Although these aspects are closely interrelated within a system, addressing and improving them independently results in better performance of the system as a whole (Arens, Chee, Hsu Knoblock, 1993). Data Governance Data governance encompasses a set of procedures ensuring that important assets in data form are properly managed in all divisions of an enterprise (Abiteboul, Benjelloun Milo, 2002). The processes ensure that data is trustworthy and employees can be held accountable for any harmful occurrences that take place due to low quality of data. The organization under review was found not to have effective control measures as to who accessed data and in what way. There were many cases of deleted or edited information by users other than the authorized owners. One way to improve data governance is by defining the governance process (Arens, Chee, Hsu Knoblock, 1993). The organization needs to make data auditable and enable its accountability. This facilitates accurate monitoring and effective reconciliation between the data source and its consumers. The process needs to cover initial input of data, its standardization and refinement along the whole information flow path (Resnik Yarowsky, 2000). … Accountability entails the creating and empowering governance roles in the company. The organization should create the roles at appropriate levels and assign them to dedicated owners and stewards (Arens, Chee, Hsu Knoblock, 1993). With outsourcing of data governance and processing becoming a reality for most organizations, the responsibility hierarchy should be accountable to the owners of data. For example, an outsourcing agent will be held responsible by an organization for breach of governance committed by the outsourced service provider (Resnik Yarowsky, 2000). Data Quality Data quality may be termed as the state of timeliness, validity, completeness, accuracy and consistency that render data suitable for the intended use (Abiteboul, Benjelloun Milo, 2002). A challenge faced by the organization under review is detecting issues caused by the quality of data at the point of entry into the system from the numerous users. They are mostly typing errors as well as intentional decisions. Recommendations to improve data quality include use of simple queries or profiling tools (Resnik Yarowsky, 2000). Others are tracking mail deliverability, verification of information prior to database entry and understanding contents of organizational data and the way it got there. Email and other personalized communications form a considerable percentage of the marketing tools. Therefore, given their high traffic, they can be accurate indicators of data quality. By implementing a process that tracks bounced and returned emails, an organization is able to monitor its data accuracy and make applicable changes (Resnik Yarowsky, 2000).