While data governance initiatives may be driven by a desire to improve data quality, they are more often driven by external regulations. In a recent report by the WaterCooler CIO community, 54% said process efficiency was the key factor; 39% - regulatory requirements; and only 7% customer service.[8]​ Examples of these regulations include the Sarbanes-Oxley Act, Basel I, Basel II, HIPAA, GDPR, cGMP,[9]​ and a host of data privacy regulations. To achieve compliance with these regulations, business processes and controls require formal management processes.[10]​ Successful programs identify significant drivers for both executive and supervisory leadership.

Common themes among external regulations center on the need to manage risk. Risks can be financial errors, the inadvertent release of sensitive data, or poor quality data for key decisions. Methods for managing these risks vary from industry to industry. Examples of best practices and commonly referenced guidelines include COBIT, ISO/IEC 38500, and others. The proliferation of regulations and standards creates challenges for data governance professionals, particularly when multiple regulations overlap the data being managed. Organizations often launch data governance initiatives to address these challenges.

Data governance initiatives improve data quality by assigning a team responsible for the accuracy, integrity, consistency, timeliness, validity, and uniqueness of the data.[11] This team typically consists of executive leadership, project management, line-of-business managers, and data stewards. The team typically employs some type of methodology for tracking and improving enterprise data, such as Six Sigma, and tools for data mapping, profiling, cleansing, and data monitoring.

Data governance initiatives can aim to achieve a number of objectives including providing better visibility to internal and external customers (such as supply chain management), compliance with regulatory law, improving operations after rapid company growth or corporate mergers, or helping the efficiency of enterprise knowledge workers by reducing confusion and error, and increasing their knowledge reach. Many data governance initiatives are also inspired by previous attempts to fix information quality at the departmental level, leading to incongruent and redundant data quality processes.

Most large companies have many applications and databases that cannot easily share information. Therefore, knowledge workers within large organizations often do not have access to the data they need to do their jobs better. When they do have access to the data, the quality of the data may be poor. By establishing a data governance practice or corporate data authority, these issues can be mitigated.

The structure of a data governance initiative will vary not only with the size of the organization, but also with the desired objectives.[12]​.

The implementation of a data governance initiative can vary in both scope and origin. Sometimes an executive mandate will emerge to initiate a company-wide effort, sometimes the mandate will be to create a pilot project or projects, limited in scope and objectives, aimed at solving existing problems or demonstrating value. Sometimes an initiative will originate lower in the organization's hierarchy and be implemented in a limited scope to demonstrate value to potential sponsors higher up in the organization. The initial scope of an implementation can also vary greatly, from the overhaul of a single IT system to a cross-organizational initiative.

While data governance initiatives may be driven by a desire to improve data quality, they are more often driven by external regulations. In a recent report by the WaterCooler CIO community, 54% said process efficiency was the key factor; 39% - regulatory requirements; and only 7% customer service.[8]​ Examples of these regulations include the Sarbanes-Oxley Act, Basel I, Basel II, HIPAA, GDPR, cGMP,[9]​ and a host of data privacy regulations. To achieve compliance with these regulations, business processes and controls require formal management processes.[10]​ Successful programs identify significant drivers for both executive and supervisory leadership.

Common themes among external regulations center on the need to manage risk. Risks can be financial errors, the inadvertent release of sensitive data, or poor quality data for key decisions. Methods for managing these risks vary from industry to industry. Examples of best practices and commonly referenced guidelines include COBIT, ISO/IEC 38500, and others. The proliferation of regulations and standards creates challenges for data governance professionals, particularly when multiple regulations overlap the data being managed. Organizations often launch data governance initiatives to address these challenges.

Data governance initiatives improve data quality by assigning a team responsible for the accuracy, integrity, consistency, timeliness, validity, and uniqueness of the data.[11] This team typically consists of executive leadership, project management, line-of-business managers, and data stewards. The team typically employs some type of methodology for tracking and improving enterprise data, such as Six Sigma, and tools for data mapping, profiling, cleansing, and data monitoring.

Data governance initiatives can aim to achieve a number of objectives including providing better visibility to internal and external customers (such as supply chain management), compliance with regulatory law, improving operations after rapid company growth or corporate mergers, or helping the efficiency of enterprise knowledge workers by reducing confusion and error, and increasing their knowledge reach. Many data governance initiatives are also inspired by previous attempts to fix information quality at the departmental level, leading to incongruent and redundant data quality processes.

Most large companies have many applications and databases that cannot easily share information. Therefore, knowledge workers within large organizations often do not have access to the data they need to do their jobs better. When they do have access to the data, the quality of the data may be poor. By establishing a data governance practice or corporate data authority, these issues can be mitigated.

The structure of a data governance initiative will vary not only with the size of the organization, but also with the desired objectives.[12]​.

The implementation of a data governance initiative can vary in both scope and origin. Sometimes an executive mandate will emerge to initiate a company-wide effort, sometimes the mandate will be to create a pilot project or projects, limited in scope and objectives, aimed at solving existing problems or demonstrating value. Sometimes an initiative will originate lower in the organization's hierarchy and be implemented in a limited scope to demonstrate value to potential sponsors higher up in the organization. The initial scope of an implementation can also vary greatly, from the overhaul of a single IT system to a cross-organizational initiative.