levels. Twenty-two new data anomalies are reported that have not been reported by other researches, and all data anomalies are classified miraculously. Based on the classification of data anomalies, two new isolation levels with different granularity are proposed, which reveals the rule of defining isolation levels based on data anomalies and makes the cognition of data anomalies and isolation levels more concise.