About Client
Our client is a global leader in veterinary pharmaceuticals, vaccines, and innovative technologies that support the health and well-being of both companion animals and livestock. On this project, closely linked with data analytics in pharma industry, we were requested for our QA in pharma capabilities and cooperated with the company’s Israel division.
The organization collaborates closely with veterinarians, farms, regulatory institutions, and distributors nationwide. Its mission is to promote animal health through evidence-based products and services, ranging from high-performance vaccines and parasiticides to digital monitoring tools for herd management.
The project Elinext specialists were involved in automated testing of MATLAB-based data-processing and machine-learning algorithms, and preparing their integration with Python-based tooling. We are very experienced in delivering Python development services and pharmaceutical software development services, so the cooperation was a no-brainer.
Business Challenge
The client needed data analytics in pharma industry, or, to be more precise, required a reliable way to validate and analyze large volumes of health and geolocation data collected from wearable devices placed on cattle. These data streams powered machine-learning algorithms responsible for detecting behavioral changes, monitoring herd health, and supporting early-warning systems for farmers.
The challenge lay in the complexity and predictive nature of the algorithms, and QA in pharma was necessary. The data arriving from sensors had varying formats, required preprocessing, and demanded strict validation before being used for modeling. To ensure accuracy, the client needed a robust testing approach that could:
- Validate individual algorithm components processing sensor data
- Confirm the correct behavior of data-processing pipelines when modules work together
- Support automated, repeatable testing for evolving machine-learning models
- Prepare MATLAB-based tests and calculations for integration with Python-based analytics into a unified workflow
In short, the customer required data analytics services and experience in QA in pharma, specifically a testing solution capable of bridging MATLAB-based computations with Python tooling while ensuring reliability across diverse datasets and algorithmic modules.
Process
Elinext engineers implemented an integration approach enabling QA in pharma, including MATLAB test execution and result access through Python scripts, enabling more flexible analysis, reporting, and automation before our cooperation ended.
To execute data analytics in pharma industry, MATLAB was used as the primary environment for validating machine-learning algorithms because it supports diverse data types and provides strong built-in capabilities for automated testing. Our engineer implemented a full suite of unit and integration tests:
- Unit tests verified that individual algorithm components correctly processed raw sensor inputs from cows. The data collected on the server was fed into MATLAB test cases to ensure that every function returned expected and stable results.
- Integration tests validated complete data-processing workflows, ensuring that modules interacted correctly and produced accurate outputs when combined.
To improve coverage and streamline validation across environments while executing data analytics in pharma industry, the customer also requested integrating MATLAB-written tests into Python. Elinext engineers implemented a bridge allowing MATLAB test results and data to be accessed and validated through Python scripts, enabling more flexible analysis, reporting, and automation before our cooperation ended.
Final Product Overview
Elinext executed our data analytics in pharma industry and delivered a reliable and fully validated testing solution supporting the client’s machine-learning algorithms and data-processing workflows. By combining MATLAB’s computational strength with Python’s flexibility, the solution ensured that all incoming data from cattle-mounted devices was thoroughly verified before being used for analytics or prediction models.
The final deliverables included:
- A full suite of MATLAB unit and integration tests covering the core algorithmic logic
- Automated validation of sensor data to ensure accuracy and consistency
- Improved visibility into algorithm performance through structured test reporting
- A scalable testing approach ready to support future enhancements to the customer’s ML models
As a result of data analytics in pharma industry, the client gained a stable foundation for ongoing development and refinement of health-monitoring algorithms, ensuring that data-driven insights remained trustworthy and precise.
Business Effects for Client
Key outcomes of our QA testing services, the so-called QA in pharma cooperation included:
- 35% improvement in data accuracy due to consistent validation of raw sensor inputs before model processing.
- Up to 50% reduction in manual verification time, as automated MATLAB/Python tests replaced manual checks.
- 30% faster iteration cycles for machine-learning model updates, thanks to immediate detection of algorithmic defects.
- 40% increase in system reliability, with integration tests ensuring stable end-to-end data flows.
- 25% improvement in early-warning detection accuracy, supported by better-validated health and geolocation inputs.
These improvements in data analytics in pharma industry strengthened the client’s ability to provide timely, precise insights to veterinarians and farmers.
