Ë
By Mariann Utrosa • February 25, 2021

Test Data Management - Simplified

Data is the heartbeat of every business application and having a proper testing strategy is essential. Data is, after all, a business's most valuable asset. Data breaches bring Enterprises to the ground. Protecting real data and managing how data moves from non-production to production or vice versa is critical.

Test Data Management (TDM) is gaining a fast traction of importance in the Enterprise, especially when non-production environments require meaningful data representing a production quality. Whether you're rolling out new features, performing software updates, or validating data to ensure quality testing, using real-time data is often very dangerous.

The Challenges

High-quality data can provide an abundance of useful information to businesses, which often involves using different tools and procedures associated with associated risks and business challenges. It's essential to consider these and plan accordingly. Some difficulties of TDM are;

Data Validity and Consistency

Over time data ages; it can become stale. Proper versioning of data is vital to maintain accurate data sets. It would help if you also had traceability at an end-to-end level, from inception all the way through the data exchange life cycle. Businesses must always maintain the integrity of their data. If you allow data to age, you may not be able to trace the data and validate its integrity, and it could prove to be very difficult to troubleshoot any issues that arise. For example, to successfully capture an audit trail, one must consider the above observations and meet all in most cases.

Data Privacy

Many applications contain sensitive Personally Identifiable Information (PII). There may be government mandates and regulations in place that stipulate the data must be masked, de-identified, or encrypted. Without a reliable process to protect that data, there's a real risk that valuable and personal information could be leaked and used maliciously. A data breach can be costly to recover from; it can damage reputations and often results in lawsuits and punitive fines.

Data Selection and Sub-setting

You need data relevant to the context of what you're testing, but how do you manage datasets effectively? You'll want to use a smaller subset of data in a scaled-down, non-production environment while mimicking the production environment. If you don't get your selection right, test coverage won't be as good as it should be. Ensuring you have the minimal viable subset to meet the application quality requirements is necessary.

Compliance and Privacy Laws

The European Union's (EU) General Data Protection Regulation (GDPR) came into effect in May 2018. They required companies to delete all instances of the EU's Personally Identifiable Information at the consumer's request, otherwise known as "The right to be forgotten." The GDPR also requires consumer consent to use their data for any purpose, including application testing. This means that if organizations use real and unmasked customer data in their testing, they are now violating the GDPR, resulting in hefty fines.

It's not just the EU that needs to be mindful. Any company that possesses data on EU-based consumers needs to comply – including all North American companies.

Best Practices and Essential Steps

When implementing a secure Test Data Management solution, following a few steps can help simplify the testing process by applying these five best practices to TDM before going to production:

  • Discover and understand your test data
  • Extract a subset of production data from multiple data sources
  • Mask or de-identify sensitive test data
  • Automate expected and actual result comparisons
  • Refresh test data

Wrapping it All Up

TDM is a challenge for most organizations, and creating a process to manage your data is quite the undertaking. 

Implementing a network-based solution like DataStealth enables companies to adopt a secure TDM approach that facilitates a self-serve model for developers and testers to access new test data sets, remove manual intervention, and eliminate any infrastructure requirements master' golden copy' and / or derivative test data sets.

Enable the convenience of automated Test Data Management while ensuring the highest degree of security and compliance in your organization globally.

DataStealth for Test Data Management