The Case of End-To-End Data Validation

End-To-End Data Validation

Emphasizing the importance of robust data validation processes, this study highlights the need for end-to-end validation to manage increasing data volumes and mitigate data anomalies. Implement continuous monitoring and quality scoring to ensure reliable and accurate data for decision-making.  

What is inside 

  • Comprehensive Validation: Integrate data governance and quality assurance for end-to-end validation.  
  • Business-Driven Data Management: Shift towards a proactive approach to data management.  
  • Continuous Monitoring: Detect and address data issues early with continuous monitoring and quality scoring.  

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Data Quality

Automate testing of Business Intelligence applications by making use of the metadata available from the BI tools such as Tableau, OBIEE, and Business Objects.

Synthetic Data

Automate testing of Business Intelligence applications by making use of the metadata available from the BI tools such as Tableau, OBIEE, and Business Objects.

ETL Testing

Automate testing of Business Intelligence applications by making use of the metadata available from the BI tools such as Tableau, OBIEE, and Business Objects.

BI Validation

Automate testing of Business Intelligence applications by making use of the metadata available from the BI tools such as Tableau, OBIEE, and Business Objects.
Products

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DataOps Suite

End-to-End Data Testing Automation

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ETL Validator

Automate your Data Reconciliation & ETL/ELT testing

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BI Validator

Automate functional regression & performance testing of BI reports

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DQ Monitor

Monitor quality of data being Ingested or at rest using DQ rules & AI

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Test Data Manager

Maintain data privacy by generating realistic synthetic data using AI

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