Why synthetic data is even a topic of discussion? After all, if real data is available, why go through the effort of generating artificial alternatives? The answer lies in the limitations of real-world data.
In many cases, real data simply doesn’t exist. Perhaps because a system hasn’t been deployed yet or the necessary data is inaccessible due to regulatory or architectural constraints. Even when data is available, it may be insufficient in size, lack diversity, or suffer from imbalance, making it unsuitable for certain applications.
Whether you’re testing systems, training machine learning models, or addressing regulatory challenges, synthetic data provides a practical alternative when real data falls short.
In this article, we’ll explore what synthetic data is, how it’s generated, and some key scenarios where it proves essential.
What Is Synthetic Data?
Synthetic data is artificially generated rather than collected from real-world observations. It can be entirely random, statistically modeled to mimic real-world properties, or structured to maintain the same relationships as actual data. High-quality synthetic data closely follows the statistical patterns and interdependencies found in real datasets, so the relationships between data points remain intact. However, generating such data is a complex process, requiring careful modeling and validation to make sure it remains useful for its intended purpose.
The Growing Market for Synthetic Data
Despite its early-stage adoption, the synthetic data market is gaining momentum. The global market for Synthetic Data Generation was estimated at US$323.9 Million in 2023 and is projected to reach US$3.7 Billion by 2030. This rapid growth is driven by a mix of emerging startups and established technology vendors entering the space.
This enthusiasm is also reflected in Gartner’s Hype Cycle for Data Science and Machine Learning, where synthetic data is positioned as a technology with strong future potential. The rise of generative AI, combined with increasing regulatory pressures from laws like CCPA and GDPR, is accelerating innovation in this space.
With companies still in the proof-of-concept phase and only a handful of vendors having referenceable customer implementations, the synthetic data market is at an exciting turning point which offers significant opportunities for innovation and growth.
Is Synthetic Data Inferior to Real Data?
Skepticism around synthetic data is natural. The idea of using "fake" data can seem counterintuitive, perhaps even unreliable. After all, when we hear the word "fake," we often associate it with something inferior. So, does synthetic data fall into this category, or can it actually be better than real data?
The answer is nuanced. While synthetic data is artificially generated, it is not inherently less valuable. In fact, in many cases, it can be superior to real-world data, offering more diversity, balance, and accessibility without the constraints of privacy regulations or data scarcity. However, its effectiveness depends on the use case.
The Benefits of Synthetic Data: Privacy, Diversity, and Accessibility
Synthetic data eliminates the need to use personally identifiable information (PII), protected health information (PHI), or any other sensitive data. This is essential for enterprises that must comply with strict privacy regulations like GDPR, CCPA/CPRA, HIPAA, or other location- or industry-specific standards. By using synthetic data, you sidestep the security and privacy concerns associated with real data, and making certain that you are compliant without compromising on utility.
Similarly, synthetic data simplifies data sharing. If you can replace part or all of your real data with synthetic data, you can freely share datasets—internally or externally—without worrying about compliance issues. This opens up new possibilities for collaboration and innovation.
Another significant advantage is the ability to address data diversity challenges. Real-world data often lacks the diversity needed for robust analysis or testing. This could be due to sample selection biases, the need to test edge cases in software applications, or insufficient demographic representation. With synthetic data, you can tailor the dataset to include the diversity you need, ensuring more comprehensive and accurate outcomes.
Then there’s the issue of data scarcity. Obtaining real data for analysis or machine learning can be incredibly challenging, especially when you need massive, diverse datasets. Real data is often incomplete or insufficient, yet it’s critical for training effective models. Synthetic data fills this gap, enabling organizations to generate the volume and variety of data required for advanced applications.
What’s particularly interesting is how synthetic data levels the playing field for smaller companies. These organizations often struggle to access large, high-quality real-world datasets due to resource constraints. With synthetic data, they can compete more effectively, developing top-notch products and services without being held back by data limitations.
Top Use Cases for Synthetic Data
Synthetic data is being adopted across various industries like financial services, telecommunications, healthcare, manufacturing, government and many more for multiple purposes. While there are many potential use cases, four stand out as the most impactful:
- Software Testing – Generating high-quality, diverse test data for development and QA teams.
