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Enterprise Data Management

Test Data Management Tools and Working Architecture - Complete Guide

Chandan Gaur | 16 August 2022

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What is Test Data?

Test data is the data used to test the software application. Example: For test the login functionality, username, and passwords required. So the values of username and password are test data. This article will give an overview of Test Data Management. Test data are of two types

  1. Static Data
  2. Transactional Data

Static data comprises names, currencies, countries, etc., which are not sensitive. But when it comes to transactional data it involves data like credit/debit card numbers, information about bank accounts or it can be your medical history, there is always a risk of the data getting stolen.

What is Test Data Management (TDM)?

Test Data Management (TDM) is a process or function that provides test data to automated tests in the right amounts, time, quality and format. It plays a critical role in the test life cycle. The amount of data generated during a test is enormous. Reporting the results helps reduce the amount of time spent on processing data, which, in turn, speeds up the entire process of application development. Test Data Management keeps a check on the following:

  1. Analyze data elements common to all tests
  2. Archiving of test data, masking, and ageing
  3. Prioritization and allocation of test data
  4. Creating reports and dashboards for metrics
  5. Creating and implementing business rules
  6. Automating master data preparation
  7. Data ageing by masking, archiving, and versioning

An approach which consolidates test first driven methodology and refactoring. Click to explore about, Golang Unit Testing and Testing Best Practices

What is the objective of Test Data Management?

For testing an application, a tremendous amount of data is generated. Creating reports minimizes the time required for processing data, which significantly contributes to the efficiency of an entire product. TDM aims to -

  1. Prioritize and allocates data
  2. Identify common test data elements.
  3. Reports generation
  4. Ensuring business rules
  5. Archiving, modifying, and versioning data as it ages

Test Data Management is based on requirements

Test Data Management, driven by requirements, allows organizations to perform shift left testing, mitigate risks, and minimize defects, thus delivering quality software faster and lower cost. Changing the test data as per requirements makes it possible for the development and testing teams to respond to the changing business requirements.

Flowcharts produced by such model-based testing can provide all the qualitative information about a system needed for testing, despite its simplicity in design. To reach 100% coverage of test cases, testers must have access to 'fit for purpose' data delivered to the right place at the right time.  Based on requirements, the generated data is matched with a test case, ensuring that it is appropriate for the individual testers. In such a scenario, testing teams are more likely to find defects the first time around, thereby avoiding the time-consuming rework that keeps continuous delivery from working.

What are the common types of Test Data?

Organizations need to identify the appropriate type of test data by weighing its pros and cons and the testing needs. Below are common types of Test data. 

Production Data 

A copy of production data is made into development/testing systems 

  1. Advantage - It provides complete test coverage 
  2. Disadvantage - This could also expose sensitive data, increases storage cost and hinders agility 

Subsets of Production Data 

Not all the data is copied; selected data from a full-size production data is made. 

  1. Advantage - Saves hardware, CPU and Licensing costs 
  2. Disadvantage - Doesn't provide complete test coverage 

Masked production data 

A false but realistic replica of your organization's data is created for testing. The purpose is to safeguard sensitive data.

  1. Advantage - Development teams can use real data without risk 
  2. Disadvantage  - The process of masking can make environment provisioning longer. Additionally, more resources like masking stages to ensure referential integrity after data transfer are required. 

Synthetic data

It is created by developers either manually or by automation. 

  1. Advantage - Good for testing new features 
  2. Disadvantage - Manually created data is prone to human error and creation of test data requires in-depth knowledge of database schemes and file systems  

What are the Test Data Management Challenges?

As industries strive to deliver quality applications, maintaining a robust Test Data management system has become more challenging. Here are a few common challenges digital enterprises face in the realm of Test Data Management:

Test data susceptibility

A failure to protect test data from malicious activities could have profound financial implications and legal repercussions for your enterprise. QA professionals can now create secure test environments and stay in compliance with regulations by using data masking and de-identification solutions.

Secure storage of sensitive data

Test data management cannot be effective without selecting the proper storage method. The most popular data stores include text files, spreadsheets, RDBMSs and several TDM tools.

Performance imprecision

Testing is critical to improving the quality of applications. You must deploy solutions that provide real-time testing to ensure your apps perform well everywhere and at all times.

How Test Data Management works?

Four essential TDM techniques that empower software testing -
  • Exploring the test data.
  • Validating test data.
  • Building test data for Reusability.
  • Automating TDM tasks to accelerate the process.

