Data integrity, value checks, format checks, and various pre-built templates or custom scripts for data validation.
The relationship between business steps that create data causing DQ issues, along with graphical representation and reporting (Next Release).
Detection of errors in manually entered data without requiring rule input and providing correct value suggestions.
Pre-built smart data controls (NLP, statistical checks, trend analysis, data distribution analysis, etc.)
Data reconciliation between 2 or more data systems.
Automatic data validation for table columns using machine learning without predefined rules (anomalies, trend breaks, distribution deviations, etc.)
Data profiling, contextualization, and comparison with historical trends.
You can easily define the DQ Rule with ready-made DQ Templates.
You can reduce operational workload and increase quality with AI Definition.
Quality Control results are scored and priority is assigned.
Data can be parametrically reduced for large datasets.
Checks can be run at any time and frequency with its own scheduler.
LDAP or its own User Management System.
Alarms are generated based on scoring results.
Supports many well-known databases.
If the tables calculated by DWH as a result of many ETLs cannot be compared with the source system and do not feel secure, the AI feature of the product can be used to compare the historical data pattern with the new data pattern..
Compare your source and target data periodically and ensure your data quality.
Quickly detects data problems due to development, migration or configuration errors
By profiling your data, you can access a lot of information about both table and column level. Get important information about your data.
