According to Gartner, the average financial impact of poor data quality on organizations is $8.2 million per year.
Business Data Validation is an integration validation tool that enables business data validation across integration channels to ensure data compliance. It helps achieve data quality using a wide variety of sources and platforms. The platform’s integration validation and machine learning capabilities make it a comprehensive data validation solution that delivers accurate and complete data for advanced analytics projects.
The Business Data Validation platform is:
- Powered by advanced machine learning algorithms
- Flexible, configurable and easily deployed
- Delivered either via cloud, on-premise or in a hybrid manner
How it Works
The platform provides you with templates to speed up data validation and streamline the overall integration process. It also allows you to select relevant templates from its library, as well as custom files from any data source. When you provide a sample file, Business Data Validation reconfigures itself to the particular file requirements. Next, it compares data from the channel with the data quality requirements, and the built-in data listener displays the data validity and integrity scores. Data is then passed from one entity to another through an automated integration channel.
The platform also allows users to write test case scenarios using a simple English editor. Its built-in AI capabilities also make suggestions for defining test case scenarios better.
Most integration solutions only ensure data integration without validation – Business Data Validation bridges the gap between these two concepts.
- Improves Data Quality and Integrity across all integration channels
- Provides out of the box templates for prompt data integration
- Provides AI-Driven recommendations so Business Users can create validation layers for a stronger Data Compliance mechanism
- Automates data quality validation using machine learning and customized test scenarios
- Improves production and efficiency by intuitively identifying and fixing data quality errors
- Decreases re-runs
- Proactively decreases costs in business to business (B2B) transactions