Collibra use case

Enable enterprise-scale data quality

Overview
How we can help
The Collibra advantage
Customer stories
Resources

Predictive data quality teams can trust

Collibra integrates predictive self-service data quality into your governance programs and leverages the power of machine learning to generate data quality rules that help boost productivity and compliance and put trusted data into the hands of every business user.

Watch the video

30% Percentage of revenue lost by U.S. businesses because of bad data.¹
19% Percentage of customers lost by companies because of poor data quality.²
47% Percentage of recent data records that have at least one critical or work-impacting error.³

Connect and empower your people with trusted data

Identify and remediate data quality issues quickly

Detect data quality issues and address them before they become a problem. Give your data teams the visibility they need to stay ahead of potential quality issues.

Build high-quality data pipelines and data products

Bring teams together to govern your data pipelines and products. Install quality checks at every step of your DataOps journey.

Build trust in your data

Focus your data quality efforts on business-critical information, metrics and KPIs. Translate data governance standards into data quality rules to ensure your business gets complete, consistent and accurate information.

Maintain compliance at scale

Review and activate data policies and quality rules. Continuously monitor data for completeness, timeliness, accuracy, and validity to ensure compliance across the organization.

Reduce the risks and costs of migrating data

Cleanse and govern data before any migration project. Transition seamlessly into post-migration with quality data you can use and rely on out of the gate.

The Collibra advantage

Foundational metadata management

Capture, reconcile and manage metadata across your enterprise via one platform. Quickly connect teams to improve data quality and usability.

ML-enabled data quality

Reduce manual rule writing and maintenance efforts by 50-70%. Automatically classify physical data and add business context at scale.

Proactive monitoring and anomaly detection

Automatically uncover data drift, outliers, patterns and schema changes to mitigate risks and improve business decisions.

Intuitive, configurable workflows

Initiate remediation workflows with the right data owners when data quality scores drop, to quickly resolve issues.

End-to-end, automated data lineage

Trace data movement across the lifecycle. Help data quality teams narrow the focus of root cause investigations and prioritize issues.

Horizontal and vertical scalability

Scale data quality across large and diverse databases, files and streaming data as your business grows. Provide a single pane of glass for data quality across all data sources.