Getting started with synthetic data generation: Tools, techniques, and best practices

Companies today are turning to synthetic data generation quite quickly as a strategy to innovate using data while addressing privacy or security constraints. Simply put, synthetic data is artificial data generated in imitation of the patterns and structures of real-world data without exposing any actual sensitive details. Thus, organizations, instead of planning to collect data from potentially costly or regulated real-world sources, can produce high-quality datasets on demand in an algorithmic way. This approach helps businesses sidestep issues like limited sample sizes, compliance hurdles, and lengthy data acquisition cycles. The result is a process that accelerates development and experimentation, powering use cases from advanced analytics to machine learning model training.

Traditionally, gathering enough diverse, accurate data for projects has been a major challenge. Whether it’s a bank needing millions of transaction records to improve fraud detection or a healthcare startup requiring patient data to train diagnostic algorithms, obtaining real data at scale can be difficult, expensive, and fraught with privacy concerns. This approach offers a compelling solution: it produces “fake” data that maintains the statistical realism of production data while removing personally identifiable information (PII). For example, financial services firm J.P. Morgan found that genuine fraud cases were too scarce to effectively train their AI models; by generating additional fraudulent transaction examples synthetically, they significantly enhanced model performance. Across industries, similar stories are emerging, and Gartner even predicts that by 2026, 75% of enterprises will use generative AI to create synthetic customer data. These trends underline why this approach is attracting so much attention in the business world.

A magnifying glass focusing on the letters “AI”, symbolizing how enterprises are closely examining artificial data as a key asset. Synthetic datasets allow companies to zoom in on insights without exposing real sensitive information.

Techniques for synthetic data generation

Conceptual illustration of multiple approaches to generating synthetic data: AI-driven generative models (center) can be combined with rules engines, masking, or cloning methods to produce realistic, privacy-compliant synthetic data. For example, an AI model might generate raw synthetic records which are then refined by rule-based checks or masked to remove any sensitive traces. In practice, finding the right mix of methods often yields the best results.

There are several techniques and methodologies by which synthetic datasets can be produced. Choosing the right approach depends on the type of data needed and the use case. Here we overview the most common techniques for generating synthetic data that enterprises employ:

• Statistical modeling and simulation:

  One fundamental approach to creating synthetic data is using statistical methods and simulations. Data scientists analyze the statistical properties of real datasets—such as distributions, correlations, and frequencies—and then generate new samples that mirror those properties. For example, Monte Carlo simulations can create realistic financial market data by sampling from probability distributions, and epidemiological simulators can produce synthetic patient health records by modeling disease progression. By tuning parameters and injecting random noise, statistical modeling can yield artificial datasets that resemble real-world patterns without duplicating any actual records. Simulation environments (such as virtual driving ranges for autonomous vehicle testing data) also fall into this category, producing synthetic data by replicating real-life processes in a controlled, repeatable way.

• Generative machine learning models:

  Progress in AI has definitely led to the emergence of very energetic models for generating synthetic data. Different energy models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) learn different types of data from real data, then generate new examples, which cannot be distinguished statistically from the originals. For instance, in GANs, one neural network (the generator) is trying to generate fake data-either images, text, or tabular data-while another (the discriminator) tries to distinguish fake data from the real data. Both learn in this adversarial way. The result can be highly realistic synthetic outputs, such as images of fictional people or sensor data for scenarios that never actually occurred. Likewise, transformer-based models (like GPT) can produce synthetic text or even tabular data by learning the structure of existing datasets.

  These generative AI methods have been successfully used for everything from augmenting training data for computer vision (creating diverse images for object recognition) to generating synthetic speech and text for virtual assistants. The key advantage is that generative models can capture complex patterns and high-dimensional relationships in data, making them ideal when simple statistical sampling falls short.

• Rules-Based Data Generation:

  Not all synthetic data creation requires complex AI models; sometimes, enterprises use rule-based engines and scripting to generate artificial data. In this approach, domain experts define rules, constraints, and distributions for each data field based on business logic or known patterns. The rules engine then produces data records that conform to these specifications. For example, an e-commerce company might synthetically generate customer orders by following rules about product combinations, realistic price ranges, and purchase frequencies.

  Similarly, telecom companies might create fake network traffic logs by applying rules for time of day, location, and event types. Rules-based generation ensures the synthetic data is plausible and internally consistent (for instance, ensuring date fields are in valid ranges or that addresses match geographic coordinates). It’s essentially a manual or semi-automated approach to generating synthetic data, allowing fine-grained control over the output. This method is very useful for software testing and prototyping when specific scenarios need to be covered.

• Data augmentation and anonymization:

  A closely related practice to generating entirely new data is augmenting or transforming existing data to create “synthetic” variants. Data augmentation is commonly used in fields like image recognition—e.g., flipping or rotating images to create new training samples—but it also applies to structured data. An enterprise might take a real dataset and introduce modifications: adding noise, shuffling values, or combining parts of different records. The line between augmentation and pure synthetic data creation can blur, but the goal is the same: produce additional artificial data that expands the original dataset without breaching privacy.

