What is Test Data Management?

Test Data Management Overview

Test Data Management Overview

What is Test Data Management? With continuous integration and continuous deployment (CI or CD) pipelines, the change to agile development is speeding up the pace of innovation while also enhancing efficiency. This shift means that test data design and giving must keep up with the faster pace for testing teams. Shift-justify testing, in conjunction with agile software delivery, helps to raise the bar for quality testing. Instead of waiting until the end, testing starts early in the development process with this strategy. To use this methodology, test data preparation must be prepared even earlier and more quickly.

Keeping accurate and harmonized data for testing is an ongoing issue with production data dispersed across many corporate platforms. The requirement to anonymize data as required by privacy legislation, as well as the data creation of synthetic data to supplement the existing data collection, adds to the complexity. Delivering high-quality test data settings at a rapid speed is crucial for Development Operations (DevOps) management and testing teams. This study examines the obstacles encountered along the way, as well as the procedures required to get there.

In this article:

  1. What do I need to know about test data management? 
  2. Shift-justify Testing Requires Shift-justify Test Data
  3. Top Test Data Management Challenges
  4. Proven Test Data Management Strategy
  5. The Benefits of Test Data Management
  6. Test Data Management Tools
  7. DevOps tech: Test data management
  8. Ways to improve test data management
  9. How to measure test data management
  10. Current State of Test Data Management
  11. Common Types of Test Data

What Do I Need To Know About Test Data Management? 

Test Data Management

It is critical to have high quality test data. Many issues can start once an application is placed into production if it is tested against generic data. To avoid issues, programs must be carefully tested against data that is as close to the actual data that will be utilized as possible.

Why not copy production data for tests?  Due to security and regulatory issues, production data is frequently unsuitable for use in a test system. To protect users from having sensitive data exposed to the development and testing teams, data that contains personally identifiable information must be updated. Data masking techniques are used in test data management to conceal personally identifying information while keeping formatting and other key data features.

Who uses test data management? Organizations that process a lot of business-critical sensitive data employ test data management. It’s especially critical in areas like health care, where a data breach involving sensitive client information might be disastrous. Most firms, however, contain sensitive data that must be hidden for testing purposes.

Shift-justify Testing Requires Shift-justify Test Data

Preparing high-quality test data has always been a difficulty, especially in the context of rapid development and continuous testing/delivery. The delivery of valid test data has gotten even more difficult as microservices and the integration of various apps have grown in popularity. It is vital to have reliable test data that is as close to production as feasible for a test cycle to be effective, whether it is manual or automated. DevOps also necessitates automation, which requires qualitative, consistent, and predictable data sets to function well. For fully automated test suites to execute, sufficient and on-demand test data should be accessible. To ensure seamless test data management operations, this test data should not impede automated testing.

Shift-justify testing and test data management

Shift-justify testing is a method of software testing that involves testing occurring early in the development process. However, testing earlier necessitates the availability of realistic test data earlier. In agile development, where software is developed in sprints, shift-justify testing is prevalent. Because each sprint necessitates its own testing life cycle, creating real-time test data frequently becomes a bottleneck, negating the benefits of agile productivity. Let’s look at the issues with test data in DevOps and then look at a practical solution for each one.

Top Test Data Management Challenges

Testing teams must deal with a plethora of data limitations, which slow down software delivery while also compromising quality and agility.

Data accessibility Testing teams don’t always have access to the data they need, or the tools they need to retrieve it. Typically, enterprise data is dispersed among multiple data sources. Customer data, for example, could be maintained in dozens of applications, including customer service (CRM), billing, ordering, tickets, collections, campaign management, churn prediction, and so on. To execute functional tests that require customer data, data from all relevant source systems would have to be provisioned.
Data availability It can be difficult to collect enough production data to cover all of the essential testing scenarios.

For example, testers may need data from 300 customers (across all systems) that fulfill a specific set of requirements to complete a test scenario, but only 200 production samples are available.

Based on the production samples, TDM tools must be able to synthesis (create) 100 data samples while ensuring data integrity across all systems.

Data quality The data may be available in many circumstances, however it falls short of the required quality criteria for the following reasons:

  • Corrupted data

Sharing and reusing test data among different testers frequently results in data corruption. Relying on such tainted data could have serious consequences that aren’t apparent until far later in the software delivery process.

  • Irrelevant or incomplete data

Apps must be evaluated against specific data that supports the scenarios that must be tested. In order to ensure that test scenarios encompass the required functionality to be tested, a comprehensive subset of relevant data with referential integrity is essential.

  • Unmasked data

Given the importance of data privacy, all production data utilized in testing environments must be sufficiently masked by anonymizing personal data (making it unidentifiable). Noncompliance with legislation such as the GDPR and the CCPA may result in significant fines and brand reputation damage.

Proven Test Data Management Strategy

Test Data Management Strategy

Enterprises can speed up test data provisioning and optimize software quality by implementing a robust test data management strategy. Here are the measures businesses should take to supply agile test data at enterprise scale and complexity.

