What are the key deliverables in data governance?

What are the key deliverables in data governance?

• A. Implementation roadmap with vision and definition of success
• B. Operational frameworks without accountability
• C. Data quality standards without monitoring
• D. Only reporting on data quality levels

Answer: (A)

The key deliverable in data governance is an implementation roadmap with a vision and a definition of success. This deliverable is essential because a well-defined roadmap lays out a clear strategy for managing data across the organization. It includes the goals of the data governance initiative, guiding principles, and ensures that all stakeholders have a shared understanding of what success looks like. Having a vision helps align the organization's data efforts with its overall objectives, while the definition of success provides measurable criteria against which the effectiveness of the governance initiatives can be evaluated. This approach promotes accountability and encourages ongoing engagement from all parties involved in data governance. In contrast, the other options lack essential elements that contribute to a robust data governance framework. Operational frameworks without accountability undermine the effectiveness of governance efforts, as they do not ensure that actions are taken or responsibilities are upheld. Data quality standards without monitoring miss the critical aspect of assessing effectiveness, which can lead to deteriorating data integrity. Lastly, simply reporting on data quality levels without a comprehensive strategy does not facilitate the necessary actions needed to drive improvements in data quality management or governance practices.

The key deliverable in data governance is an implementation roadmap with a vision and a definition of success. This deliverable is essential because a well-defined roadmap lays out a clear strategy for managing data across the organization. It includes the goals of the data governance initiative, guiding principles, and ensures that all stakeholders have a shared understanding of what success looks like.

Having a vision helps align the organization's data efforts with its overall objectives, while the definition of success provides measurable criteria against which the effectiveness of the governance initiatives can be evaluated. This approach promotes accountability and encourages ongoing engagement from all parties involved in data governance.

In contrast, the other options lack essential elements that contribute to a robust data governance framework. Operational frameworks without accountability undermine the effectiveness of governance efforts, as they do not ensure that actions are taken or responsibilities are upheld. Data quality standards without monitoring miss the critical aspect of assessing effectiveness, which can lead to deteriorating data integrity. Lastly, simply reporting on data quality levels without a comprehensive strategy does not facilitate the necessary actions needed to drive improvements in data quality management or governance practices.