Why You Need Real-World Test Data

A good system test consists of a set of valid scenarios. These scenarios should also cover exceptions, for example, when a user returns a product to a webshop.

To make testing effective, the data used for testing must be accurate and realistic. But real-world data is often off-limits or subject to privacy concerns.

Realistic Data Representation

Many people automatically associate synthetic data with fake data, and while the term may sound misleading, it can be used to create a dataset that is both accurate and useful. In fact, synthetic data has been around in one form or another for decades. It's found in flight simulators and scientific simulations of everything from atoms to galaxies.

Synthetic data is artificially created and manipulated to meet specific usage goals, such as validating software or evaluating performance. It can be partially or fully synthetic, meaning it contains real data from existing observations and measurements or generated entirely from models and simulations.

In addition to being a convenient shortcut when real data is too hard or expensive to collect, synthetic data can be used to explore unique scenarios that would be difficult to examine using only real-world data. For example, it can be used to generate fraud data that would be very difficult or dangerous to gather in the real world or train self-driving cars to handle rare accidents, like a car crash in the middle of a populated shopping mall.

Scalability

A scalability test allows you to find the threshold for the amount of load that an application can handle before it starts to slow down or break down. This allows you to make necessary changes before you start adding actual users. This prevents costly delays in production and helps you avoid losing user confidence, sales, and revenue.

Scalability testing is a critical part of the software development process. It is important to test the system under a range of load conditions, both up and down. It is also essential to determine whether the system can recover from a failure, as well as its peak performance under a certain load.

A successful scalability test can be the difference between an e-commerce website that fails to meet business demands during a busy day and one that thrives. A scalability test can also save companies from having to pay for expensive upgrades to their hardware or infrastructure in order to support new user growth.

Data Reusability

The notion of data reusability is influenced by the way in which it’s collected and shared. For example, if data is collected for one study and then used in another study that asks the same question, this would usually be considered a reuse. Conversely, when a research team retrieves its own data from a repository to deploy in a different project, this is often not considered a reuse because it’s essentially the same process as conducting the original research.

In addition, the format of the data influences its reusability. For example, astronomy datasets are released in standard formats such as FITS, which allows them to be analyzed with existing tools and combined with other data sets.

Successful planning for reusability involves limiting fragmentation by implementing a next-gen data management strategy that includes metadata collection, standardized data formats, and lineage, as well as providing flexible, cloud-ready infrastructure to store data at scale. This is important to enable digital transformation, operational resilience, and the creation of additional value for customers, stakeholders and employees.

Privacy

As artificial intelligence and machine learning become more widely used, there are growing concerns about how the algorithms that make them work could reveal personal details or discriminate against people. This means that researchers and developers must test their systems on real data, but it may be difficult or impossible to get access to the kind of detailed information they need.

Another concern is the possibility of bias — whether it’s a subtle social bias that affects results, or more serious errors caused by limited sample sizes or incomplete data sets. Using synthetic data can reduce both of these issues, ensuring that the algorithms are tested against full and varied datasets without being exposed to any real-world problems that might occur in production.

AI-powered synthetic data generation can be generated faster, cheaper, and in a secure environment. This type of data can be a powerful tool for ensuring maximum software quality and privacy compliance. It’s important to choose a data generator that retains the data structures and referential integrity of the source data, while adding a level of sophistication that ensures that synthetic data is completely accurate and a true representation of real-world data.