Build self-reliant teams with automated data quality
Validate every data value before ingesting into AI-model, automate and orchestrate data quality workflows within AI-workloads.
Simplify and scale your data quality
Visibility into the health of the entire data
- Get automated reports on data quality KPIs such as completeness, correctness, uniqueness, timeliness, validity, and accuracy without any setup.
- A no-code interface to enable users to define and manage flexible thresholds for custom data quality KPIs.
- Monitor & assess the quality of data in your pipeline by comparing it with past trends.
Business user-friendly rules and contracts
- Easy-to-use interactive no-code interface to build and manage data quality checks for your key tables.
- Advanced configuration options to set up complex rules within a single table or across multiple tables.
- Automate actions like alerts, DQ binning, circuit breaker, and remediation based on rule outcome.
With well-governed data across your organization
- Effortlessly integrate with the data catalog of choice.
- Real-time data reliability insights and alerts within data catalogs.
- REST API support to integrate into any catalog system.
Ensure teams trust your AI outcomes and not question it
- Orchestrate workflows to identify suspicious data and segregate it for review.
- Integrate with the ticketing system for review and remediation.
- Notify impacted users whenever there’s a data outage.
Connect. Analyze. Alert. Advise.
Connect Datasources
Connect your datasource, or send data via REST, or load a local file.
Analyze Data Health
Quickly identify and pinpoint data anomalies, errors, or inconsistencies.
Alert
Actian will learn your data and its trends and automatically alert on unexpected drifts.
Recommendations
Actian will finally advice you on next best actions for your data sets.
Discover the complete platform
Open architecture
No-code connection to data lake and lakehouse –natively supports raw formats like Iceberg, Hudi, and Delta.
Data quality
Validate every value before ingesting into AI-model, automate and orchestrate DQ workflows in AI-workloads.
Anomaly detection
No sampling, ML-driven anomaly detection on column values and business metrics.
Data layer consistency
Improve quality across bronze, silver, and gold layers embedded design patterns to stop bad data at bronze.
Data layer health
No code analysis and reporting of your data lake and lakehouse.
Incident management
Alerting, ticketing, investigation and remediation workflows.