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.
Trend and Anomaly Detection: Identify potential risks in business processes by detecting anomalous behaviour and trend changes in data sets.
Source and Target System Data Comparison: Increase your data accuracy by identifying inconsistencies, omissions or errors between source and target systems during data integration.
Automatically Check Your Data with AI: Quickly detect errors and inconsistencies by automatically checking your data with AI-powered algorithms.
Data Validation and Compliance Check: Check the accuracy and validity of the data, as well as guarantee compliance with the company's internal policies or external regulations (e.g. GDPR, KVKK).
