Data quality is a critical component of public information systems, particularly in the management of motor vehicle taxes, which constitute a major source of regional revenue. However, significant challenges persist, including incomplete or duplicated records, invalid identifiers, and limited interoperability with external databases. These issues remain despite various improvements since 2018, such as the integration of Population Identification Numbers (NIK) and the recording of taxpayer phone numbers. Although recent tools such as automated validation mechanisms and real-time quality dashboards, are available, they have not been widely implemented to address systemic deficiencies in regional taxation systems. This study seeks to assess the data quality maturity level of the motor vehicle tax system at the Regional Revenue Agency (Bapenda) of Bengkulu Province using David Loshin's Data Quality Management Maturity Model (DQM). It further aims to develop actionable recommendations grounded in the Data Management Body of Knowledge (DMBOK). Data were collected through a survey of six key respondents using a 133-indicator questionnaire, with analysis conducted across five maturity levels. The findings indicate that most domains fall within the Defined level, with the highest score in Performance Management (4.23) and the lowest in Data Quality Protocols (3.38). Recommendations include implementing automated validation via the Dukcapil API, developing internal regulations, establishing a data governance team, standardizing data formats, and integrating data quality monitoring into the operational dashboard. This study contributes to the enhancement of data governance in the public sector, particularly within motor vehicle tax administration. © 2025 IEEE.