Collibra use case

Data reliability

Monitor, validate and certify the health of your data with automated checks for accurate, consistent and reliable business outcomes

Ensure data quality across multiple sources with automated and targeted rule writing. Remediate faster with no-code, business-ready data quality dimensions and self-service rules for accurate and consistent data products. Visualize data health and certify data for trusted business decisions.


Average annual cost of poor data quality cost to organizations1


Organizations adopting data quality solutions through 20242


Those responsible for data who fully trust it3

Key initiatives

Certified and accurate business reporting

Data quality monitoring and detection

Reduce data downtime and code-heavy processes with automated rule writing over AI’s broad and large use of data.

Only Collibra automatically detects anomalies and generates business-ready rules for fast, targeted data remediation.

Data and analytics governance

Data quality validation and testing

Understand the quality of data, easily define what healthy data looks like and see changes that impact your work.

Only Collibra enables the business with clear metrics and self-service rules in natural language for no-code, tailored data quality dimensions.

 Data compliance and risk management

Data quality reporting and auditing

Understand data health and prioritize columns with greatest impact to improve ROI and compliance.

Only Collibra visualizes data health completely with automated reports for cost, scale, coverage and completeness.

Customer stories

Learn more

1How to Improve Your Data Quality, Gartner, July 2021

2Gartner Identifies 12 Actions to Improve Data Quality, Gartner, December 2022

3IDC PlanScape: Data Quality Management, doc #US51397423, December 2023