  Meanwhile, data anonymization techniques like masking also contribute to safe synthetic datasets. By replacing sensitive values (names, Social Security numbers, etc.) with fictitious but realistic alternatives, companies can generate a synthetic version of a database that preserves the utility of the data. For instance, a bank could release a synthetic dataset of transactions where account numbers and personal details are entirely artificial, yet spending patterns and correlations remain intact. This way, analysts can experiment on data with the statistical richness of real records but none of the confidential information. In essence, it achieves the core promise of synthetic data generation – preserving data utility without exposing real data.

Each of these techniques plays a role in enterprise synthetic data generation, and often they are used in combination. For example, a project might start by extracting real data characteristics via statistical analysis, use a generative model to produce a base synthetic dataset, then apply rule-based adjustments or masking for additional realism and privacy. The next step is equipping teams with the right tools to implement these techniques efficiently.

Tools and platforms for synthetic data generation

As interest in this approach grows, a variety of tools and platforms have emerged to help organizations create and manage synthetic datasets. These range from open-source libraries for developers to enterprise-grade platforms with user-friendly interfaces. Below we review some widely used options:

• Mostly AI:

  A well-established synthetic data platform often cited in industries like finance, telecom, and healthcare, Mostly AI uses AI-driven simulation to generate structured data that closely mimics real datasets. It emphasizes privacy by design—ensuring that outputs comply with regulations such as GDPR—and even allows users to query synthetic data via natural language. (Notably, Gartner recognized Mostly AI as a “Cool Vendor” for its innovative approach.)

• Gretel.ai:

  Users can easily access synthetic data, and Gretel facilitates the Grand Unified Theory of convenience concerning the means by which synthetic data can be created and dealt with gradually by designers without much programming experience. Substantial data tools are offered by Gretel while generating synthetic text, tabular, and even time-series data. No less amusing is the presence of APIs, and the integrations make it programmatically convenient with conventional data pipelines. In respect of its simplicity and automation, any data analyst can very swiftly run the synthetic dataset for any machine learning experiments or sharing data, thereby enabling, to a great extent, the democratization of synthetic data generation for non-primitive groups.

• Tonic:

  Tonic.ai gives development teams a practical way to work with data that looks and behaves like the real thing, without putting sensitive information at risk. Instead of relying on production databases for testing, teams can use Tonic to create synthetic versions that closely reflect real-world complexity, including relationships between tables and other constraints. It also includes tools to mask or de-identify actual data when needed, making it possible to safely blend real and generated information. With flexible deployment options (whether on your own servers or in the cloud) and compatibility with a wide range of databases, Tonic has become a go-to solution for companies that need safe, high-quality test data without compromising on realism.

• Synthetic Data Vault (SDV):

  Developed by the Data to AI Lab at MIT, the Synthetic Data Vault (SDV) has become a popular tool among data scientists exploring synthetic data solutions. It’s an open-source library packed with advanced models built specifically for generating different types of data, whether it’s tabular, time-based, or relational. For instance, its tabular feature can analyze the structure of a dataset and create new, synthetic rows that still reflect the original patterns and relationships between columns. Since it’s open source, SDV is highly adaptable and benefits from constant contributions by the research community. That makes it a useful environment for testing and applying synthetic data methods in real-world business scenarios.

• Synthea:

  Synthea is a free, open-source program built for healthcare teams that need access to lifelike patient data without running into privacy issues. Instead of using real medical records, it creates entirely fictional patients—complete with illnesses, medications, test results, and treatments—based on clinical guidelines and public health trends. Researchers and healthcare companies often turn to it when they need large datasets to train AI tools or test electronic health systems. Since the data is synthetic, there’s no risk of exposing anyone’s personal information, making it a safe and practical solution for a wide range of healthcare projects.

• Faker:

  Faker is a lightweight tool that developers often rely on when they need to whip up fake data fast. Available in languages like Python and JavaScript, it helps generate everything from names and email addresses to company names and random values. It’s not built for complex data modeling, but that’s not really the point—it shines when you need a quick, realistic-looking dataset for demos, testing, or filling out a mock database. Whether you’re simulating thousands of customer profiles or just need some dummy data for a project, Faker gets the job done without much fuss.

There’s actually a whole range of other tools worth mentioning. Hazy is one offering synthetic datasets through a marketplace setup. Then you’ve got simulation tools that work with engines like Unity to create driving scenes for training autonomous vehicles. In cybersecurity, too, there are purpose-built generators that help simulate attacks and defenses without touching sensitive data.

Read more: Dancing with the data: AI tools that empower you to make decisions with finesse

People tend to mix and match based on what they need. If flexibility is a priority, something open-source like SDV or Faker does the trick. They’re free, adaptable, and have active user bases contributing improvements regularly. For teams looking for a smoother experience, commercial platforms like Gretel or Mostly AI often appeal more. They come with interfaces that are easier to use and typically include some level of customer support.