Exploring the Test Data

Data can be present in different forms and formats, which can be spread across multiple systems as well. The respective team needs to search for the right data sets by their requirements and the test cases. Locating the correct data in the required format inside the time limitations is critical. This increases the demand for a robust Test Management tool that can manage with end-to-end business requirements for testing an application. It is clear that manually locating data and retrieving it is a slow task and might bring down the efficiency of the process. Hence, it is fundamental to bring into play a Test Data Management solution that ensures useful coverage analysis and data visualization.

Validating the test data

In the present scenario where associations are implementing agile methodologies, the data can be sourced even from real users. This data mostly comes through the application, which continued as practice for creating and exploring test data that gets utilized for conducting test cases by QA teams. Thus, the test data must be secured against any break in the development process, where sensitive personal data such as names, contact details, financial information, and addresses must not get uncovered.

This test data can be additionally stimulated to generate a real environment, which can further influence the outcomes. Real data is vital for testing applications, which are sourced from production databases and later masked for safeguarding the data. It is crucial that the test information is validated and the resulting test cases give a genuine picture of the production environment when the application goes live.

Building Test Data for Reusability

Reusability is essential to ensuring cost-effectiveness and maximizing the testing efforts. The objective should be to utilize it as much as possible and optimize the value of work that has been done. It should be getting from a central repository. Eventually, no time was wasted in resolving any unseen issues with the data. Datasets get put away as reusable assets in the central repository and supplied to the respective teams for further utilization and validation.

Automation can Accelerate the Test Data Process

Test Data Management involves scripting, data generation, data masking, cloning, and provisioning. Automation of all these activities can turn out to be successful. It won't just quicken the procedure yet additionally make it considerably more proficient. During the Management process, the test data gets connected to a specific test, which can feed into an automation tool that confirms that the data is given in the expected format at whatever point is required.

Automating the process assures the quality of the test data during the development and testing process. Like Regression Testing or any common tests, even the production of test data is automated. It helps in replicating enormous activity and the number of users for an application to create a production scenario for testing. It helps save time in the more extended run, reduces efforts, and helps detect any error with the data on an ongoing basis. Eventually, the QA team would be in a better position to streamline and validate test data management efforts.


A process that allows the user to check that the provided data, they deal with, is valid or complete. Click to explore about, Guide to Data Validation Testing Tools and Techniques

What are the benefits of Test Data Management?

The benefits of test data management are below mentioned-
  • Create better quality software that will perform reliably on deployment.
  • Prevents bug fixes and rollbacks.
  • Creates a more cost-efficient software deployment process.
  • Lowers the organization's compliance and security risks.
  • Customized test data to different kinds of testing - Functional, Integration, Performance, Security, etc.
  • Thereby resulting in no overstepping of test data by multiple teams.
  • Traceability of test data to test cases to business requirements helps to understand the test coverage as well as a defect pattern.
  • Assembles relationships and efficiencies by granting insights-driven Decision-Making across the entire organization.
  • Reduced data refresh cycle.

Why Test Data Management matters?

The quality of test data matters. If applications are tested against generic data, many problems can arise once the application is put into production. Applications must be tested rigorously against data to avoid problems that are as similar as possible to the actual data that will use.

Data and Continuous Delivery

Accurate, relevant, high-quality data is essential to the cornerstones of Continuous Delivery - Test Coverage, Automation, and Continuous Testing. With quality data, can discover defects earlier in the development life cycle for a less expensive fix and less danger of bugs in production. If testing and QA flop due to poor data quality, end-product fails too. The knock-on impact is unhappy clients who complain to possibly millions of people on social media and switch their service to another brand, taking friends, family, and followers with them. On the other hand, incredible information cleanliness improved security and streamlined data management, resulting in an improved Customer Experience (CX), Digital Happiness, customer loyalty, better brand name, and higher income.

Data Regulations

Another obvious standout advantage of getting to grips with data, not just for the test but enterprise-wide. The benefits are mitigating the risk of hefty fines; improving revenue by leveraging quality data, and reducing the risk of security breaches, to drive effective Decision-Making.
A deployable path that the software follows to its production with Continuous Integration and Continuous Delivery practices. Click to explore about, Continuous Integration and Continuous Delivery

How to adapt Test Data Management (TDM)?

