Data quality dimensions
The six data quality dimensions — and how to turn them into action
Bad data is too vague to fix. Completeness, validity, consistency, uniqueness, timeliness and accuracy give business and data teams a practical way to score data health, prioritize cleanup and improve trust in reporting, MDM, analytics and AI programs.
Start here
What a data quality dimension actually measures
A data quality dimension is one specific way data can be fit or unfit for use. Instead of debating whether a dataset is “good,” teams can identify whether records are missing required fields, using invalid values, duplicated, stale, inconsistent or inaccurate.
CompletenessValidityConsistencyUniquenessTimelinessAccuracy
The six dimensions
Six ways enterprise data breaks business trust
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Completeness
Required fields are populated. In enterprise systems, missing owner, effective date, status, amount, domain or reference information can block workflows and reduce reporting trust.
✓
Validity
Values follow the required format, domain or business rule, such as email format, stage values, country codes or product hierarchies.
↔
Consistency
The same business fact agrees across systems, objects and reports, reducing conflicting versions of customer, account or product truth.
◎
Uniqueness
Each real-world customer, account, product or supplier is represented once, reducing duplicate records and split activity history.
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Timeliness
Data is current enough for the decision being made, such as active customer, product, supplier, account or reference attributes.
🎯
Accuracy
Data reflects real-world truth, such as correct customer identifiers, product attributes, supplier details, account relationships or reference values.
PDI approach
From quality dimensions to remediation priorities
PDI applies the six dimensions to business-critical objects across MDM, data warehouse, integration, analytics and cloud data environments. The goal is to convert scattered data issues into a ranked remediation backlog that business owners can act on.
Enterprise data qualityMDM data qualityData profilingStewardship workflowsAnalytics readiness
Proof point
Built on PDI’s enterprise data quality and MDM delivery experience
PDI helps enterprises modernize data platforms, improve data quality, accelerate cloud migration and operationalize AI across platforms including Informatica, Snowflake, Databricks, AWS, Azure, Google Cloud and modern analytics ecosystems.
FAQ
Common questions
What are the six data quality dimensions?
The six commonly used dimensions are completeness, validity, consistency, uniqueness, timeliness and accuracy. Together they help teams measure whether data is trustworthy and fit for business use.
How do data quality dimensions help business teams?
They translate “bad data” into specific issues that can be assigned, prioritized and remediated, such as missing fields, duplicate records, stale data or inconsistent formats.
Where should companies start?
Start with the data objects tied to business outcomes: customers, accounts, products, suppliers, opportunities, cases, policies, claims, loans or other high-value domains.
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