- Machine Learning – Creating synthetic datasets to train AI models when real-world data is scarce or costly.
- Data Sharing & Compliance – Enabling organizations to share data internally or externally while maintaining privacy and regulatory compliance.
- Training & Simulations – Using synthetic data to model rare or hypothetical scenarios for AI-driven applications.
Among these, the most demand in the market today is for software testing and machine learning, so let’s dive into those in more detail.
Synthetic Data for Software Testing
Development and QA teams need robust, high-quality test data to validate software applications. Traditionally, production data is used for testing, but this approach has limitations:
- Privacy & Compliance – Real production data often contains personally identifiable information (PII), requiring masking or anonymization. At this point, the data is already partially synthetic.
- Data Integrity – Masking data while maintaining relational integrity is complex and can introduce inconsistencies.
- Limited Test Scenarios – Production data may not cover all possible test cases, especially for new features that don’t yet exist in real-world data.
- Scenario Simulation – Testers may need to simulate specific conditions, such as data aging (e.g., expired licenses, future dates), which is difficult to achieve with production data alone.
Synthetic data solves these challenges by allowing teams to generate on-demand, compliant, and diverse datasets that improve test coverage and accelerate development cycles.
Synthetic Data for Machine Learning
Machine learning models require vast amounts of high-quality data for training. However, real-world data collection presents several challenges:
- Data Scarcity – Some scenarios (e.g., fraud detection, medical diagnoses) don’t occur frequently enough in real data to train an effective model.
- High Costs & Labor-Intensive Labeling – Annotating real-world data for supervised learning is expensive and time-consuming.
- Bias & Imbalance – Real datasets often contain biases or lack diversity, leading to skewed models.
Synthetic data helps overcome these obstacles by generating balanced, well-labeled, and diverse datasets that enhance machine learning performance.
A common concern is whether training a model on synthetic data generated by another machine learning model creates a feedback loop with no real-world value. However, AI models can benefit from synthetic data when it introduces new variations and complexities. The key is ensuring the synthetic data generation process is robust, diverse, and complementary to real-world data.
Approaches to Synthetic Data Generation: Techniques and Methods
The following methods represent the most prevalent approaches to synthetic data generation. Each has its place, depending on the use case, the level of realism required, and the constraints of the project.
Data Masking and Transformation
The most prevalent method involves taking real data and transforming it to mask sensitive information while preserving its utility. You start with your real dataset, and then modify it as needed to meet specific requirements, such as privacy or diversity. This approach can range from simple masking to more complex transformations. If taken to the extreme, this method can even be considered rule-based data manufacturing, where the dataset is entirely randomly generated but maintains consistency with the original data’s structure and relationships.
Data Cloning
Data cloning is another practical technique, particularly useful when you need to scale up a dataset for testing purposes. For example, if you’re load-testing a system or evaluating how a UI element performs under large datasets, you can clone a real dataset repeatedly. Each iteration applies new data masking, creating multiple copies of the same dataset with slight variations. This allows you to simulate larger datasets without compromising the integrity of the original data.
Statistical Modeling
Statistical modeling has been a cornerstone of synthetic data generation for a long time. This approach uses statistical techniques to create data that mimics the patterns and distributions of real-world datasets. While traditional statistical methods are still widely used, they’ve been complemented, and in some cases, surpassed by more advanced techniques.
Generative AI
Generative AI is the hot new topic in synthetic data generation. Tools like GPT, Gemini, DeepSeek and other generative models take real-world data and use it to train AI systems. Instead of directly using the original data, these models generate entirely new datasets that retain the relationships and behaviors of the original fields. The beauty of this approach is that the AI captures these relationships automatically so you don’t need to explicitly define them. This makes generative AI a powerful tool for creating highly realistic synthetic data at scale.
Matching Techniques to Scenarios
Ultimately, the choice of technique depends on your use case. Rule-based systems excel when no reference data exists, while masking and generative AI are ideal for scenarios involving sensitive data or machine learning. For load testing, cloning and rule-based approaches provide the scalability you need. By understanding these options, you can select the right approach for your organization’s unique challenges.
