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The software development cycle is filled with challenges, as organizations are faced with not only decreased time-to-market but also increased application complexity. To ensure applications remain stable and functional, from initial development through product launch and beyond, organizations need to employ a variety of testing types. 

Of course, as development increases in complexity, so does the testing required. A vital component of any successful testing scenario is test data management (TDM). It allows enterprise-level organizations to streamline, automate, and control all testing types used while reducing costs and increasing test quality. 

What Is Test Data Management (TDM) in Software Testing?

Test data management is the process of creating, managing, implementing, and delivering test data. Traditionally, software development testing took place in decentralized silos, but TDM consolidates testing under the purview of a single team, group, or department. 

Test data management services gather the data necessary for automated software tests, including data from unit, integration, and system tests. It involves obtaining and storing appropriate and accurate data required for automated tests, reducing or eliminating the need for human involvement in the testing process (a concept similar to robotic process automation).

As TDM has grown in popularity, it has expanded to include synthetic data generation, data masking, subsetting, artificial intelligence, and more.  

Ultimately, test data management increases the reliability and quality of the finished software product, resulting in a superior end-user experience. Also, the data obfuscation aspect of TDM helps organizations comply with all applicable data privacy laws and regulations.  

Who Uses Test Data Management (TDM) in Software Testing?

While the answer of “everyone” might sound simplistic and broad, the truth is that test data management techniques benefit all types of software applications. If testing occurs during the development cycle (and it should), TDM processes increase the accuracy, organization, and usefulness of the results. 

Because all software development requires testing, TDM will benefit essentially any project. That said, certain organizations and applications practically mandate the use of a test data management strategy

Enterprise-level applications require TDM due to their complex, multi-faceted testing needs. TDM benefits all major testing areas found in enterprise development, including functional, non-functional, performance, and automation testing.

Additionally, the obfuscation processes of TDM make its use essential for applications that involve personal or sensitive data, including any sites or applications connected to e-commerce, finance, and health care.   

What Types of Testing Is Data Management for?

Data management focuses on three broad categories of testing.

1. TDM for Performance Testing

Performance testing gauges an application’s performance under the expected workload, assessing its responsiveness, stability, and scalability. TDM allows you to focus testing on infrastructure and user-facing elements to achieve fast, reliable performance.

The best test management tools help increase refresh cycles and bulk data generation. 

2. TDM for Functional Testing

While performance testing analyzes the speed and stability of the application, functional testing determines if the software acts according to pre-determined requirements. Essentially: Does the software do what it should? Test data management services help maintain quality control over the core application plus new and upgraded features. 

TDM helps alleviate or prevent low coverage, access limits, lengthy data sourcing timelines, high dependency, and issues related to testing environment size.  

3. TDM in Automation Testing

Test data strategy for automation and hyperautomation processes allow touchless operations while also increasing accuracy by reducing the potential for human error. Test data management processes are used across all types of test data management automation tools and testing, including robotic process automation

A test data strategy for automation helps alleviate slow front-end data creation, lack of access to dynamic data, and an inability to access the testing environment. 

Benefits of Test Data Management 

advantages of setting up an testing center of excellence (TCoE)

TDM strategies, along with test data management automation tools, provide multiple benefits for enterprise-level organizations.

1. Improves Data Quality

All the testing in the world is fruitless if it’s built on incomplete, irrelevant, or corrupted data. TDM identifies, manages, and stores the data needed for automated testing, so you can ensure it’s appropriate and complete. Plus, by ending the need for data transfer between multiple testers, data corruption is minimized, if not eliminated. 

2. Develops Realistic Data

Testing results will be unproductive if testing data doesn’t accurately represent production data. TDM allows organizations to identify and store test data that mirrors the data found on production servers, ensuring test results reflect real-world software functions. Referred to as “realistic data,” it’s similar to production data in format, quantity, and other factors.    

3. Improves Access to Data

Automated software testing only operates efficiently when data is available at pre-determined times. For example, the data warehouse testing tools might need to access data at certain times for authentication purposes. Because TDM focuses on data storage, the appropriate data is always ready when required by the automated testing software and production timeline. 

