Test data is an essential part of software development. Without accurate test data, you can’t build applications that perfectly meet today’s customers’ needs and expectations. Test data leads to greater software security, design, and performance.
Since test data is crucial for software development, it’s pivotal to have an excellent system to handle it. If test data isn’t managed properly, issues can arise—such as compliance risks and underperforming digital services.
In this post you will discover everything you need to know about streamlining your test data management process, from best practices to top challenges.
What Is Test Data Management?
Understanding how test data works is crucial before delving into test data management.
There are three types of test data: valid, invalid, and borderline. Companies use test data primarily for software testing or non-production purposes. Developers use it to assess how software performs in different settings and environments.
Test data is useful for performance testing, which can help show how a system responds to things like normal traffic and sudden spikes. Test data can also be used to security test a system, making sure it’s safe from unauthorized access or outside attacks.
What Does Test Data Management Entail?
You cannot use test data until you generate it. This is done through something called test data management, which coordinates the creation, improvement, and provisioning of tailored data for specific tests.
Test data management has two parts: preparation and usage.
1. Preparing Test Data
Data preparation for testing purposes involves taking data from a production environment and making it suitable for use in test environments. This may involve migrating data into the test environment, transforming it to ensure referential integrity, relationships, and quality, or creating new synthetic test data. There are three common approaches to preparing test data: cloning full production databases, subsetting production databases or creating synthetic test data sets i.e. fake data.
2. Using Test Data
After data is collected and sorted, it goes to the developers & testing teams who will use it to test software. During this stage, accuracy and security are key to maintaining industry standards set by government regulations. If developers have doubts about the quality of data they’re using, it could impact testing results.
Test Data Management Best Practices
The following strategies and systems are the best practices for managing test data, though every company has their own way of approaching it.
Employ Data Discovery
In any organization, data is usually residing on different machines and systems. Also, this data exists in various forms. So, it’s important to have a broad understanding of your data. This way, you’ll know the origins of the information before it starts being used or prepared for usage. Moreover,data discovery can also confirm that there is enough data available for software testing purposes and the potential “risk” of the data. For example the storage of PII Data.
Compliance Automation
Today, companies have to cope with an always-growing amount of data privacy regulations from both industry and government institutions. Common examples include the Health Insurance Portability and Accountability Act (HIPAA), the General Data Protection Regulation (GDPR), and the California Consumer Privacy Act (CCPA).
In other words, it’s a challenge to keep up with evolving rules and data privacy regulations. Yet, by leveraging automated test data management platforms that simplify regulatory compliance and offer authoritative guidance, you can steer clear of complications.
Use Strong Data Governance
Since testing environments often involve a lot of sensitive data, it’s important to deploy security technologies that will limit exposure and prevent unauthorized data access. For example, companies can use SIEM tools to monitor and restrict access to data in test environments.
Remember to Secure Data
If you’re working with sensitive customer data, production data, it’s essential to mask, encrypt or de-identify the PII* information to protect the owner. Data masking guarantees precise, dependable and realistic test data while avoiding a data breach, complaints, fines, and penalties.
*Personally Identifiable Information.
Top Challenges of Test Data Management
While managing test data, companies will often come across different challenges. If not careful, this can then result in a slower development or other unfavorable circumstances. To prevent this from happening, being aware of the following potential hazards is key:
Too Little Data
In order to be successful while testing, you need an abundance of precise data. Oftentimes, developers start gathering test data only to realize they don’t have enough useable information. A frequent workaround for this is synthetic data. While synthetic data isn’t as dependable as real data, it can still aid in certain cases and enable teams to run essential tests.
Too Much Data
In some cases, a company’s data quota may be surpassed. An excess of enterprise data requires more expensive storage and processing, as well making it challenging to organize databases.
Delete unneeded test data, including duplications and outdated tests that have become irrelevant.
Poor Performance Quality
Although software is typically tested before it goes into production, this does not guarantee that it will function as anticipated. Apps can experience a number of performance problems due to factors such as devices failures or poor connectivity.
By predictive testing and comprehensive stress testing an application, it’s possible to plan for failures before they happen, thus making the software stronger.
Weak Data Creation
Many developers prepare test data manually, which takes time away from other projects and can result in errors. This inaccurate or insecure testing often includes valid, invalid, and null data.
The best approach is generally to automate data creation by using dependable data generation tools that can generate a lot of valid test data quickly. This way, you’ll both save time and spend less on generating thedata.
Lack of Expertise
The lack of developers is making it difficult for companies to release software, as test data tools, or data security tools, usually require professional training and skills. This is especially true for data that complex and sensitive in nature. Without the adequate personnel, this task becomes close to impossible. ERP technology can take your company to new level.
Conclusion
Whether you are working in the healthcare industry, dealing with personal data, or trying to keep up with evolving regulations, managing test data effectively can be a major challenge. However, by using automated platforms that offer strong data governance and security features, as well as automating data creation and testing processes, you can successfully navigate today’s complex landscape.
With the right tools and expertise, you can ensure that your data is reliable and secure, enabling you to deliver high-quality software on time and within budget.
How Enov8 Helps You Simplifies Test Data Management
Test data management can go one of two ways. It can empower your development engineers and enable them to create great, and secure, software or, alternatively, turn into a expensive operational headache.
Looking for a solution to avoid that headache? Why not look at Enov8’s Data Securitization and Test Data Management platform. A test and data security solution that helps you identify where data security exposures reside within production data, rapidly remediate these risks to avoid security breaches, and validate your compliance success. The solution also comes with DataOps accelerators like Data Synthetics to create test data, Data Provisioning to streamline test data preparation, Data Mining & Test Data Bookings.