The critical phases involved in a TDM process are -
  • Planning
  • Analysis
  • Design
  • Build
  • Maintenance
Phase Steps Involved
Planning 1. Assign Test Data Manager (TDM) and define data requirements and templates for data management.
2. Prepare documentation including a list of tests and data landscape reference.
3. Establish a service level agreement and set up the test data management team.
4. Appropriate plans and papers signed off.
Analysis 1. Initial setup and sync exercises involve data profiling for each datastore assignment/recording of version numbers for existing data in all environments.
2. Collection/consolidation of data requirements.
3. Update project lists.
4. Analyze data requirements and the latest distribution log.
5. Asses for gaps and impact of data modification.
6. Define data security, backup, storage, and access policy.
7. Prepare reports.
Design 1. Decide the strategy for data preparation and Identify regions needing data to be loaded/refreshed.
2. Identify appropriate methods and data sources and providers.
3. Identify tools.
4. Data Distribution plans.
5. Coordination/communication plan.
6. Test activities plan.
7. Document for a data plan.
Build 1. Execute plans and masking/de-identification where applicable.
2. Backup data and Update logs.
Maintenance 1. Support change requests, unplanned data needs, problems/incidents.
2. Prioritize requests and Analyze requirements and consider if they can be met from existing/modified current data including data assigned to other projects.
3. Required data modification and back up new data.
4. Assign version markers and log with an appropriate description.
5. Review status of ongoing projects.
6. Data profile exercises.
7. Assess/address gaps.
8. Refresh data where needed.
9. Schedule and communicate maintenance.
10. If necessary, redirect requests.
11. Documentation and reports.

What are the Test Data Management Best Practices?

It can be complicated to manage test data in agile, so it's not always clear how to do it. Following are some Eight Test Data Managament best practices that must be followed. 

  • Focus on Data security
  • Focus to Application Security 
  • Effective Planning is the key
  • Automation is crucial 
  • Isolation of test data from real data
  • Continuous Data Analysis
  • Data refreshing using a Central repository
  • Mock the production environment
  1. Focus on Data security

    The dummy data is similar in structure and algorithm to the real data, which could put the sensitive data at risk and go against the industry standards and government regulations. Data masking can be a breakthrough for the issue, keeping the real data safe while masking.
  2. Focus to Application Security 

    Apps under development lack many security protocols. It becomes necessary to delete information such as names and addresses from apps, thus preventing data from being stolen or misused.
  3. Effective Planning is the key

    As data volumes rise, proper storage and management are essential. A standardization of tests across groups reduces the amount of unnecessary overhead associated with testing. Having a good test plan is the key to efficiently enabling this standardization.
  4. Automation is crucial 

    Enabling automation wherever possible to avoid monotonous test scripts execution.
  5. Isolation of test data from real data

    Identification of test data is the foremost responsibility of a company. Besides sorting the data, it is also important to assign it appropriately. Those responsible for storing the data must know what data was used for testing and thoroughly understand all business processes.
  6. Continuous Data Analysis

    The test data requirement is scenario-based and can be hard to manage with the increased complexity of application and business processes. Analysis of this data could help ineffective management.
  7. Data refreshing using a Central repository

    There can be considerable savings in time and effort when a central repository contains all types of data that may be required for testing. Frequently accessed data sets can be easily discarded, ensuring that the correct data is always available and reducing storage costs.
  8. Mock the production environment

    Understand the end-user scenario and the data required and compare it with the data currently available for testing. 
Additional best practices for Test Data Management are highlighted below-
  • Never use excel as a test data source for automation, unless that’s the only option.
  • Externalize test data.
  • Discover and understand the test data.
  • Generate unique prerequisite data through automation for each automation run, wherever possible.
  • Consider all test environments.
  • Localization+Environment combined strategy.
  • Mask or de-identify sensitive test data.

Java vs Kotlin
The core data that refer to the business information shared across the organization. Click here for Master Data Management: Architecture and Best Practices

What are the Best Tools for Test Data Management (TDM)?

Some tools for Test Data Management are -
  • Informatica Test Data Management tool.
  • CA Test Data Manager (Datamaker).
  • Compuware’s Test Data Management.
  • Tarantula
  • InfoSphere Optim Test Data Management.
  • HP Test Data Management.
  • LISA Solutions for Test Data Management.

Conclusion

Test data management is the planning, designing, provisioning, storing, and managing test data to be used in testing software or products. Test data management tools help organizations increase the quality of the software by generating synthetic data and data profiling.