4. Ensures Data Compliance

TDM helps organizations maintain compliance with all relevant government and other regulations, such as HIPPA, CCPA, and the EU’s GDPR. Test data management GDPR and other such regulations require production data that can include user names, location data, personal information, and more – data that needs masking before testing can occur. 

The best test data management tools allow organizations to automatically anonymize data for both internal and external use to ensure compliance.   

Challenges & Pitfalls of Test Data Management

challenges load testing

While test data management provides vital benefits for enterprise-level software development, they also have potential pitfalls. Understanding the challenges of TDM allows organizations to anticipate and minimize their effects. 

1. Production Cloning Is Slow and Expensive

To obtain testing data, most organizations will pull data from production servers and then anonymize it. However, gathering production data can be time-consuming, especially late in the development process when dealing with large amounts of code. 

After cloning the data, you need somewhere to store it. Infrastructure and storage costs can add up quickly. You can mitigate these costs with data slicing. Instead of cloning all production data, the team will carve out a smaller, representative “slice” of data. 

2. Obfuscation Processes Add Cost and Complexity  

As described earlier, user data is heavily regulated, even for internal testing, and requires anonymization. Unfortunately, the data obfuscation process adds complexity and costs to the development process. 

While the speed, accuracy, and cost-effectiveness of obfuscation are all improved with automated testing tools, a learning curve for relevant teams will still exist. 

Top Signs / Reasons Indicating That Your Organization Needs Test Data Management

While all software development benefits from test data management, organizations don’t always prioritize implementation. The following signs indicate an organization will see near-immediate benefits from implementing TDM:

  • Data size increases “across the board,” including increases in data set size, total data sets, database instances, and upstream systems.   
  • A significant amount of production time is spent preparing data for testing. 
  • Production data far outpaces the amount of testing data available. 
  • Application features are going live with errors. 
  • Testing teams are decentralized or must rely on data from a central source. 
  • Testing teams are overworked and unable to keep up with testing needs. 
  • Upstream data generates the vast majority of testing data.
  • Testing data sets aren’t reusable or easy to duplicate.

Test data management helps reduce, correct, and prevent these issues, among others. 

Types of Data in Software Testing

Software applications generate incredible volumes of data during development and after release. The test data management process typically focuses on the following data types:

1. Production Data

Production data is generated by real people using your application. Depending on the size of your user base and your application’s complexity, the volume of production can become very large, very quickly – which is why it’s typically divided into subsets based on testing needs. 

Note that production data often contains sensitive information relative to compliance issues, such as medical and financial data, that require obfuscation.  

2. Synthetic Data

Synthetic data is created either manually or with automated testing tools. It simulates real user behavior as closely as possible. 

Although it circumvents the need for data blurring, synthetic data does have limited usefulness. It’s primarily used for load testing new features. 

Accurately creating synthetic data requires a high level of expertise, although an automated test data management tool makes it easier. 

3. Valid Data

Valid data is the term used to describe data produced when no unexpected errors or incidents occur. The data’s format, values, and quantity align with pre-test expectations. Valid data tests what’s called the “happy path,” which is when the user’s journey follows the anticipated course. 

4. Invalid Data

Invalid data derives from the “unhappy path.” It is the data from unexpected scenarios and faults. Invalid data is also used as a part of chaos testing, which tests the limits of an application under a deluge of bad data. 

What Makes “Good Quality Data” for Software Testing Purposes?

Software testing checklist

Testing with incomplete or irrelevant data is often worse than forgoing testing entirely, as the conclusions drawn, and subsequent actions taken will be incorrect. But how do organizations identify “good” data for software testing purposes? Look for these three data quality characteristics:

1. Accuracy

Good data mirrors real-life procedures closely. If using masked production data, it should directly pertain to the area you’re testing – it can’t be a random sample of user behavior. Synthetic data should accurately resemble real user behavior, including their unpredictable nature. 

2. Validity

Good data matches the purpose of your testing scenario. For instance, most online shoppers don’t purchase 200 quantities of a single item, so extensive testing of system behavior in that scenario is a poor use of resources. However, you do want to test for situations where people purchase ten items. 

