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Test Data Management (TDM) refers to the function that manages, delivers, and creates test data for the application development team. Testing efficiency and testing objectives completeness are influenced by the quality of test data. A TDM approach seeks to provide the testing teams with data sets of appropriate quality, adequate quantity, acceptable format, and in an appropriate testing environment. The provisioned data may be synthetic, a subset of the production environment data set, or a lookup table.

What is test data, and why is it important?

Test data consists of non-production data sets that are modelled after the organization's actual data so that the testing and development team can perform rigorous and valid tests. Typically, test data is generated either by the tester or by an aiding program or function. The data collected during testing may be re-used or used once and then forgotten. Test data can also be used as a confirmatory tool, typically to verify that a set of inputs into a function produces a particular result. It is also intended to be used for bug detection during fixes. The test data can exist in various forms like: 

  1. System test data
  2. SQL test data
  3. Performance test data
  4. XML test data

The similarity of test data with the production data makes it suitable for testing and development teams to test the application rigorously against a similar production-grade data set. It is highly preferable for organizations with a large set of business-critical sensitive data that cannot withstand any breach in customer data. 

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 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

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.

Test Data Management Objectives

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 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.

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 TDM best practices:

  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. 

Conclusion

With the increase in the business processes and end-user needs, test-environment could be a significant factor in determining the application feasibility. Organizations maintain dedicated Test Environment Maintenance teams responsible for smooth release cycles to deal with the changing curve. 

Companies can transform how they consume and manage data by building a Data Platform for TDM. IT operations teams can deliver data 100 times faster and save ten times as much space as before.

What's Next?

- Read about Internal Developer Platform (IDP) benefits and its key components

- Learn How to get started with Application Modernization?