How to Address Your Test Data Management Challenges

Effectively addressing test data management challenges, improves agility, software quality, cost efficiencies, data compliance, and employee experience. 

Table of Contents

The Need for Test Data Management
Test Data Management Challenges
Benefits of Addressing the Test Data Management Challenges
Entity-based Test Data Management 

The Need for Test Data Management 

While enterprises have spent the last decade advancing and refining software development methods, processes, and tools, test data remains a bottleneck to speed and agility. DevOps and QA teams often find it frustrating and time-consuming to get the data they need from different sources – and then have it formatted, masked for compliance – and finally loaded into their testing environments.  

According to Gartner’s Software Engineering Leaders Survey, onboarding, nurturing, and retaining top talent ranks as the #1 challenge facing DevOps managers today. The survey indicates that the top concern for more than half of the managers is employee experience. So, adhering to test data management best practices isn’t just about improving agility, software quality, cost efficiencies, and compliance, but also about improving job satisfaction. 

Test data management is the answer. 

Test Data Management Challenges  

To address the 8 most common test data management challenges, test data management tools should be able to: 

1. Source test data 
   Enterprise data is often siloed and fragmented across dozens of data sources, and stored using different technologies and data formats, making it difficult for testers to obtain the data they need for each test. According to research, QA engineers spend almost half their time finding and analyzing test data. 

2. Subset test data
   Subsetting enables testing teams to identify and extract a precise test data subset to activate specific test scenarios with 100% coverage. This is especially important when recreating production issues. In doing so, it also enables teams to reduce the quantity of test data (as well as associated software and hardware costs). 

3. Protect test data 
   Data privacy regulations, such as CPRA, GDPR, and LGPD require that Personally Identifiable Information (PII) – sensitive information that can be used to identify an individual (e.g., name, Social Security Number, driver’s license, email address) – be de-identified or anonymized within the test environment. Discovering and masking all PII, while ensuring referential integrity of the test data across systems, is labor-intensive and time-consuming for data teams. 

4. Enforce referential integrity 
   Referential integrity refers to the consistency of data across database tables. For example, when a foreign key value is used in a table, it must reference a valid, existing primary key in its parent table. Ensuring referential integrity of test data across databases is critical to the validity of the data and becomes even harder to enforce after the data is masked.

5. Achieve full test coverage
   Test coverage is a metric used to measure how much of an application’s code is exercised by test cases. Defining the needed test cases is one challenge, but ensuring you have the relevant test data to fully execute the test cases is an even greater one. Low test coverage is directly related to high defect density. 

6. Reduce false positives and negatives 
   When test data is poorly designed, it often causes false positive errors, leading to valuable time and effort wasted in dealing with non-existent bugs. When test data is insufficient, it leads to false negatives, which can affect the quality and reliability of the software. 

7. Reuse test data 
   Reusing test data is critical when re-executing test cases to validate software fixes. By versioning datasets, it becomes possible to quickly rerun tests to validate that software bugs that were discovered in a previous test were resolved. It’s also essential for running regression tests using the same data.  

8. Prevent QA data collisions 
   It's not uncommon for testers to inadvertently override each other’s test data, resulting in corrupted test data, lost time, and wasted efforts. In such scenarios, test data must be provisioned again, and tests need to be rerun. 

Benefits of Addressing the Test Data Management Challenges 

Companies that effectively address their test data management challenges can expect to improve: 

1. Agility 
   Providing development and testing teams with the right data, at the right time, enhances agility and accelerates time to market for new software applications. 

2. Software quality 
   DORA (DevOps Research and Assessment) is a Google Cloud research program that defines metrics – deployment frequency, lead time for changes, time to restore service, and change failure rate – to rate how DevOps teams perform. Proper test data management should improve all these metrics. 

3. Cost efficiencies 
   When done well, test data management should improve cost efficiencies by reducing infrastructure costs, accelerating data provisioning, preventing data duplication, better balancing the use of resources, expanding test coverage, allowing for data versioning, and providing self-service capabilities. 

4. Compliance 
   The right test data management solution should provide for both synthetic data generation tools and data masking tools to ensure that only authorized personnel have access to real data, enable companies to comply with data protection regulations (like CPRA, GDPR, and HIPAA), and minimize the impact of a data breach, rendering any exposed data useless to attackers.  

5. Employee experience   
   For data engineers, copying production databases into staging environments, manually scrubbing, masking, and formatting data is a long, tedious, repetitive process. For DevOps and QA teams, waiting for the data, using the wrong data, dealing with problems related to the data (e.g., reporting false positives, lacking sufficient test coverage, overriding each other’s test data, etc.) The right test data management solution improves job satisfaction for data engineers, as well as DevOps and QA teams, alike. 

Entity-based Test Data Management 

Entity-based test data management ingests and organizes data via business entity (customer, employee, device, order, etc.) into a test data store, while compressing and anonymizing the data, and enforcing referential integrity. It enables testing teams to provision compliant subsets to their target environments and easily move test datasets from one test environment to another, between sprints. 

It covers every phase of the test data management lifecycle:  

1. Define and source 
   Relevant test data is identified using a simple, customizable GUI accessing 100s of relational database technologies, NoSQL sources, legacy mainframes, flat files, and more. 

2. Refresh and synchronize 
   Sync strategies and refresh rates for the test data are unique to each business entity, allowing for full control over the test data. 

3. Clone and subset 
   With the ability to rapidly clone and subset test data, engineering teams accelerate software delivery by eliminating long response times, reducing test failures, and expanding test coverage.  

4. Mask and secure 
   Data is masked centrally, so the most complex rules can be implemented, simply and consistently, across all data. Each business entity is encrypted with a different key, for extra protection. 

5. Generate synthetic data 
   When defining a business entity schema, you also define a pathway to synthetic data generation, resulting in synthetic test data, whose definitions can be enhanced to comply with any requirement. 

6. Provision 
   Test data management hinges on its ability to move data from many sources to many target systems. The entity-based solution executes in-memory, in a distributed environment, so provisioning test data is quick and efficient.