Trusted data starts with predictive, self-service data quality
Show your data some love by putting quality at the heart of your data strategy. Collibra Data Quality & Observability proactively surface quality issues in real-time and makes reliable and accurate data readily available so you can drive intelligent and informed decisions.
Auto-discovered and adaptive data quality rules
Leverage machine learning to generate explainable and autonomous data quality rules. Reduce manual rule writing and errors to increase trust in your data.
Horizontal and vertical scalability
Scan large and diverse databases, files and streaming data with Spark-based parallel processing that gives you 90%+ coverage at scale.
Proactive monitoring and anomaly detection
Continuously monitor and detect data quality issues. Automatically uncover data drift, outliers, patterns and schema changes to mitigate risks and improve decision making.
Unified scoring and personal alerts
Leverage a unified scoring system to report across all data sources. Send out personal alerts to allow users to proactively detect, escalate and remediate data quality issues.
Carry out row, column, conformity, and value checks between your source data storage and target data lake. Identify missing records and broken relationships.
Automatically understand semantic schema so that sensitive data can be classified and masked during data quality checks.
Better quality data means better decision making
Support predictive, continuous, self-service data quality to streamline the time and effort it takes to get to trusted insights.
Proactively manage data issues
Proactively manage issues across varied databases, data warehouses and data lakes with a unified, business-friendly scorecard.
Modernize data quality
Reduce complexity and drive better insights with auto-discovered and adaptive data quality rules.
Build high quality data pipelines
Continuously monitor data movement and automate data quality checks at every point in your DataOps journey.
Improve regulatory compliance
Help manage risks by ensuring your data is always complete, timely, accurate and valid.
Reduce the risk and cost of migrating data
Validate data integrity between source and target systems.
A top 10 bank reduced 60% of its manual data quality workload, and saved $1.7M+ by automating data quality management.
A leading insurance organization completed their regulatory audit in just four weeks. This had previously taken them two years.
A leading global investment bank shaved more than 25% off the time and resources needed to manage and audit data and avoided a seven-digit audit fine as a result.
Collibra: a quality act
Use predictive data quality and observability to build trust in your data. Make Collibra Data Quality & Observability a key part of your larger data strategy.
- Seizing Opportunity in Data Quality (mit.edu)
- Collibra customer benchmark
- Assessing Data Quality: A Managerial Call to Action (May 2020, HBR)