High-quality data is a prerequisite for high-quality software testing. However, locating and using the right data within strict time constraints is challenging.
Test data managements tools make it easier to locate, access and use the data your teams need. Moreover, they can help you to streamline the process by automating critical tasks.
The ability to provision test data that reflects real-life operating conditions is vital for the success of enterprise applications. However, sourcing test data of the right quality can be challenging.
Privacy laws and other regulatory requirements limit the use of production data in testing environments. And if the same data is used by multiple testers, it can be corrupted.
In addition, many companies have limited facilities to refresh their test databases. This means that they have to wait for DBAs to prepare the database before testing can begin.
This is an inefficient process and can lead to delays in obtaining test data. It is also costly and leads to inconsistencies in the results of tests. In order to solve these problems, you need a data management tool that allows you to create and distribute reusable test data. It should also include automation for target database creation, provisioning, validation checks, and workflows to eliminate manual processes.
Generating synthetic test data can be challenging, especially as the number of tables in a database increases. Subsetting technology solves this by rapidly creating smaller sets of referentially intact data.
With subsetting, the underlying database is left intact and all the information is retained. By copying only a small fraction of the production database, teams can achieve much more reliable test data and significantly reduce infrastructure costs.
However, this approach can cause problems. First, it requires the use of a complex programming stack that software engineers may not be familiar with. Also, it relies on complicated queries to subset data from the underlying database.
These queries are likely to be subject to performance issues, which can result in a delay in generating test data and impact the testing cycle. Moreover, these queries can expose sensitive data. Masking is a better solution, but it has its own set of challenges.
Easy Distribution of Data
Good test data management is all about getting the right data to the right places at the right time. For example, you need different data sets for unit tests than you would for end-to-end acceptance testing. And you want a variety of data formats to conduct black box testing to verify that your system responds correctly to data it doesn’t expect.
Sourcing relevant, accurate, and focused test data can be difficult in modern digital applications. Time is wasted in creating and provisioning test data, and data becomes stale overtime.
Many organizations struggle with a lack of automated tools for data provisioning to environments. They invest in technologies to automate build processes and infrastructure delivery, but they lack comparable tools for delivering and managing test data. A streamlined test data management process can eliminate manual steps such as initial target database setup, configuration and validation checks to enable low touch deployments of ephemeral data environments. This reduces development and testing cycles, lowers infrastructure costs and improves compliance and security.
It’s important to set aside time for data cleanup – it’s often overlooked. This process focuses on removing or updating information that isn’t correct, outdated or duplicated. This also involves removing irrelevant data points that slow down analysis or confuse results. Typically, this is done on a regular basis to reduce the amount of time needed to process rogue information.
This is especially important because data errors and inconsistencies can impact your business’s performance and reputation. Dirty data costs companies over $3 trillion each year. This makes data cleaning a non-negotiable step in your business’s data handling routine. You can do this manually using a variety of tools and techniques or automate it with a data quality firewall.
By implementing this protocol across your organization, you can improve the accuracy and reliability of your test data management and ensure its integrity. This boosts the effectiveness of your insights and provides a strong foundation for your artificial intelligence and machine learning efforts.