Define data rules using an easy to use web interface and share the results with your business users.
Automates the testing of data at Rest or in Motion thus by empowering business users, Data Stewards and Data Owners.
Categorizes data checks into data quality dimensions such as Data Accuracy, Consistency, Unicity, Conformity, Completeness.
Computes Data Quality score for data assets and displays trend reports on a Data Quality Dashboard.
Profiles data assets and compiles statistics from the past. Predicts expected values and detects deviations ahead of time.
Crawls data sources for metadata information about Tables, Columns, and changes to them over time.
Data rules can be defined centrally by business users and applied automatically to data elements in multiple data sources.
AI enabled detection of Sematic Data Types classifies data (PII, PHI) and applies data type-specific quality rules.
A dictionary of enterprise data elements and their definitions that can be mapped to physical tables and datasets.
Validate the list of values in categorical data elements with the reference data to ensure that the values are as expected.
Check for data integrity by matching data from various sources to ensure that data is consistent across systems.
Displays a trend of Data Quality Scores for the enterprise. Users can drill down to review the scores at Data Model, Table, Data Element and Data Quality Dimension levels. Data Quality Score is automatically computed based on the Data Quality Rules for data at rest as data in motion (ETL).
By automating the testing of data being ingested in your Data Lake and Data Integration projects, DataOps Data Quality enables Continuous Integration.
We support your data source in whichever form it is. You think of any kind of data source – whether it is a relational, NoSQL, cloud, or file data source – we support most of them.
Support data quality checks for the following DQ dimensions
Add value to your Data Analytics projects and save money.