If you have any questions, would like to request training for yourself or your team, or believe that you've identified a Data Governance, Data Quality or Data Stewardship opportunity at Stanford, please don't hesitate to contact us.Matt Hoying
Data Governance (DG) is a cross-functional set of roles, policies and enabling technologies that work together to ensure that an organization is getting the maximum net benefit out of its data assets. To be both successful and sustainable, a DG program must be integrated with business and IT processes throughout the organization.
The fundamental purpose of all DG programs is to improve the effectiveness and efficiency of business processes. Critical business processes are especially sensitive to the quality of data, and the failure of such processes may have far-reaching impacts. Quality data that is “fit for use” across the organization can only be developed and maintained through the collaboration of a diverse set of data stakeholders. This group must commit to formalized responsibilities, policies and procedures around the effective management of data. All DG procedures, including data quality remediation and master data standardization, are most efficient and effective when they are understood and performed consistently throughout the institution.
To achieve institution-wide commitment, the strategy and structure of each DG program must be designed with the unique priorities, competencies and goals of that organization in mind. This may lead to vastly different DG implementations, even among similar organizations. Additionally, as a DG program grows and technical and business environments change, its strategy and structure must continually evolve to remain effective.
High-quality data is a critical success factor across all functions of any organization. Proactive data management and a well-defined Data Governance program are required for the full value of this institutional asset to be realized.