  • Define

Begin by establishing explicit criteria for the data collection method for the test. These identify the data subsets that should be utilized to test the use cases, such as the required business entities to cover the testing scenarios, the volume of data required for testing, its sources, and its freshness, among other things. An automated data catalog can be used to inventory and classify test data assets, as well as visually map information supply lines.

  • Extract

It’s time to extract test data from the organization’s production systems now that you’ve determined which data is required. A test data management solution – that can interact with production systems and extract test data according to established rules – comes in handy when the required data is distributed across many different systems and data sources.

  • Refresh and sync

Testing is a continuous process. Testing should be repeated once bugs are detected and addressed to assure quality. A test data management approach should include the ability to swiftly roll back previously used test data – by the specific tester, for the specific use case – without affecting test data currently in use for other tests. Companies should look for a test data management technology that is flexible, easy to integrate with source systems, and can roll data back on demand.

  • Mask

Without sufficient privacy and security protections, any test data management approach is incomplete. When dealing with production data, the difficulty is to maintain data integrity and data security while ensuring data privacy. Centralizing test data from numerous sources into a test data warehouse, using data masking techniques to safeguard it, and securing it along the route makes achieving data compliance and security requirements a straightforward and efficient procedure.

  • Synthesize

When test teams are unable to extract a sufficient volume of test data from production, a data synthesis solution is required to generate the required dataset. A test-data management strategy must include the ability to easily generate synthetic data from real production data.

  • Provision

It’s time to move the test data to the target test environments after acquiring the necessary test data, generating missing data, and masking it as needed. Test data management tools should provide a quick and seamless path from various source systems to various environments. Testers should be able to upload, modify, and remove test data sets manually or automatically via CI/CD integration.

The Benefits of Test Data Management

Test Data Management Benefits

Boosting effectiveness

When proper test data management tools and methods are used, the effectiveness of both the testing process and the delivered software product increases significantly. High-quality test data can be provided in minutes, allowing development teams to increase test coverage, accelerate delivery, and improve the organization’s agility.

Reducing time and cost

Teams are able to detect bugs early in the software development process and thus fix them at a much lower cost by quickly provisioning the necessary test data. Furthermore, freeing development teams from having to work hard to generate relevant data allows them to focus on innovation and moving the organization forward.

Preventing privacy and quality issues

Teams can adhere to privacy requirements and protect the company’s brand when test data management is both safe and of the highest quality. Reduced production errors and the avoidance of data breaches boost consumer trust, allowing businesses to stay ahead of the competition.

Test Data Management Tools

Test Data Management Tools

Enterprises save millions of dollars by switching to agile software development using high-performance test data environments. Test data management can help with compliance, cost-cutting, and improving the end-user experience. The task at hand is to identify the best appropriate test data management technology for your company.

The most recent advancement in test data management systems is a business entity approach, in which an entity might be a specific customer, product, order, or any other business object that is critical to the application under test. By business entity, test data is taken from the source systems, unified and masked as an entity, and then supplied to the target test systems. This solution streamlines the test data management process while also ensuring test data referential integrity, TDM efficiency, and total control over the TDM process.

Furthermore, data for business entities, both structured and unstructured, is ingested into a centralized test data warehouse, allowing testing teams to apply selection criteria to the entities in order to subset and provision the data appropriately.

The test data warehouse provides data versioning, which allows testers to separate test data and rollback test data. Before being stored, data in the test data warehouse is masked in flight. Personal identifiable information (PII) is saved in a variety of unstructured data structures, such as check pictures, PDF documents, chat scripts, audio files, XML documents, and others.Among the Best Test Data Management Tools are DATPROF,Informatica, CA Test Data Manager (Datamaker), Compuware’s, InfoSphere Optim, HP, among others.

DevOps Tech: Test Data Management

DevOps Tech & Test Data Management

Automated testing is an important part of today’s software development processes. To ensure that your app or service operates as intended and can be securely deployed to the production environment, you’ll need to be able to run a complete set of unit, integration, and system tests. It’s vital to provide realistic data to your tests to verify that they’re validating realistic scenarios. Test data is essential since it is required by all sorts of tests in your test suite, both human and automated. With good test data, you can validate common or high-value user journeys, test for edge cases, reproduce problems, and simulate mistakes.

It is, however, difficult to efficiently use and manage test data. Excessive reliance on data defined outside the scope of the test can make it brittle and cost more to maintain. External data sources may create delays and have a negative impact on test results. Copying manufacturing data poses a risk since it may contain sensitive information. To overcome these challenges, you must treat your test data with care and strategy.

How to implement test data management

Successful teams handle test data management with the following basic concepts, according to research conducted by DevOps Research and Assessment (DORA):

  • There is enough test data to perform comprehensive automated test suites.
  • On-demand test data for automated test suites is available.
  • The automated tests that teams can conduct are not limited or constrained by test data.

