A leading clothing and accessories retailer with a wide customer base and over 130,000 employees, operating 3500+ stores globally.


Key Challenges

Poor visibility across various customer channels


Inaccurate business insights


Disparate data sources


Margin erosion due to improper visibility to cross-channel pricing

The Impact


accuracy in data validation through the use of utilities


Operational cost savings of $700,000 with accurate data consolidation and real-time availability of reporting data


15-20% effort savings with automated data validation


Shorter cycle times by reusing validation queries


The Solution

End-to-End testing solution for omni-channel reporting

Infosys solution helped the client gain seamless testing and omni-channel reporting across inventory, sales and pricing, thereby achieving significant cost, effort and time saving.

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Infosys deployed a Big Data Testing Solution that consolidated, validated and transformed data to improve testing and reporting.

Infosys leveraged the Hadoop ecosystem to ingest raw data along with business intelligence (Bl) reports validation. We ingested, validated, transformed, and consolidated raw data across various tables and layers based on source-to-target mapping. Data reports were also validated according to mapping logic along with functional report testing.

Infosys deployed a dedicated quality assurance (QA) team to enable end-to-end testing, thereby helping the client meet their goals. We were chosen for our proven expertise in enterprise data warehousing (EDW) as well as our knowledge of the client's landscape.

Automated data validation with in-built quality rules

Two key differentiators make the Infosys solution a unique one.

  • Leveraging an automated approach to validate metadata the data processing layers, thereby Identifying data compatibility issues between RDBMS and NoSQL data stores
  • Creating in-built quality rules to automate the validation of a huge volume and variety of data, thereby the rejection of incoming data files from different source systems.
Optimal data testing coverage for a data lake implementation


How to ensure data quality during data migration

A test approach giving proper attention to data and why such a high failure rate for data migration programs.

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