A leading sporting goods organization required a testing solution for its PIM implementation with heterogenous systems across various locations and brands
Key Challenges
- Complicated business rules and manual data enrichment demanded a great deal of time and effort on the part of visual merchandisers
- Restrictions in the platform implemented for Product Information Management (PIM) and the lack of intermediate staging layers posed challenges for test automation and real time data integration
- Complex integration of data from 15+ heterogeneous systems (across 40+ locales and 2 brands)
- Large complex QA engagement involving multiple vendors and variety of validations
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The Solution
Automating data validation for STEP-technology-enabled Master Data Management (MDM) system implementation
Stibo Systems’ STEP Technology platform was used by the client for MDM as part of Product Information Management implementation. An end-to-end validation framework supporting multiple types of validation was the need of the hour for the same.
Infosys developed a validation framework which provided a comprehensive testing solution for PIM implementation validation.
Validation framework supporting multiple validations and an initial real-time data migration of approximately 5 million records were done as part of this engagement.
The complete end-to-end testing framework included the below key components:
- Java based tool for Integration Testing
- Custom tool for automated Data Comparison
- Jmeter based framework for Performance Monitoring
- Agile dashboards for Status Reporting
- BDD test framework for Regression Testing
Infosys’ Approach
First of its kind validation framework for Stibo Systems’ STEP technical platform Considering the complex architecture, a complete end-to-end testing framework was developed by Infosys which took care of the inherent limitations of the platform while supporting the complicated business rules and client needs.
Benefits
Automated data enrichment process reduced the efforts of visual merchandisers by 40% and led to elimination of misaligned contents across channels
100% data validation coverage and better root cause identification of data quality issues with the implementation of automated validation framework and standardizing data governance concepts
An overall 70% effort saving by adopting automated data comparison techniques
