Leveraging Pre-Fabricated Test Data to Validate Artificial Intelligence Solutions

The big challenge

A challenge, rather the top challenge the engineers face, is to acquire the right type, volume, and variation of data to train the AI models. These AI systems require substantial volumes of test data throughout the product life cycle. The data also must be highly diverse to simulate lifelike scenarios that enhance the accuracy of the model. Majority of the QE teams are largely dependent on centralized teams for provisioning test data. However, the data provided by the centralized teams are not always accurate or might just not be enough to carry out testing of AI solutions. Moreover, data reused from production carries an associated risk of exposing sensitive customer information, along with a larger challenge of getting data for a rare scenario, that has never occurred in production. This process most of the times, results in delays in provisioning the right data to the QE teams. And if available, these could be a small data set inadequate to achieve the accuracy expected from the algorithms. To address these challenges, a test data pack available on-demand for the QE team helps in provisioning large volume of diverse ready-to-use test data.

AI Test Data Pack


An exclusive AI Test Pack validates Intelligent Automation Solutions that comprises of data for major business domains like Retail, Healthcare, Insurance and Banking. The solution can also be used to recreate production scenarios from Production Tickets, Purchase Orders, Invoices, Insurance Claims, Electronic Medical Records (EMRs)/Electronic Health Records (EHRs) etc. The retail industry has grown exponentially and has seen deep penetration to the end customers. E-Commerce solutions have been growing leaps and bounds across geographies and thrive to provide personal consumer experience. Changes and updates flow in rapidly to keep the customers glued to their brands. However, for the testing teams, there is a dearth of data that can be leveraged for test lifecycle automation. Teams require large volumes of data to ensure the highest coverage of test scenarios with expected and unexpected outcomes

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