Having the right data for testing is vital. Yet, many development teams struggle with having access to a sufficient amount of fit for purpose test data.
This leads to poor quality software with mediocre results on deployment. Here are 5 tips for better test data management: 1. Use a central repository. 2. Automate the process. 3. Create reusable data sets for different tests. 4. Mask sensitive data.
1. Create a central repository
With ever-increasing business reliance on software, rigorous testing is key. But the process often falls short due to lack of availability and quality of test data management.
Many of these tests require large amounts of data and the use of sensitive information. Cloning production data can expose personal information and lead to reputation or financial damage, while relying on outdated test data can result in inaccurate testing.
With a central repository, all files and artifacts are ingested and validated in a single location. This reduces work between teams and provides a clear, immediate understanding of how changes to assets impact the entire system. This central repository also allows you to identify and categorize the notable properties of each file or artifact across cases and data sources, accelerating validation times and increasing overall efficiency.
2. Automate the process
Managing large amounts of data manually is a tedious task. Automating this process will not only save time but also ensure the accuracy of the test results. Moreover, it will help you in avoiding any data-related issues that may cause delay in software deployment.
Data is available in various formats and spread across multiple systems, making it challenging for testing teams to locate the right data. Moreover, the data needs to be refreshed in a short period of time or else it may become stale.
Automation of this entire process will streamline the whole process of scripting, data generation, cloning and masking of sensitive data. It will further reduce manual effort and make the entire process much more efficient. This will ultimately result in better quality software that will perform reliably on a real-world environment.
3. Create subsets of data for different tests
Once you know what kind of test data you need, you can create or obtain it. It may be generated synthetically or taken from production systems (with the right security and privacy controls in place).
Test data needs vary between projects, and creating subsets for different tests is key to managing them. For example, black box testing requires a variety of different date formats to test how an application handles them. This can be generated easily using sample commands.
Subsetting also helps developers work more efficiently by shaping and sizing test datasets to fit their local environments. This decreases risk, speeds up development cycles, and saves storage costs. It also makes it easier to reproduce defects and accelerates debugging. Ultimately, this improves product quality and reduces time to market.
4. Mask sensitive data
Data masking is a key component of test data management. It obfuscates sensitive data when moving it from production to test environments. For example, it can replace personally identifiable information with fictitious values, while maintaining referential integrity.
The masked values must look realistic enough to enable testing and analysis. They also must persist through multiple extractions and be consistent across environments. This is called deterministic masking, which ensures that the same value will be masked across tables and columns (for example, first name – ‘Adam’ to ‘James’).
This process also protects production data as it is moved from production systems to non-production environments, like training and development. This is essential because 15% of bugs found in software are due to data related issues. This enables better quality tests and faster bug detection to improve speed of deployment.
5. Archive data
Having the right test data management process in place allows you to meet regulatory and compliance needs, reduce costs as a result of early bug-detection and create better software that performs reliably on deployment. It also helps ensure the quality and availability of test data and reduces the time it takes to retrieve and reprocess it.
Archiving data keeps records of past activity without cluttering active storage systems, improving resource management efficiency. Additionally, it’s a vital component of compliance for organizations with regulated industries that require the retention of electronic records as well as the ability to access archived data. Choosing a reputable data archiving vendor with technical support is critical. This ensures that you’re supported with the expertise to quickly resolve issues and maximize archiving capabilities.