Strive to achieve each of these conditions in all of your development teams to improve test data management methods. These techniques can also help you improve your test automation and continuous integration capabilities.

Ways to Improve Test Data Management

Ways to Improve Test Data Management

The following guidelines can assist you in making better use of test data:

  1. Favor unit tests. Except for the code being tested, unit tests should be independent of each other and any other portion of the system. External data should not be used in unit tests. Unit tests should make up the majority of your tests, according to the test automation pyramid. Higher-level tests are more difficult to triage and maintain than well-written unit tests that run on a well-designed codebase. Increasing the coverage of your unit tests can reduce the testing needs for higher-level tests that use external data.
  2. Minimize reliance on test data. Data from tests must be carefully maintained and updated on a regular basis. You’ll need to update or reuse related test data as your APIs and interfaces change. This procedure entails a cost that may have a negative influence on team velocity. As a result, it’s best to keep the amount of test data required for automated testing to a minimum.
  3. Isolate your test data. Run your tests in well-defined environments with predictable inputs and outputs that can be compared to real-world results. Make sure that any data used by a test is directly associated with it and isn’t tampered with by other tests or processes. Wherever practical, your tests should use the application’s APIs to establish the necessary state as part of the setup process. It’s also necessary to isolate your test data if you want to run tests in parallel.
  4. Minimize reliance on test data stored in databases. For the following reasons, maintaining test data saved in databases can be particularly difficult:
  • Poor test isolation. Databases keep data indefinitely; unless expressly reset, any changes to the data will continue across tests. Test isolation is more difficult with less trustworthy test inputs, and parallelization may be impossible.
  • Performance impact. For automated tests, speed of execution is critical. Interacting with a database is often more time consuming and inconvenient than interacting with locally stored data. When possible, use in-memory databases.
  • Make test data readily available. Running tests on a copy of a whole production database carries a certain amount of risk. It can be difficult and time-consuming to update the data. As a result, the information may become outdated. Sensitive information might be found in production data. Rather, identify the data portions that the tests require. These portions should be exported on a regular basis and made easily accessible to tests.

How to Measure Test Data Management

It’s critical to track your success against the main criteria stated earlier as your approach to test data management grows.

  1. Adequate test data is available to run full automated test suites. You can track how much time developers and testers spend managing and editing data for use in test suites to get a sense of this. Perceptual measurements (surveys) can also be used to capture this by asking teams if they have enough data for their work or if they believe this is a restriction.
  2. Test data for automated test suites can be acquired on demand. This can be measured by the percentage of critical data sets that are available, the frequency with which those data sets are accessed, and the frequency with which they are refreshed.
  3. Test data doesn’t limit or constrain the automated tests that teams can run. This can be expressed as the number of automated tests that can be executed without requiring extra test data. You may also ask teams if they believe that test data limits their automated testing operations using perceptual measurements (surveys).

Current State of Test Data Management

In today’s digital economy, every company must bring high-quality applications to market at a faster and faster pace. While many companies have used agile and DevOps to accomplish this goal, many have underinvested in test data, which has created a bottleneck in the drive to innovate. Because of a higher emphasis on application uptime, shorter time-to-market, and lower infrastructure costs, the TDM industry has transitioned to a new set of strategies. TDM, like other IT initiatives like as DevOps and cloud, is rapidly evolving.

Test data management (TDM), once considered a back-office job, is now a vital business enabler for organizational agility, security, and cost efficiency. As the number of application development grows, many large IT organizations are realizing the value of consolidating TDM functions into a single group or department, allowing them to use innovative tools to create test data and operate much more efficiently than decentralized, siloed, and unstructured TDM teams. TDM’s outlook has since expanded to include the use of data subsetting and synthetic test data generation, as well as the most recent application of masking to modify production data, as increasing centralization has begun to offer considerable efficiency improvements.

Common Types of Test Data

Types of Test Data

There is no-single technology that can meet all of the TDM specifications. Instead, teams must deliver an integrated solution that incorporates all of the data types required to support a variety of testing needs. Following the assessment of test data requirements, a good TDM technique should aim to provide the appropriate forms of test data while weighing the advantages and disadvantages of each.

Production data delivers the most comprehensive test coverage, but at the cost of agility and storage costs. It can further entail exposing sensitive data in some cases. Subsets of production data are far more adaptable than entire copies. They can help you save money on CPU, hardware, and license, but getting enough test coverage might be tough.

Masked production data (either complete sets or subsets) allows development teams to work with real data without putting themselves at risk. Masking methods, on the other hand, may lengthen the time it takes to furnish an environment. To preserve referential integrity after data has been altered, masking demands additional storage and personnel in staging environments.

Synthetic data circumvents security concerns, but space savings are limited. While synthetic data may be required to test new features, it is only a small percentage of test cases. Creating test data manually is also prone to human error and necessitates a thorough understanding of data relationships both within the database initiatives or file system and those implicit in the data itself.

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