Which approach best describes data quality assessment in an enterprise CX program?

• A. Ignore data quality until end.
• B. Define quality rules, profiling, anomaly detection, and remediation processes across data sources.
• C. Assess data quality only after dashboards are built.
• D. Assume data quality is uniform across all sources.

Answer: (B)

In enterprise CX, data quality must be managed proactively across every data source. Defining quality rules establishes explicit standards for accuracy, completeness, timeliness, consistency, and validity. Data profiling then reveals how the current data stacks up against those standards, while anomaly detection spots unusual spikes or drops that signal issues. Remediation processes create a clear path to fix problems and prevent them from happening again, and doing this across all data sources keeps data aligned as it flows through analytics, dashboards, and AI initiatives. This holistic, cross-source approach is essential because CX analytics rely on data from many systems—CRM, web and mobile analytics, contact centers, surveys, and more—and poor quality in any one source can distort insights and undermine decisions. By building a proactive data quality program, you gain trustworthy data, better governance, and more reliable CX dashboards and ML outputs. Waiting to address quality until the end, or assuming all sources are equally good, or trying to assess quality only after dashboards are built, leads to hidden issues and brittle insights.