Are you getting the best out of your data and AI?
Expected growth rate of AI between 2023 and 2030
of execs see AI as the main business advantage
of EBIT generated from AI by high-performers in AI
To be exceptional in AI, you need to be exceptional in data quality
End-to-end data quality
for ML model development
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.
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.
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.
See it in action
Deploy more models that work as you intended
20-50% moremodel deployments per year
20-50% lesstime to resolve issues
and data drift
1-5% bettermodel precision
Work with the lead innovator in data quality
Unified platform for data discovery and quality
Document, discover, and assess the quality of the data you need in one tool via connected and efficient workflows.
Scalable data quality
Onboard new data sources faster with AI-based data classification and quality checks assigned automatically. Reuse data quality rules and never code repetitive checks again.
Other vendors are transactional in their behavior. With Ataccama, there’s a genuine belief of shared responsibility of success that we feel within T-Mobile.Daniel West Data Management Lead, T-Mobile