Data quality profiling in Foundry involves?

• A. Log data size.
• B. Set up dashboards with no profiling.
• C. Run profiling to gather statistics on distributions, completeness, uniqueness, and anomalies; use quality rules to enforce.
• D. Validate data using external audits only.

Answer: (C)

In Foundry, data quality profiling centers on running profiling to gather statistics about the data—distributions, completeness, uniqueness, and anomalies—and then using quality rules to enforce expectations. Profiling examines the actual data to reveal how values are spread, how much is missing, how many duplicates exist, and any unusual or inconsistent patterns. Quality rules translate these insights into enforceable checks, such as non-null constraints, valid value ranges, allowed domains, or referential integrity, and they trigger governance actions when data falls outside accepted thresholds. This combination lets you quantify quality, monitor it over time, and prevent problematic data from moving through pipelines. Logging only data size misses the depth of insight; dashboards without profiling don’t measure quality; external audits alone don’t provide automated, ongoing governance within the platform.