The Pitfalls of Synthetic Data
To succeed with synthetic data, you need to carefully consider the trade-offs: the benefits it brings versus the potential pitfalls. Synthetic data is not real-world data, and treating it as a one-size-fits-all solution can lead to unwanted surprises if not approached carefully.
Accuracy and Drift
Synthetic data can be inaccurate, especially if the generation techniques are inappropriate or the underlying real-world data is insufficient. If your sample of real data is too small or unrepresentative, the synthetic data may "drift" from the original intent, resulting in datasets that don’t accurately reflect the real-world scenario. This is particularly true with generative AI models, which, much like GPT, can produce "hallucinations", plausible-sounding but incorrect outputs. The challenge lies in ensuring the synthetic data is good enough for your specific use case.
Complexity of Data Relationships
The more complex your data relationships are, the harder it becomes to maintain them in synthetic data. Current techniques struggle to accurately replicate intricate relationships, especially when dealing with high-dimensional data or datasets with numerous interdependencies. If these relationships aren’t preserved, the synthetic data may fail to deliver meaningful insights.
Time Sensitivity
Synthetic data isn’t always well-suited for time-sensitive information. Real-world data often evolves rapidly, and synthetic data generation processes may not keep pace with these changes. This can lead to datasets that feel outdated or irrelevant, particularly in fast-moving industries.
Resource Intensity
Generating high-quality synthetic data is resource-intensive. Whether you’re using machine learning techniques or rule-based approaches, the process requires significant time and effort. You need to validate the dataset so it’s accurate and representative, which can be a time-consuming task. Additionally, creating and maintaining the rules for synthetic data generation, adds another layer of complexity.
Validation Challenges
Validation is a critical step in any synthetic data project. You must rigorously test the synthetic dataset so it behaves like the real-world data it’s meant to replicate. This often involves comparing synthetic and real data across multiple dimensions, which can be both technically challenging and labor-intensive.
Recommendations for Implementing Synthetic Data Generation in Your Organization
Implementing synthetic data generation requires careful planning and execution. Here are some key recommendations to help you get started:
Identify Gaps and Opportunities
Begin by identifying areas in your organization where data is missing, incomplete, or expensive to obtain. Synthetic data shines in scenarios where real data is unavailable, insufficient, or restricted by privacy regulations. However, it’s not always an all-or-nothing approach. Consider a hybrid model where you merge real data with synthetic data. For example, you can replace sensitive PII with synthetic data while keeping the rest of the dataset intact. This approach balances realism with compliance.
Tailor Solutions to Use Cases
We saw that software application testing is a primary use case for many organizations. Synthetic data is particularly valuable here, as it allows you to simulate edge cases or scenarios that don’t exist in production data. Whether you’re testing for performance, scalability, or edge cases, synthetic data can fill the gaps where real data falls short. Evaluate your testing needs and determine the right balance of real and synthetic data for your specific use cases.
Educate Stakeholders
Not everyone is immediately convinced of the value of synthetic data. Some may view it as “fake” or inferior to real data. To overcome this, educate internal stakeholders about the benefits and limitations of synthetic data. Highlight the business value it can deliver, such as enabling compliance, reducing costs, and accelerating innovation. A well-informed team is more likely to embrace synthetic data as a strategic tool.
Evaluate Vendors Through Proof of Concept (POC)
If you’re considering multiple vendors, don’t rely solely on their claims. Conduct a proof of concept (POC) to verify their capabilities. Test how well their solutions generate realistic synthetic datasets that match your production data. Pay attention to the entire data lifecycle, from sourcing and generation to validation and deployment. Choose vendors that can deliver high-quality synthetic data while managing the complexities of the data lifecycle.
Prioritize Accuracy and Privacy
The most critical differentiators for tabular synthetic data generation platforms are accuracy and privacy. Any solution you choose must generate highly accurate datasets that closely mimic your real-world data. At the same time, verify that the platform maintains strict privacy controls, so sensitive data is never exposed. These two factors are non-negotiable for successful synthetic data implementation.
Final Thoughts
Synthetic data is a powerful tool, but its success depends on how well it’s implemented. By identifying the right use cases, educating stakeholders, and carefully evaluating vendors, you can unlock the full potential of synthetic data for your organization. Remember, the goal isn’t to replace real data entirely but to complement it in ways that drive innovation, compliance, and efficiency.
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