3. Exceptions

Data should cover issues that likely will happen, but infrequently. A scenario where a customer pays for an item with a coupon code is a common example of “exception data” in the e-commerce arena.  

What Questions Should You Ask Before and While Planning for Data Testing Management?

Testing success is largely determined in the planning phase. During the initial stages, teams should ask the following questions.

1. What Data Do We Need?

Determining what data needs collecting is a two-part process. First, it must relate to the testing scenario. It must also have business relevance to help testing remain cost-effective and efficient. 

2. How Much Data Do We Need?

Too much data, such as copying all production data, is costly, time-consuming, and overly complicates the process. On the other hand, if the sample size is too small, the results will be inaccurate. 

3. When Do We Need the Data?

Is the testing scheduled, or should the data be available on-demand? Teams should coordinate all test schedules and refresh cycles before testing begins. 

4. What Type of Testing Is Needed?

Software testing automation requires stable, predictable data sets. If the data necessary for your test varies considerably, manual testing might produce better results. 

Steps in Managing Data Testing

steps in setting up a robust test data management system (TDM)

While specifics will vary, enterprise-level software developers will generally follow these steps when implementing a TDM strategy. 

1. Data Creation – Techniques in Generating Data for Testing, etc.

To generate effective data, you’ll need to consider its accuracy and relevance. Does it replicate realistic scenarios? Additionally, you need to generate exception data, which covers scenarios outside typical user activity. 

2. Data Obfuscation

You will need to mask all production data to remain within regulatory compliance. The most common types of obfuscation include anagramming, encryption, substitution, and nulling. While manual obfuscation is possible in a limited capacity, enterprise-level masking requires automated tools.  

3. Data Slicing

Copying all production data is often a waste of resources and time. With data slicing, a manageable set of relevant data is gathered, increasing the speed and cost-efficiency of testing. 

4. Provisioning

Provisioning occurs after the data is obtained and masked. During provisioning, data is moved into the testing environment. Automated tools provide the ability to enter test sets into test environments using CI/CD integration, with the option for manual adjustment. 

5. Integrations

Test data from multiple sources within the IT ecosystem must be integrated into the CI/CD pipeline (the CI/CD pipeline is the established process for code changes). Achieving integration requires early identification of all data channels. 

6. Versioning

Creating versions of test data helps teams repeat tests to gauge results. Additionally, versions allow for the monitoring of precise alterations to testing parameters. 

Characteristics & Properties of Test Data Management

TDM adapts to the ever-changing needs of any software development project. However, regardless of any adjustments needed for an organization, the TDM process will also display the following characteristics: 

1. Improved Data Quality and Fidelity

TDM increases the accuracy and realism of your testing data so that it provides a truly representative sample of user behavior. All processes ultimately lead to one goal: a reliable, stable user experience.

2. Regulatory Compliance

Test data management software ensures all production data is sufficiently masked before testing, keeping your organization with all privacy regulations. By remaining compliant, you’ll avoid legal repercussions, including fines, and negative public relations issues. 

3. Improved Product Quality

Quality assurance is a time-consuming, costly process – but also necessary for launching functional, user-friendly applications. TDM processes allow for faster error identification, improved security, and more versatile testing compared to the traditional siloed method. 

How To Implement Test Data Management

RPA and Testing Center of Automation (TCoE)

Your organization’s software product will dictate a variety of testing specifics, but the basic implementation of test data management concepts involves the following five steps:

Step 1: Planning

Start by forming a data test team, who will then determine test data management requirements and documentation while also developing a comprehensive testing plan. 

Step 2: Analysis

During the analysis stage, data requirements across teams are consolidated. Backup, storage and similar logistical issues are also implemented. 

Step 3: Design

The design stage is the final point of planning before testing begins. Teams should identify all data sources while also finalizing plans for communication, documentation, and test activities. 

Step 4: Build

The build stage is where the “rubber meets the road.” Plans are executed. First, data masking occurs. Next, data is backed up. Finally, testing is run. 

Step 5: Maintenance

After test data management implementation, the company will need to maintain the processes for the project’s lifecycle. TDM maintenance includes troubleshooting, upgrading existing test data, and adding new data types. 

