Data Quality: Dimensions, Rules, and Constraints

As the big data industry matures, data quality has become an unavoidable topic; the article begins by defining data quality as a set of evaluation rule dimensions that provide a way to measure and manage information and data.

Distinguishing rule dimensions helps align them with business needs, prioritize evaluation order, clarify what can be obtained from each assessment, and better schedule project actions when resources are limited.

The main rule dimensions are completeness, uniqueness, validity, consistency, accuracy, timeliness, and credibility, each describing a different aspect of data integrity.

Completeness includes sub‑dimensions such as non‑null constraints, ensuring required fields are populated.

Uniqueness covers unique constraints that prevent duplicate records, typically enforced via primary or composite keys.

Validity comprises code‑value domain constraints, length constraints, content‑format constraints, and range constraints, each checking that data conforms to predefined standards.

Consistency is broken down into equality dependencies, existence dependencies, and logical dependencies, which enforce relational and logical rules across datasets.

Accuracy assesses whether data values truly reflect the real‑world entities they represent, often requiring manual verification.

Timeliness measures the delay between a business event and its correct storage and availability in the system.

Credibility evaluates whether data growth patterns follow expected trends, flagging anomalies that may indicate synchronization issues.

The article emphasizes that improving data quality is incremental: start with the most critical dimensions, apply easy‑to‑implement checks, and progressively address more complex rules to achieve comprehensive data governance.