Data quality is closely related to the quality of information. Low data quality leads to inaccurate information and leads to a decrease in business performance. PT BPI as a company that serves the management of local government financial data needs to control and maintain data quality to remain good. The quality of the data produced is very important to note. This needs to be done to maintain the credibility and capability of PT BPI. This scientific writing aims to provide recommendations for improving data quality management. This scientific writing uses qualitative methods with document studies and several interview sessions. The models used in this scientific writing are Data Quality Management Maturity from Loshin and Data Management Body of Knowledge (DMBOK). The results of measuring the maturity level of data quality management at PT BPI using the D'Lhosin model shows that the organization is still at level one. This indicates that PT BPI does not yet have adequate and thorough basic knowledge about data quality management. PT BPI cannot meet the eight measurement characteristics at level two. Based on the resulting maturity measurement to reach level two, the writer made recommendations from eight unmet characteristics based on DMBOK. copy; 2022 IEEE.