Deliver AI-ready, quality data with automated profiling, cleansing and monitoring
Ensure your data drives decisions you can trust
High-quality data is crucial to the success of your business. With IBM’s data quality tools, you can mitigate risks, make informed decisions, and foster innovation—all while ensuring compliance and enterprise-wide trust in your data.
IBM’s data quality tools empower organizations to perform comprehensive data profiling, assess quality, identify and pinpoint issues before they impact decision-making.
Benefits
Increase trust in your data, enabling leaders to make decisions grounded in facts rather than assumptions.
Maintain compliance and industry regulations via consistent data governance and reporting capabilities.
Save time and resources by eliminating manual data correction efforts with automated workflows and accelerate the identification of data quality issues when overlayed with data lineage.
Mitigate business risks with real-time insights into data quality gaps.
Features
Harness AI-driven automation to identify and update technical metadata profiles. Generate data quality checks automatically based on detected primary-foreign key relationships and historical stability patterns. This streamlined process ensures all your data receives necessary attention, irrespective of its location, and prepares it for AI and analytics use in hybrid and multi-cloud environments.
Monitor data streams to ensure every piece of information entering your systems adheres to enterprise-wide quality standards.
Automate the detection and resolution of data inconsistencies with intelligent workflows that address the root cause of issues. Identify and resolve inconsistencies, duplicates, and errors across your data landscape using intelligent algorithms for higher accuracy.
By employing active metadata, organizations can automatically apply data quality checks with relevant business terms. This approach significantly reduces manual efforts required for data quality monitoring, while simultaneously safeguarding adherence to both business and domain-specific requirements.
Monitor data quality compliance against SLA rules to focus remediation efforts where they matter most—so your teams stay efficient without compromising the integrity of your data pipeline.
Leverage artificial intelligence to score your data quality and receive actionable insights on areas for improvement. Keep a record of your data’s quality journey over time, enabling full traceability for compliance and decision-making.
Connect with a broad range of data sources, platforms, and applications to ensure a unified data landscape.
Harness AI-driven automation to identify and update technical metadata profiles. Generate data quality checks automatically based on detected primary-foreign key relationships and historical stability patterns. This streamlined process ensures all your data receives necessary attention, irrespective of its location, and prepares it for AI and analytics use in hybrid and multi-cloud environments.
Monitor data streams to ensure every piece of information entering your systems adheres to enterprise-wide quality standards.
Automate the detection and resolution of data inconsistencies with intelligent workflows that address the root cause of issues. Identify and resolve inconsistencies, duplicates, and errors across your data landscape using intelligent algorithms for higher accuracy.
By employing active metadata, organizations can automatically apply data quality checks with relevant business terms. This approach significantly reduces manual efforts required for data quality monitoring, while simultaneously safeguarding adherence to both business and domain-specific requirements.
Monitor data quality compliance against SLA rules to focus remediation efforts where they matter most—so your teams stay efficient without compromising the integrity of your data pipeline.
Leverage artificial intelligence to score your data quality and receive actionable insights on areas for improvement. Keep a record of your data’s quality journey over time, enabling full traceability for compliance and decision-making.
Connect with a broad range of data sources, platforms, and applications to ensure a unified data landscape.
Related resources
Access the report to learn why IBM named a Leader in the 2025 Gartner® Magic Quadrant™ for Data and Analytics Governance Platforms
Auto DQ connects business needs with tech rules, aiding data providers and non-tech users in automating scalable, context-aware data quality monitoring.
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