Data Quality Improvement

Client's Challenge

Modulant’s client, one of the nation’s largest banking and financial services companies, needed to improve its processes for validating new and updated software. To support its Quality Assurance and testing programs, the company had been using test data platforms that were intended to include data representative of production systems. However, the processes to maintain these platforms had become inefficient over time. One issue was that there was no documentation to link testing criteria to business needs or specific test plans. To address these limitations the company wanted to increase the relevance of its test criteria, thereby increasing the accuracy and efficiency of its software testing process.


Modulant implemented a robust, flexible and sustainable test data extract solution to capture the structure, meaning and usage of test data; as well, the selection criteria used. This allowed business users to validate that relevant data was being used for quality assurance testing. The solution replaced legacy Cobol programs by automating extraction instructions that drove an ETL Engine. These instructions were derived directly from the repository of the test data and its business context so that managers had complete visibility into the selection criteria for the test data.


Procedures for building and maintaining test platforms were tied to business needs and specific test plans, resulting in a 20 percent reduction in platform maintenance costs. More relevant test data was provided to support software development and release schedules. In addition, the customer was able to reduce its reliance on subject matter expertise by 80 percent and ETL implementation costs by 40 percent.