What is Test Data?

One of the top test data management trends is shift-left testing, a quality engineering approach that prioritizes testing early in the development lifecycle – so that testing doesn’t become a bottleneck in agile software development. The idea behind shift-left testing is to identify and fix defects as early as possible in the development process, reducing the time and cost associated with testing and debugging later on in the development cycle.

Traditionally, testing is performed at the end of the development cycle once the software development has been completed. A shift-left testing approach starts testing at the beginning of the lifecycle, with tests run in parallel with software development. Early testing reduces the risk of expensive rework and delays later in the development cycle by identifying defects and fixing them earlier.

Shift-left testing also emphasizes collaboration between developers and testers, with the goal of identifying defects and issues as early as possible. Collaboration can reduce the risk of expensive rework and delays by ensuring the software being developed meets the requirements and specifications of the product team.

Shifting testing to the left requires the preparation of compliant test data subsets to enable development and QA teams to quickly test specific scenarios and/or functionality with maximum test coverage. This is achieved by automating and streamlining various processes currently done manually.

By using appropriate test data preparation tools, some organizations have reported as much as a 90-95% reduction in the time taken to provision high-quality test data.

Test data can be created manually or automatically using test data management tools (which provision test data from production environments, or generate synthetic test data).

Manually

Manmade test data relies on ETL tools and complex scripting. While adequate for smaller datasets, this approach is not an option for larger ones. Additionally, to create data manually, testers must have a deep understanding of the application, including its database design, and all the business rules related to it.

Automatically

Test data management tools, whose global market is expected to grow from 1 billion in 2022 to 2.5 billion in 2030 with a CAGR of 11% according to verified market research, provision test data that simulates real-life scenarios. Much of the functionality of these solutions (e.g., data masking, data tokenization, and synthetic data generation) can be automated, allowing users to generate realistic datasets of various sizes and distributions quickly and easily.

• Using production data ensures that the resultant test data is fresh and accurate, because it’s already been validated against the original database schema. The main problem with this method is data privacy and security. Any sensitive data must be identified and anonymized before being used in test environments – due to legal and compliance issues, depending on the jurisdiction. Production data may also change over time, so it's crucial to consider data versioning to align the test data with the current state of the application.

• Generating synthetic data results in test data that mimics the features, structures, and statistical attributes of production data, while maintaining compliance with data privacy regulations. Synthetic test data is commonly used to test applications in a closed test environment before deployment to production. It can be more efficient to use than test data sourced from production because it allows testers to generate large amounts of test data quickly and easily.

QA teams spend almost a third of their testing time dealing with faulty test data. In other words, at least a day a week is wasted on test data provisioning. That’s because the test data must be: 

1. Sourced 

   Enterprise data is often dispersed and siloed across many different data sources, including legacy mainframes, SAP, relational databases, NoSQL and cloud environments. It's also stored in many different formats, making it difficult for software teams to quickly access the data they need.

2. Subset 

   The ability to subset large, diverse test datasets into small, focused ones, helps QA teams focus on specific test scenarios without the need to process or analyze large volumes of data. Not only is test data subsetting crucial for recreating and correcting problems, it also minimizes the amount of test data and related hardware and software costs. 

3. Protected 

   Data privacy laws, including CPRA and GDPR, and, mandate that Personally Identifiable Information (PII) – like names, addresses, telephone numbers, and Social Security Numbers – be protected in the test environment. Anonymizing sensitive data can often be complicated and time-consuming for data teams.

4. Consistent 

   Referential integrity is the consistency of data and schema across databases and tables. Ensuring the referential integrity of masked test data is critical to its validity. 

5. Extensive 

   Test coverage refers to the extent an application's code has been tested. It measures the percentage of code, requirements, or functionalities that have been exercised by a set of tests. Defining the required test cases is the first step, but ensuring sufficient test data to fully challenge the test cases is equally important. Less test coverage translates into more defect density.

6. Lifelike 

   Test data must be as representative of the original data as possible. Badly designed test data often results in false positive errors, leading to wasted time and effort on non-existent problems. Insufficient test data leads to false negatives, which can impact the quality and reliability of the software. 

7. Reusable 

   Reusing test data is a critical capability when verifying software fixes. Versioning test datasets for reuse lets testers perform regression testing (re-testing to ensure that the errors found in previous tests have all been resolved). 

8. Reservable 

   Testers often override each other's test data inadvertently, leading to corrupted test data. The ability to reserve specific test datasets for specific testers prevents the need to re-provision the test data and re-run the tests. 

Enterprises that effectively address their test data challenges benefit from improved: 

1. Response times 

   Provisioning the right data, at the right time, to development and testing teams, improves agility and speeds up time to market for new software builds. 

2. Software quality 

   High-quality test data, coupled with a superior management tool, optimizes the 4 DevOps research and assessment metrics – deployment frequency, lead time for changes, time to restore service, and change failure rate. 

3. Cost efficiencies 

   Test data best practices cut costs by speeding up test data provisioning, balancing resources better, enabling test data versioning, expanding test coverage, preventing test data duplication, providing self-service capabilities, and reducing infrastructure costs.

4. Compliance 

   Using data masking tools and synthetic data generation tools on test data enables companies to comply with data protection regulations (like CPRA, GDPR, and HIPAA), ensures that only authorized personnel have access to real data, and minimizes the impact of a data breach by making any exposed data meaningless to hackers.  

5. Employee experience 

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

A unique test data management approach ingests and organizes data via business entities (customers, employees, devices, orders, etc.) into a test data store, compressing and masking the data, while maintaining referential integrity. It enables testers to provision compliant test data subsets to their target environments and easily move them from one test environment to another, and from one sprint to the next. 

It covers every phase of the test data management lifecycle:  

1. Defining and sourcing 

   Relevant test data is identified and extracted using a simple, customizable GUI capable of accessing hundreds of relational database technologies, NoSQL sources, legacy mainframes, flat files, and more. 

2. Syncing and refreshing  

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

3. Segmenting and subsetting 

   With the ability to rapidly segment and subset test data, engineering teams speed up software delivery by eliminating long response times, expanding test coverage, and reducing test failures.  

4. Masking and encrypting 

   With dynamic data masking, the most complex rules can be implemented, simply and consistently across all data. And each business entity is encrypted with a different key for additional protection. 

5. Generating synthetic data 

   When a business entity schema is defined, it also defines a pathway to synthetic data creation. 

6. Provisioning and moving the data 

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