Which practices are commonly used to ensure data quality?

• A. Data profiling and cleansing.
• B. Data encryption and archiving.
• C. Data replication and mirroring.
• D. Data minimization.

Answer: (A)

Ensuring data quality hinges on understanding the data’s quality characteristics and then improving them. Data profiling analyzes data to reveal issues such as missing values, inconsistencies, duplicates, and outliers, giving a clear picture of quality levels across datasets. Data cleansing takes the next step by correcting errors, standardizing formats, deduplicating records, and validating data against business rules, thereby improving accuracy, completeness, and consistency. Together, profiling followed by cleansing is the standard approach to maintain reliable data for reporting and analytics.

Encryption and archiving protect confidentiality and retention, not the quality of the data itself. Replication and mirroring aim to keep data available and consistent across systems, but they don’t inherently fix data quality. Data minimization focuses on privacy and reducing data collected, which doesn’t directly enhance data quality.