Data quality for governed AI & model training
Trust your data so you can trust your AI. Ensure the data feeding your models and agents is reliable, governed, and AI-ready.
Are you getting the most
out of your data and AI?
Expectation
Reality
-
AI models and agents producing unreliable outputs
-
No governance for the data feeding your AI
-
Data scientists spending more time cleansing than building
-
Data drift silently degrading model performance
-
Repetitive, manual data prep slowing every AI initiative
To be exceptional in AI, you need to be exceptional in data quality
See the end-to-end data quality process for ML model development
Speed up data collection
Publish trusted, AI-ready datasets to a central data catalog with quality checks already in place.
Easily discover datasets, understand their contents and quality, and identify data owners.
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, AI-detected anomalies, freshness problems, and PII exposure risks. Investigate with unified lineage rich in metadata overlays and get to root cause faster.
See it in action
-
Ataccama Data Quality for governed AI & model training
Why Ataccama for governed AI
Unified platform for data discovery and quality
Unified data trust for AI. Discover, assess, and trust the data feeding your models and agents in one platform via connected and efficient workflows.
Scale with the
ONE AI Agent
Your digital data steward auto-generates DQ rules, detects anomalies, and recommends fixes. Write rules once and apply them anywhere across your data estate.
What our customers say
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.
Ataccama
features Learn more about our capabilities for data quality for governed AI
See the platform in action
Get a personalized walkthrough from our experts to discover how Ataccama can help you.
Tailored for your industry
Learn how businesses in your industry are leveraging Ataccama for success.
ROI calculator
Estimate the potential ROI of Ataccama with our calculator. Prove the value of your data in minutes.
FAQ
AI models and agents are only as reliable as the data they are trained on and consume. Poor data quality leads to unreliable outputs, data drift that silently degrades model performance, and data scientists spending more time cleansing than building. Organizations that invest in governed data quality for AI see more models deployed successfully and faster time to production.
Ataccama provides a unified data trust platform where teams can discover, assess, and validate the data feeding their models and agents in one place. The platform publishes trusted, AI-ready datasets to a central data catalog with quality checks already in place, so data scientists can easily find datasets, understand their contents and quality, and identify data owners.
The ONE AI Agent acts as a digital data steward that auto-generates data quality rules, detects anomalies, and recommends fixes across your data estate. It enables a write-once, apply-anywhere approach to quality rules, scaling data trust across all AI workloads without requiring teams to manually maintain separate quality processes for each model or pipeline.
Ataccama covers the full AI data lifecycle: data collection with trusted datasets published to a central catalog, data cleansing with automated profiling and standardization, and deployment monitoring with continuous quality checks. This end-to-end approach ensures data quality is maintained not just at training time but throughout the model's production lifecycle.
Poor data quality causes AI models to produce unreliable outputs, introduces bias that leads to non-compliant decisions, and creates data drift that silently degrades model accuracy over time. Without governance for the data feeding AI, organizations face repetitive manual data preparation that slows every initiative and prevents data scientists from focusing on high-value model development.
Organizations that invest in data quality for AI see higher model reliability, faster deployment cycles, and reduced risk of non-compliant AI decisions. By eliminating bottlenecks in data preparation and freeing up data scientists' time, teams can deploy more models, ensure effective collaboration with data engineering, and scale AI initiatives with confidence.