What is the role of data profiling in data quality management?

• A. It replaces data cleansing entirely.
• B. It is used to understand data characteristics to identify quality issues; supports remediation.
• C. It is optional and rarely used.
• D. It only measures data volume.

Answer: (B)

Data profiling helps a data quality program by inspecting data to reveal its structure, content, and relationships. It analyzes attributes such as distributions, frequencies, patterns, data types, null values, duplicates, outliers, and referential integrity. This diagnostic view shows where data fails to meet business rules or quality standards and helps prioritize which issues to fix. Profiling does not replace cleansing; it informs and guides cleansing, standardization, enrichment, and deduplication efforts. It is not optional in mature data quality programs, and it looks beyond data volume to evaluate completeness, validity, accuracy, consistency, timeliness, and uniqueness. In short, it’s about understanding data characteristics to identify quality issues and support remediation.