Data stewardship is the process of defining, implementing, and enforcing policies, standards, roles, and responsibilities for the quality, security, privacy, and usage of data within an enterprise1. Data stewardship has the greatest influence on data quality assurance, as it ensures that the data is accurate, complete, consistent, timely, and fit for its intended purpose1. Data stewardship also helps to identify and resolve data quality issues, monitor and measure data quality performance, and improve data quality over time1. The other options are not as influential as data stewardship, as they are specific aspects or techniques of data management, but not comprehensive processes. Data encryption is the process of transforming data into an unreadable format to protect it from unauthorized access or modification2. Data encryption can enhance data security and privacy, but it does not directly affect data quality assurance. Data classification is the process of categorizing data based on its value, sensitivity, and risk to the enterprise. Data classification can help to apply appropriate controls and policies for data protection and compliance, but it does not directly affect data quality assurance. Data modeling is the process of creating a representation of the structure, relationships, and meaning of data within a specific domain or context. Data modeling can help to design and optimize databases and applications that use data, but it does not directly affect data quality assurance.