What is Data Quality

Definition

Data quality is the measure of how well data fits its intended use, assessed across dimensions such as accuracy, completeness, consistency, timeliness, validity, and uniqueness, so organisations can trust the data driving their decisions and operations.

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Quantifies trust in data through measurable dimensions rather than vague assurances
Prevents flawed decisions caused by inaccurate, incomplete, or stale records
Reduces downstream rework by catching defects close to where data enters the system
Provides a baseline to monitor so quality regressions are detected over time

Real World Example

A telecom operator measures data quality on its customer table and discovers that 12 percent of records have invalid postal codes, prompting a source-system fix that improves delivery success for marketing campaigns.

FAQs

What are the dimensions of data quality?

Common dimensions include accuracy, completeness, consistency, timeliness, validity, and uniqueness.

How is data quality measured?

Teams define rules and metrics for each dimension and run automated checks that score datasets and flag records that fail.

How does data quality relate to data validation?

Validation is the act of checking records against rules; data quality is the broader, ongoing measure of how fit the data is for use.