Accelerate your AI readiness with data quality governance
Achieve compliant, high-value AI adoption with our best-in-class data quality solution, robust governance, and proven strategies that boost ML model accuracy and ROI.
AI readiness: Are these bottlenecks holding you back?
Lack of AI-ready data
Automate large scale DQ monitoring, anomaly detection, data profiling and more. Simplify issue discovery and remediation before they impact AI initiatives. Improve quality of data across the enterprise for better outcomes, and free up valuable data scientist and engineering time.
Siloed data governance failing AI governance
Gain visibility and control over the AI data landscape. Catalog and document all of your critical data assets, data products, and reports in one place. Filter and find these assets through full-text search and powerful filtering capabilities.
Data drift and model degradation
Track changes in underlying data flows effortlessly with automated data lineage, continuous DQ checks and anomaly detection, and be alerted of potential model degradation before it happens.
End-to-end data quality
for ML model development
Speed up data collection
Publish trusted data sets to a central data catalog and put data quality checks in place.
Easily find data sets, understand contents, data quality, and owners of data.
Eliminate repetitive data cleansing
Monitor critical data assets and source data. Catch and remediate issues early, and maintain baseline data quality for ML training data.
Prevent recurring issues from happening with data validation placed at entry points.
Cleanse data efficiently when necessary with a clear view of invalid records.
Catch data drift early
Implement continuous data quality checks for monitoring incoming model data.
Get alerts on DQ issues, anomalies detected by AI, freshness issues, and PII occurring where it shouldn’t.
Investigate issues with data lineage rich in metadata overlays and get to the root cause faster.
