Efficient test data management is a critical component of modern software development. With the demand for quality applications growing rapidly, managing and provisioning test data has become more challenging.
In this blog, we explore the essentials of test data management, highlight its importance for DevOps initiatives, and discuss ways to overcome common challenges while harnessing advanced tools for improved efficiency, compliance, and cost management.
What is Test Data Management?
Test data management is the process for providing controlled data access to modern teams throughout the software development lifecycle (SDLC).
See my colleague Vikram Kulkarni explain test data management in 5 minutes:
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Why Is Test Data Management Important?
Modern test data management solutions help organizations accelerate application development speed, code quality, data compliance, and sustainability initiatives by providing timely access to fresh relevant data downstream for code development, automated tests, troubleshooting, and validation.
Back to topWhat Can Test Data Management Tools Do?
Test data management involves the synchronization of multiple data sources from production, versioning copies, sensitive data discovery, compliance masking data, and multicloud distribution of test data to support agile development and automated testing.
Managing Sensitive Data
A test data management tool helps CIO and CISO teams to administer security controls such as data masking, authorization, authentication, fine grained data access management, and audits logs in downstream environments as part of test data management processes. This helps organizations quickly meet compliance and data privacy regulations at test data provisioning, while also reducing data friction for AppDev and software test teams.
Back to topState of Test Data Management Tools
Test Data Needed
Modern DevOps teams need high quality test data based on real production data sources for software testing early in the SDLC. This helps development teams bring high-quality applications to market at an increasingly competitive pace.
Data for DevOps
Though many organizations have adopted agile software development and DevOps methodologies, there has been an underinvestment in test data management tools—which has constrained innovation.
Accelerate DevOps Initiatives
Modern DevOps teams are focused on improving system availability, reducing time-to-market, and lowering costs. Test data management helps organizations accelerate strategic initiatives such as DevOps and cloud by greatly improving compliant data access across the SDLC. Test data automation improves software development speed, code quality, data compliance, and sustainability initiatives.
Back to topTransform DevOps with Modern Test Data Management
Successful application development requires streamlined test data processes. Learn how DevOps test data management addresses the biggest challenges in modern software development, from eliminating data constraints to improving speed, quality, and compliance. Get your copy of the white paper today.
Common Test Data Management Challenges
Application development teams need fast, reliable test data but are constrained by the speed, quality, security, and costs of moving data to environments during the software development lifecycle (SDLC). Below are the most common challenges that organizations face when it comes to managing test data.
Slow, Manual, High-Touch Provisioning
Test environment provisioning is a slow, manual, and high-touch process.
Most IT organizations rely on a request-fulfill model, in which developers and testers find their requests queued behind others. Because it takes significant time and effort to create test data, it can take days, or even weeks to provision updated data for an environment.
Often, the time to turn around a new environment is directly correlated to how many people are involved in the process. Enterprises typically have 4 or more administrators involved in setting up and provisioning data for a non-production environment. Not only does this process place a strain on operations teams, it also creates time sinks during test cycles, slowing the pace of application delivery.
Lack of High-Fidelity Data
Development teams often lack access to test data that is fit for purpose. For example, depending on the release version being tested, a developer might require a data set as of a specific point in time. But all too often, they are forced to work with a stale copy of data due to the complexity of refreshing an environment. This can result in lost productivity due to time spent resolving data-related issues and increases the risk of data-related defects escaping into production.
Friction in Release Cycles
For many applications, such as those processing credit card numbers, patient records, or other sensitive information, static data masking is critical to ensuring regulatory compliance and protecting against data breaches. According to the Ponemon Institute, the cost of a data breach—including the costs of remediation, customer churn, and other losses—averages $3.92 million. However, masking sensitive data often adds operational overhead; an end-to-end masking process may take an entire week because of complexity for managing referential integrity across multiple tables and databases.
Rising Storage Costs
IT organizations create multiple, redundant copies of test data, resulting in inefficient use of storage. To meet concurrent demands within the confines of storage capacity, operations teams must coordinate test data availability across multiple teams, applications, and release versions. As a result, development teams often contend for limited, shared environments, resulting in the serialization of critical application projects.
Regardless of your challenge, adopting test data management best practices can offer a path forward.
Back to topCommon Types of Test Data
There are four common ways to create test data for application development teams and testing teams in the SDLC.
Production Data
Real data from production environments provide the most complete test coverage, but can add friction without modern DevOps test data management tooling because of security controls around sensitive data.
Data Subsets
Test data subsets can improve static test performance while providing some saving on compute, storage, and software licensing costs. However, subsets do not provide sufficient test coverage for system integration testing needs. Subsets intrinsically omit test cases and contains sensitive values because it's still a direct copy of production values.
Masked Data
Production data obfuscation using masking techniques helps teams leverage existing data in a compliant manner to quickly provision test data that meets regulatory requirements such as PCI, HIPAA, and GDPR.
Masking takes all the data from production, leverages algorithms to identify sensitive data, applies PII data masking to sensitive fields while keeping only relevant data for testing. This enables test data provisioning of realistic values without introducing unsafe levels of risk.
Synthetic Data Generation
Synthetic test data intrinsically contains no personally identifiable information or sensitive information. This makes synthetic data creation an appealing choice for initial prototyping of new features or model exploration of test data sets.
Synthetic data generation typically involves mathematically computing values or selecting list items using algorithms to match a statistical distribution.
While synthetic data can help with initial unit tests, it cannot replace complete data sets that are needed throughout the testing process. Realistic data from production contains valuable test cases that are necessary to validate applications early and often to shift left issues in the SDLC.
📘 Further reading: Test Data Strategy for the Enterprise: How to Enable Success
Back to topGet Started with Perforce Delphix for Test Data Management
Perforce Delphix test data management solutions make it easy for you to deliver realistic, compliant data in minutes — not days. Plus, Delphix recently introduced AI-powered synthetic data generation, giving you the best of both worlds (masking + synthetic).
Delphix delivers, so you can:
- Automate provisioning of realistic data, so you can 2x increase project velocity.
- Gain high-fidelity data to drive quality testing reducing defect rates.
- Remove friction in release cycles by enabling fast data masking.
- Bookmark, rewind and re-baseline your database in minutes regardless of the size of the database.
- Reduce your storage footprint by 10x, so you can mitigate rising storage costs.
Further reading >> Why Companies Choose Perforce Delphix for Test Data Management
See Delphix In Action: Request a Demo
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