Data teams are often constrained by manual rule writing and management, with limited data connectivity and a siloed view of data quality. With predictive, continuous and self-service data quality, organizations can centralize and automate data quality workflows to gain better control over their end-to-end data pipelines and streamline analytics processes across the enterprise.
Reduce complexity, bottlenecks, repetition and guesswork in data quality rule management.
Audit data and generate reports to comply with regulations including GDPR, CCAR, BCBS 239, HIPAA and more.
Continuously monitor data objects to discover violations of business rules and initiate remediation with the right data owners.
Speed up the development of new data pipelines that integrate data quality.
Securely move to new systems by cleansing data in advance of data migration projects.
Leverage high-quality, trusted data to drive strategic decisions across the enterprise.
Leverage machine learning to generate explainable and autonomous data quality rules.
Detect data drift, outliers, patterns and schema change.
Automatically understand the semantic schema to classify, label and mask sensitive data.
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Learn how to set up and use Collibra Data Quality.
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Learn why it's important to have reliable data quality for your business.
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Learn about the dimensions of data quality to better understand your data.
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