DataOps Suite for Healthcare – ROI Whitepaper

DataOps Suite for Healthcare

The healthcare sector faces significant data quality challenges due to evolving data definitions, dynamic business rules, and complex interconnections. Discover how DatagapsDataOps Suite addresses these challenges, leading to substantial improvements in data quality and operational efficiency.  

What’s inside 

  • Changing and Moving Data Definitions: Address evolving business contexts and metric definitions.  
  • Constantly Updating Business Rules: Ensure data integrity with continuous transformation validation.  
  • Interconnected Datasets: Manage interdependencies among various data sources.  
  • Vendor Standardization: Overcome inconsistent data capture, storage, and transfer methods.  
  • AI Modelling: Maintain accurate data for successful machine learning and AI models.  

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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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