Quality Capabilities

The 6 Dimensions of Data Quality

Data Quality Sense evaluates your data across 6 distinct quality dimensions (capabilities). Each dimension focuses on a different aspect of data quality and produces independent scores that roll up into an overall quality rating.

Completeness

Measures whether fields contain values. Detects null, blank, and missing data across your selected fields. Learn more →

Validity

Checks whether values conform to expected formats, ranges, and patterns. Supports picklist validation and regex matching. Learn more →

Uniqueness

Identifies duplicate values across records. Flags fields where unique values are expected but duplicates exist. Learn more →

Timeliness

Evaluates whether data is current and up-to-date. Measures freshness based on configurable time windows. Learn more →

Consistency

Checks logical consistency between related fields. Detects contradictions like a closed date before an open date. Learn more →

PII Detection

Scans for personally identifiable information in free-text fields. Helps with data privacy compliance. Learn more →

How Scoring Works

Each capability produces a score from 0 to 100 for every scanned field:

• 100 — Perfect quality for this dimension
• 75–99 — Good, with minor issues
• 50–74 — Moderate quality, attention recommended
• 0–49 — Poor quality, action required
• 0 — No data to measure (e.g., all fields empty)

Scores are aggregated:

1. Field Score — Individual field result per capability
2. Dimension Score — Average across all fields for one capability
3. Definition Score — Weighted average across all dimensions

Capability Applicability

Not all capabilities apply to all field types. DQS automatically handles non-applicable combinations:

Field Type	Completeness	Validity	Uniqueness	Timeliness	Consistency	PII Detection
Text	✓	✓	✓	—	✓	✓
Number	✓	✓	✓	—	✓	—
Date	✓	✓	—	✓	✓	—
Picklist	✓	✓	—	—	✓	—
Boolean	✓	—	—	—	✓	—
Email	✓	✓	✓	—	—	✓
Phone	✓	✓	✓	—	—	✓