Real-World Data vs. Fake Data: Choosing the right strategy for effective testing

Testing environments play a critical role in software development, ensuring applications function correctly before release. To achieve this, having test data that simulates real-world scenarios is essential. However, the choice between "fake data" and "real-world data" sparks an interesting debate, as each approach offers significant challenges.

In this article, we will explore the key differences between these two types of data, analyze their benefits and challenges, and ultimately highlight how a strategic combination of both can optimize the testing process, ensuring accuracy, security, and efficiency in development environments.

What is real-world data?

Anonymized real-world data is derived from production environments, ensuring it does not contain personally identifiable information while complying with regulations such as GDPR, CCPA, LPDP, and others.

These datasets offer a high degree of realism, as they preserve referential integrity, maintain the natural complexity of real-world scenarios, and accurately reflect user behavior, system interactions, and business logic. Additionally, real-world data naturally exhibits aging, reflecting how information changes over time and capturing historical trends and patterns that influence system behavior.

By leveraging real-world data, organizations can test applications under conditions that closely resemble actual usage, improving the reliability and effectiveness of their testing processes.

What benefits do real-world data offer?

Using real-world data provides significant advantages for your organization:

• Captures the complexity of real-world behavior, including intricate patterns, sudden fluctuations, and inherent biases while ensuring data privacy.
• Maintains appropriate statistical distribution and frequency.
• Preserves relationships and interdependencies between elements, allowing comprehensive "end-to-end" testing.
• Reduces the gap between development, testing, and production environments.
• Facilitates integration testing with other systems under production-like conditions.
• Provides immediate availability and reusability.

Challenges of using real-world data

Working with anonymized real-world data presents challenges. Identifying the right data for each test case, anonymizing it effectively, and delivering it on-demand to the testing environment are key challenges, especially in complex and costly environments with large volumes of data. Managing real-world data requires robust tools to ensure that no sensitive information is exposed and that masking processes remain effective, as well as addressing other critical challenges in test data management.

Synthetic data

The term "fake data" or "synthetic data" is widely used across industries but lacks a universally accepted definition. Different sectors and vendors interpret this concept in various ways depending on their testing needs and available technologies. While some consider synthetic data as manually created datasets, others define it as AI-generated data, or even simply masked real data. As these variations can create confusion, understanding the most common approaches provides greater clarity about what synthetic data really means.

Some of the most common definitions include:

• Traditionally created data: Data manually or traditionally generated using tools like spreadsheets, scripts or bussines apis. While quick to produce, it often lacks complexity, is prone to errors, and becomes costly over time.
• AI-Generated data: Data created by AI models trained on real-world patterns. Although it can mimic realistic behaviors, its reliability remains limited for mission-critical applications. For the time being, there is no evidence of successfully using this approach for testing business support systems.

Synthetic data limitations

These approaches to synthetic data generation often fall short when it comes to accurately simulating production environments, facing critical limitations challenges such as:

• Lack of aging: No representation of time-based changes.
• Limited complexity: Misses intricate, real-world dependencies.
• Absence of rare scenarios: Struggles to simulate edge cases.
• No technical debt: Fails to reflect legacy patterns and old system quirks.
• Unrealistic data: Lacks inconsistencies found in production.
• Reduced data richness: Missing the diversity of real-world interactions.
• Insufficient volume: Smaller datasets than real production environments.
• Inaccurate data distribution: Does not replicate real-world patterns.

These gaps make these synthetic data approaches unreliable for testing environments that aim to mimic production conditions accurately.

How does icaria Technology generate high-quality synthetic data?

To overcome these limitations, icaria Technology has developed a model-based synthetic data approach that ensures realistic, secure, and scalable datasets for high-quality testing environments. This approach allows us to create high-quality test data that mirrors real-world conditions without compromising security, compliance, or performance.

Advantages of icaria Technology's synthetic data

Our approach to synthetic data offers significant advantages for software testing environments. By replicating the structure, patterns, and complexity of real-world data while ensuring the exclusion of sensitive information, this method strikes a balance between realism, scalability, and security. Here are some key benefits of using our synthetic data:

• Realistic test scenarios with no privacy risks
  Maintains relationships, distributions, and behaviors from real-world datasets without exposing PII. By generating this data from pre-existing models, we ensure that test environments mirror production scenarios.
• Consistency across testing stages
  Ensures smooth transitions between development, staging, and production phases by preserving referential integrity and data relationships.
• Scalability and flexibility
  Generates large volumes of test data tailored to specific needs, supporting extensive performance and scalability tests.
• Customizable for testing requirements
  Allows the generation of datasets designed for edge cases, rare scenarios, or new application features.
• Cost efficiency
  Reduces manual effort and minimizes rework costs through automated processes, ultimately saving resources during the testing lifecycle.

When to use real-world data and when synthetic data?

After reviewing what real-world data is and our definition of synthetic data, the question arises: which one should we use in testing?

Real-world data is the best option for testing due to its richness and complexity, accurately reflecting system behavior and user interactions. Since this data already exists, it is often more efficient to use it rather than generating new datasets, which can introduce additional challenges and complexities.

However, this does not mean synthetic data has no place in a robust testing strategy. In certain situations, our synthetic data approach can be particularly useful, such as:

• When testing requires data that is not yet available in existing application environments. For instance, during new developments involving changes to the application’s data model, there will be no existing data for the new model, necessitating synthetic data generation.
• When specific datasets are rare but essential for testing. Some scenarios occur infrequently, meaning only one or two real-world examples exist. In these cases, synthetic data can generate additional instances, ensuring all testers and developers have access to the necessary data.

The perfect combination for reliable testing with icaria TDM

In the high-complexity environments managed by icaria Technology, particularly in icaria TDM, the reality is significantly more complex. These applications function in mission-critical domains where the margin for error is nonexistent.

By combining real-world data with synthetic data, organizations can create a balanced and efficient approach to test data management that ensures accuracy, compliance, and scalability.

Choosing the right type of data for each scenario, or combining both, helps companies improve test quality, comply with regulations, and optimize resources. With icaria TDM, achieving this balance has never been easier. This approach not only enhances testing efficiency but also strengthens confidence in systems, ensuring applications meet the highest quality standards before deployment.

If you have questions or want to learn more about how icaria TDM can transform your organization's test data management processes, feel free to reach out to our team!