Success Story

Test data generation for an AI-driven platform

Discover how a Global 2000 software vendor synthetically replicates enterprise deployments of their flagship AI tool with Curiosity’s Enterprise Test Data!

The need

A global 2000 software vendor had launched their flagship, AI-driven project management platform, but were struggling to create and provision accurate test data.

The problem

Manual data creation meant they couldn’t mirror large implementations in test environments, slowing bug fixes and damaging enterprise client relationships.

The solution

Today, they generate on demand data across a complex SQL Data warehouse, while automatically documenting the system API and data model with Enterprise Test Data.

The benefits at a glance

From creating 100s of rows of data manually, to generating millions on demand.
An intuitive, visual approach to defining data and complex business logic.
Generation of data across databases creates a complete Data Warehouse.
Automated data comparisons and validation document the underlying data model.
The location and categorisation of sensitive data, to support compliance requirements.
A gold copy data set that is 50% bigger than all client data combined.

A danger to client loyalty

A multi-national software vendor had launched a new, AI-driven project management tool. The flagship platform was aimed at enterprise clients, and testing and development therefore needed to mirror large enterprise implementations.

Too complex to create by hand

Test environment teams were struggling to generate accurate data of sufficient variety and volumes. The vendor had sought to create data manually; however, this was unable to create the volume and variety of data needed to mirror enterprise software implementations. This was due to the vast and inherent complexity of the data.

Limited data understanding and documentation

While the data to generate was vastly complex, it was also poorly understood and poorly documented. There were no specifications of the physical data model or its relationships. The only available documentation lay in Swagger specifications of the API, which is used to push data into the project management platform.

Data warehouses, on demand

Today, the software vendor leverages Curiosity’s Enterprise Test Data to generate consistent data for the project management platform’s artifacts, hierarchy, audit logs, and more.

Intuitive, visual data design

Intuitive, visual data design using Curiosity’s workflow engine maps the complex data and its interrelations, avoiding the time and errors associated with assembling functions in copious scripts.

Data validation and comparisons

While generating data via the API, automated data comparisons and validations provide understanding of the platform’s complex data. This enables accurate data engineering, rigorous testing, and quality development.

Automatic documentation

Enterprise Test Data automatically produces accurate documentation, populating the relationships and logic uncovered during data generation, comparisons and validation as articles in the software vendor’s internal wiki.

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On-demand, synthetic data warehouses

Data generation for an AI-driven platform
50% more test data than all client data combined
18 test databases drive parallel development

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AI is making system data more complex than ever. Developing AI-driven, AI-built systems requires diverse data that reflects intricate relationships, trends and hierarchies. Model-based data generation is perfect for overcoming this complexity.

Principal Test Data Engineer

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Test data generation for an AI-driven platform

The benefits at a glance