Life Sciences

Life Sciences

  • Can ETL Validator help compare data from multiple sources?
  • Does ETL Validator support Continuous Integration?
  • Is there any way to schedule tests and receive email notification?
  • Is there reporting available for Test Runs?
  • What is File Watcher?
  • What if my data source is not supported by ETL Validator?
  • Is there a free trial available for ETL Validator?
  • What is a repository and workschema? what databases are supported as repository?
  • What are the Architectural components of ETL Validator?
  • What are the System Requirements for doing a pilot?
  • Can ETL Validator help compare data from multiple sources?
  • Does ETL Validator support Continuous Integration?
  • Is there any way to schedule tests and receive email notification?
  • Is there reporting available for Test Runs?
  • What is File Watcher?
  • What if my data source is not supported by ETL Validator?
  • Is there a free trial available for ETL Validator?
  • What is a repository and workschema? what databases are supported as repository?
  • What are the Architectural components of ETL Validator?
  • What are the System Requirements for doing a pilot?

Life Sciences Solutions

In the intricate realm of life sciences, the accurate and seamless flow of data is not just a necessity – it’s a lifeline. As the life science industry continues to advance, organizations are increasingly relying on innovative data testing tools to ensure the integrity of their data. Leveraging the success of healthcare data management, Extract, Transform, Load (ETL), Business Intelligence (BI) testing, and reference data testing emerge as transformative approaches for life science organizations, providing a foundation for precise data management and operational excellence.

ETL in Life Science

Efficient Data Processing: ETL tools stand as the backbone of effective life science data management, seamlessly extracting, transforming, and loading data from various sources. This facilitates a standardized format, enabling organizations to effortlessly collect and consolidate a wealth of information, from research data to compliance records.

Benefits of ETL in Life Science

Data Integration: Break down silos by integrating disparate data sources, providing a comprehensive view of research findings, clinical trials, and regulatory compliance.

Data Quality Assurance: Ensure the accuracy and reliability of life science data through robust data cleansing and validation processes, reducing the risk of errors in critical information.

Smooth Data Migration: Navigate transitions to new systems with ease by employing ETL tools for efficient data migration, minimizing disruptions, and ensuring continuity in research and development processes

Insights through BI Testing

Critical BI Layer: Business Intelligence (BI) tools play a pivotal role in transforming raw data into actionable insights in the life science industry. BI testing ensures the accuracy, reliability, and performance of these tools, empowering professionals to make informed decisions based on trustworthy data.

Key Aspects of BI Testing in Life Science

Dashboard Validation: Guarantee the accuracy and functionality of life science dashboards, providing real-time, relevant information to researchers, clinicians, and regulatory professionals.

Data Visualization Assurance: Confirm that BI tools convey complex information through clear and accurate visualizations, contributing to effective data-driven decision-making in research and development.

Performance Testing: Validate that BI tools can handle the scale of life science data without compromising speed or functionality, ensuring seamless operations in data-intensive research environments.

Elevating Operations with Reference Data Testing:

Standardized Framework: Reference data forms the backbone of coding and categorizing information in life science operations. Rigorous testing of reference data ensures consistency across systems, enhancing interoperability and compliance.

In the ever-evolving landscape of life sciences, integrating robust data testing tools is paramount for collecting, unifying, and leveraging vast amounts of data. ETL and BI testing, coupled with meticulous reference data testing, empower life science organizations to enhance research outcomes, gain deeper insights, and modernize their operations. By embracing these technologies, life science professionals can confidently navigate the complexities of data management, improving the quality and efficiency of research and development processes. Elevate your life science data management with cutting-edge testing solutions – the key to unlocking the full potential of your data.

Success Stories

1. Tableau Data Validation for a Pharma Giant

A US pharma company used BI Validator to automate regression testing for 11,000 Tableau users across six environments. This streamlined the process, ensured accurate testing, and built trust in report metrics.

Download the case study

Trusted Data Summit

If you missed the Trusted Data Summit event, you can still access the videos below to learn from the sessions about transforming data management. 

Signup for a free trial of ETL Validator

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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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