This Top 5 brokerage firm was experiencing significant project delays due to long testing cycles. They also had serious concerns regarding the security of customer data in their test environments. They needed a solution that could work with very large data sets, reduce the size of their test data sets to reduce cycle times, and support end-user testing without the need for real customer data. Our client selected Acceletest to create meaningful subsets of production data from multiple large-scale source systems, and to de-identify sensitive data elements to protect the privacy of their customers.
Our client leveraged Acceletest’s requirements driven rules engine to produce a representative subset of production scenarios to support their testing. Acceletest automatically sourced the data from multiple disparate platforms. They also used Acceletest’s intelligent de-identification engine to replace real customer information with realistic, meaningful values that made sense to their testing community. When key values were changed, like social security numbers or account numbers, AcceleTest automatically maintained referential integrity within databases, and across data sources, including flat files and leading RDBMS platforms.
Our client was able to install and configure the tool to their requirements in the first week after only a few hours of training. In less than one hour, Acceletest processed over 27 million customer records to produce a representative subset of production data, with all sensitive data elements transformed to eliminate the risk of a data breach out of their test environment. Several smaller data sets were processed in less than one minute. Without the benefit of Acceletest, it would have required several highly technical resources working over several weeks, to produce a similar result.
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