How can we make sense out of the ever-increasing data volumes and how can we set data free? The answer is synthetic test data generation and thus ConnectIQ was born.

What is synthetic data?

Synthetic data is data artificially generated by an AI algorithm that has been trained on a real data set. Synthetic data contains all the characteristics of production minus the sensitive content. As a result, the synthetic data set has the same predictive power as the real data, but none of the privacy concerns that impose user restrictions.

What makes it special is that data scientists, developers and data engineers have the complete control, and they don’t need to put faith in unreliable, incomplete data, or struggle to find enough data at the scale they need.

Advantages of synthetic data

With synthetic data, the value lies in the characteristics and patterns inside the data – its quality, balance and bias. Synthetic data allows you to optimize and enrich your data, offering several benefits.

Security: Protecting sensitive data
Synthetic data’s biggest benefit is that it eliminates the risk of exposing critical sensitive information to non-production environments. In the opposite case where real data is used, even if it is protected by encryption, anonymization, or other advanced privacy preserving techniques, there is always the risk of compromising or exposing it in some way.

Speed: Faster data access
Another challenge is getting access to the data quickly so you can start generating value from it. Leveraging synthetic data helps to overcome privacy and security challenges that often make it difficult and time-consuming to get and use data.

Increased data quality
The real issue with using production data in non-production environments is quality. A lot of production data is very similar, being collected from common or “business as usual” transactions, and is sanitized to exclude the bad data that will break systems. Testing therefore is not focused on non-functional and negative testing, and provides only 20% functional coverage. However, negative testing should constitute around 80% of testing, as it is these outliers and boundary scenarios that cause systems to collapse. In any other case, defects will invariably make it into production, leading to rework, critical delays, increasing costs, and potential project failure.

With synthetic data, you can control how the resulting data is structured, formatted and labeled. That means a ready-to-use source of high-quality, dependable data is just a few clicks away.

Scalability
Few data consumers can access exactly the data they need and, on the scale, they need, to develop and test their applications. Synthetic data are unique in bridging that gap, making it much easier and cheaper for companies to supplement their own data with additional data, without the worry of compromising anyone’s privacy.

Sharing Data Securely
Data sharing within different teams of the organization, as well as third-party vendors further complicates access issues. Organizations want to outsource testing and/or development without exposing production data to unauthorized users, or share data to downstream environments for AI and Analytics.

How ConnectIQ makes synthetic data easy, accessible and flexible

Maybe you want to test new scenarios today instead of waiting for months for sensitive data to clear compliance. Maybe you want to build more robust systems with no risk of mission-critical failure. Or maybe you want to automatically generate millions of realistic customer profiles in minutes, without putting original customer records and PII at risk.

Whatever you need your data to do, ConnectIQ can help you do it safer, faster, and more collaboratively.

• Rich synthetic test data maximizes data coverage and finds bugs earlier and at less cost to fix
• Easy-to-use, drag-and-drop functions, and visual data flows generate consistent data journeys for testing.
• Preserve the privacy of your production data while optimizing the quality of your test data
• Reduce your costs and save time by up to 90%
• Advanced analytics to design and build better data
• 40x faster data provisioning
• 100x faster access

With its Data-as-a-Service (DaaS) technology, it allows users to access data the same way they are accessing applications and infrastructure: as a service, available instantly, anywhere and on demand. It addresses the need for better data orchestration, delivering application data with the scalability, automation, and self-service capabilities demanded by fast-moving project teams. It enables organisations to move away from legacy systems and practices, helping them cut application project schedules in half and gain significant business agility and ROI.