Test Data Management Strategies

how does automation testing work in industries like banking for example

Because TDM touches on so many different elements of the development process, it can quickly grow complicated. The following strategies allow you to stay focused and continually refine your organization’s test data management approach.    

Strategy 1: Enhance Data Delivery

Seek to consistently reduce the delivery time for test data by utilizing software testing services such as ZAPTEST. Tools with DevOps capabilities streamline testing with a low-touch approach. 

With ZAPTEST users can select Sequential; Random or Unique test data using auto or specific numbers of rows. They can specify data range and “out of values” policies allowing to create realistic data-driven test scenarios for Functional (UI and API), Performance testing and RPA.

Additionally, automated testing software can replace IT ticketing systems with a self-service system for users.  

Strategy 2: Reduce Infrastructure Costs

The volume of test data grows during development, resulting in increased use of infrastructure resources. TDM tools can help minimize associated infrastructure costs through data consolidation, archiving, and a process called bookmarking, which makes better use of testing environment space. 

Strategy 3: Improve Data Quality

Test data management solutions continually increase data quality characteristics by focusing on three key elements: the data’s age, accuracy, and size. 

How To Improve Test Data Management

TDM isn’t a static process. After the initial setup, you’ll want to strive for continual improvements by following these test data management best practices

1. Isolate Data

By running tests in a controlled environment, you can isolate the data to better compare expected versus actual output. Isolating data also allows for parallel testing.  

2. Minimize Database Storage

Storing test data in databases reduces automatic testing speed while also increasing the difficulty of isolating data. Automated tools, plus techniques such as data slicing, help reduce the amount of database storage required. 

3. Focus on Unit Tests

Follow the guidelines established by the test automation pyramid, which recommends making unit tests approximately 50% of your testing. Unit tests run independently of external data, cost much less than other testing types, and are relatively quick to implement. 

How To Measure Test Data Management 

what is software test automation

The following metrics provide crucial information on the effectiveness of your TDM strategies. 

1. Is Enough Test Data Available?

You can measure test data availability by tracking the time spent managing data for use in testing. If insufficient data is available, development time slows, and developers will feel constrained. 

2. Is Test Data Available for Automated Testing?

Automated testing processes require data on demand. Monitor the percentage of available data sets, plus the frequency they’re accessed and the frequency they’re refreshed. 

3. Are the Automated Tests Limited by Testing Data?

How many automated tests can you run with your current test data? If you need to run more tests than your data allows, you’ll need to gather test data more frequently.  

The easiest and most accurate way to obtain these measurements is with test data management software

 

Privacy Issues & How to Prevent It

While test data management originated as a method to gather and analyze data, over time it’s become equally important at preventing various privacy issues.

1. Data Regulation

TDM ensures your company remains in compliance with CCPA, HIPAA, GDPR, and all other relevant data privacy regulations. Failure to properly mask data during testing can result in significant financial and even potentially criminal penalties.

2. Consumer Backlash

Data breaches can result in substantial damage to a company’s image, as users will become reluctant to use an application prone to leaks. Test data management implementation helps garner user confidence by both preventing leaks and assuring potential users their data will be kept secure.  

Conclusion

The need for testing in software development will only grow more necessary and more complex. To streamline development processes, while maintaining quality control, enterprise organizations will need to utilize test data management software, specifically test management tools such as those created by ZAPTEST.  

The best test data management tools provide comprehensive, responsive test data creation and management, allowing for superior software with greater functionality delivered faster than ever before.  

FAQs

Here are quick answers to common questions about test data management in software testing. 

What is Test Data Management?

Test data management is the creation, management, and analysis of data necessary for automated data warehouse testing tools. Processes focus on identifying high-quality data pertaining to specific testing parameters, masking it, and delivering it to appropriate teams.

The best test data management tools automate many of the processes such as data gathering, obfuscation, and storage.  

What is Test Data in Software Testing?

A large portion of the data used in software testing is production data, which is generated by real users. Due to privacy regulations, production data requires masking before use in testing.

Software testing data can also be synthetic, which means it’s artificially manufactured to replicate the behavior of real users as accurately as possible. It’s often used to test new features or upgrades before they go live. 

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