Which elements are essential in governing data quality?

• A. Data quality standards, metrics, data profiling, stewardship, and remediation processes.
• B. Only data storage capacity and speed.
• C. Data quality is uncontrolled by governance.
• D. Data quality depends solely on user satisfaction.

Answer: (A)

Governing data quality requires a structured framework that defines what quality means, measures it, investigates data to find issues, assigns responsibility, and provides a plan to fix problems. Data quality standards establish the criteria data must meet, giving you a clear, shared expectation of quality. Metrics turn those standards into measurable targets, so you can track performance, compare data across sources, and see when quality improves or declines. Data profiling digs into actual data to reveal specific problems like missing values, inaccuracies, duplicates, or inconsistent formats, guiding where actions are needed. Stewardship assigns clear ownership and accountability for data assets, ensuring there is someone responsible for maintaining quality and enforcing standards. Remediation processes outline the steps to correct defects, implement fixes in data pipelines, and prevent recurrence, creating a sustainable approach to data quality. Without these elements, quality becomes inconsistent and reactive rather than managed and improved. The other options miss these essential governance components, focusing only on capacity or user satisfaction or denying the role of governance in quality.