The problem? If done in-house, using anonymized data significantly increases the burden on in-house development teams, as they must ensure that anonymization software works correctly and all PII data points are identified. Anonymized data provides the benefits of realism without the security risks associated with using PII. This involves taking real data and masking any personally identifiable information (PII). “Anonymized” DataĪnother common solution is anonymized data. The problem? Although this data better mimics reality, it remains challenging to maintain referential integrity between mock data tables within more complex systems. It can often generate data that resembles real data, such as people's names, company names, and addresses. This enables it to integrate into a software development pipeline, automating the creation of test data before tests are run.įor example, mock data can generate long or short strings, add special characters to strings, and include random NULL fields. Mock data can be generated against a set of rules to ensure this consistency. However, because developers use automated tools to develop it, it is typically more consistent.

Mock data is another in-house solution, but can also be created using generators or open-source libraries. The problem? It isn’t a scalable solution, making it viable only for small-scale, ad hoc testing. It can be useful for testing a specific edge case, as developers can more carefully craft the data to align with their needs. The data doesn’t often look like real data - an address field might contain random characters rather than something resembling an actual address.ĭummy data is quick and easy to make. Developers commonly create it as a placeholder for actual data. Dummy Dataĭummy data is an in-house solution for generating test data. Let’s examine these tried-and-true methods before exploring the more modern data synthesis approach. All four of these traditional approaches to generating data fall into the in-house category. There are three types of solutions for generating data in-house: dummy data, mock data, “anonymized” data, and data bursts. Four Approaches to Generating Test Data In-House Let’s take a look at four common ways to generate fake data that don’t always pan out… plus, a solution that works. There are many techniques for creating that fake data - from ad hoc, small-scale data that developers create on the fly, to data generated using a data synthesis platform.Įach strategy for producing fake data has unique benefits… but not all of them produce the same quality of results.

When testing an application, it’s common to generate fake data to simulate user entry, database contents, and high throughput scenarios.

Dummy database generator