Data Provisioning for a Leading Loyalty Management Solutions Provider
Client Background
Client is leading product company that develops and operates professional customer loyalty programmes based on tailor-made IT solutions and process management systems. Their loyalty solutions have always set highest standards in the respective industries.
Business Context
Client was on the path of re-engineering their solution with a more scalable technology platform and had engaged on a performance testing exercise for their flagship product. They were looking to build confidence about their system such that their system would work well having a large volume of data over a period of 10 years. Unfortunately, the client did not have access to old historical and were looking for having such data in place for their exercise.
How did we help the Client?
Recommended them that synthetic data generation was the best solution on hand for their needs, that too without investing on expensive data generation tools keeping in mind their budget availability.
Synthetic data created data was to reflect the system state having 10 years of historical data. Complexities of data needs grew extremely high, we engineered data accounting for 25 to 30 data parameters- addressing it in terms of definitional data, master data and transactional data.
Leveraged solutions for meaningful data generation, that worked in tandem with macros and PL/SQL code that would deliver large volume of data for the engagement addressing all complexities associated with it.
Defined processes in place that helped created large data with adequate fall back mechanisms in multiple states of driving the data.
Benefits Delivered
Designed a solution to synthetically generate meaningful data of large volume, 1 million records under 7 minutes, helping the customers without any delays.
Processed designed was repeatable and already optimized which provided adequate confidence to the customer in terms of fall-back mechanisms.
Large volume of data created was stored as data images and was being able to quickly restore on short notice.
Synthetically generated was easily reusable across other test types like functional, security as well
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