In practice, a lot of companies start out with open tools while testing ideas. Once things start scaling, or when production environments come into the picture, they move to more full-featured solutions. At the end of the day, picking the right tool comes down to the kind of data you’re working with—whether that’s images, structured data, or plain text—how much you plan to scale, and how critical privacy features are for your use case.

Best practices for implementing synthetic data generation in enterprises

Before getting started, take a step back and think about what you’re actually trying to do. Is it about putting a new system through its paces? Maybe you want to train a model but can’t use real data for privacy reasons. Or perhaps you’re looking for a safe way to hand off data to a partner. Whatever the case, having a clear goal upfront helps. It keeps the team aligned and gives you something solid to measure progress against.

• Define clear objectives:

  Before diving in, ask yourself: What problem are you solving? Are you trying to stress-test a new platform, train a model without risking sensitive data, or share datasets safely with an outside team? Setting specific goals upfront keeps everyone on the same page and makes it easier to track whether your efforts are paying off. It also ensures the synthetic data you create actually supports the business.

• Ensure data quality and preparation:

  The old saying “garbage in, garbage out” holds true here. If you’re basing synthetic data on real datasets, make sure that the original data is clean. That means no duplicates, missing fields, or lingering errors. It also helps to include edge cases so your synthetic data doesn’t miss rare but important patterns. Since synthetic data mimics the source, flaws in the original will carry over. A strong input means better, more dependable output.

• Diversify and mitigate bias:

  One common pitfall? Assuming your synthetic data is automatically fair. If your real-world dataset leans heavily in one direction—say, a particular age group or geographic region—your synthetic version might do the same. To avoid that, mix in a variety of data sources or tweak the generation process to include different segments. If you’re simulating customers, for example, make sure to reflect a range of behaviors and backgrounds. The more balanced your dataset, the more trustworthy your analytics or AI models will be.

• Choose the right technique and tool for the job:

  Not every use case demands advanced AI models. If you’re just creating sample data for a form or mock app, a lightweight library like Faker will probably do the trick. But for more complex needs—like financial forecasting, medical records, or image generation—you’ll want tools that are up to the challenge. That might mean using deep learning models or a platform like Mostly AI or SDV. Also consider how easily the tool fits into your current systems and whether it can scale with your needs.

• Validate synthetic data quality:

  Once the data is generated, don’t just assume it’s good—check it. First, see how closely it mirrors the original dataset. Look at things like distribution, correlations, and summary stats. Then test how useful it actually is: for example, train a model on synthetic data and validate it with real-world test cases. If the results are wildly different, something’s off. Keep in mind that relying only on synthetic data for too long can lead to model drift, where accuracy starts to drop because the model’s lost touch with real-world patterns.

• Safeguard privacy and compliance:

  Even though synthetic data isn’t real, you still need to treat it carefully. It’s possible, in some cases, for bits of real data to slip through or be inferred. That’s why it’s smart to run privacy checks and apply safeguards like differential privacy when needed. Follow the same security protocols you’d use with real data, and make sure your workflows are aligned with legal requirements like GDPR or HIPAA. The point of synthetic data is to avoid risk, not create new ones.

• Document and iterate:

  One thing that helps in the long run? Good documentation. Note what real data you used, what models you applied, and how you validated the output. That way, if anything needs auditing—or adjusting—you have a record. Keep an eye on how your teams are using synthetic data, and ask for feedback. If it’s falling short in certain areas or not keeping pace with new patterns in the real world, you’ll know it’s time for an update. Like any data process, it gets better with review and iteration.

By following these best practices, enterprises can maximize the benefits of synthetic data generation while minimizing risks. It ensures that synthetic data projects remain aligned with business objectives, ethically sound, and technically effective.

Case study: Anthem’s synthetic data journey

Here’s a real-world case worth mentioning. Anthem, a major health insurance provider in the U.S., ran into a challenge that’s pretty common in healthcare: figuring out how to catch fake claims without risking patient privacy. Back in 2022, their CIO talked about teaming up with Google Cloud to build a synthetic data setup that could help with both issues. The goal? Create a system that could generate tons of patient-like data—enough to mimic actual medical records and insurance claims—without exposing anything sensitive. Crucially, none of this data would correspond to real individuals, yet it would maintain the realistic patterns and complexities needed to train AI systems.

Scaling fraud detection while safeguarding privacy

The synthetic data platform enabled Anthem to train and validate machine learning models for fraud detection far more effectively. Previously, one limiting factor in fighting fraud was the scarcity of known fraudulent examples (healthcare fraud is relatively rare compared to the vast number of legitimate claims). By creating a massive volume of varied but realistic fraudulent claim data via synthetic data generation, Anthem could feed its AI detection systems with plenty of “training fuel” without waiting to accumulate years of actual fraud cases.

According to reports, the result was scalable and efficient claims monitoring processes that could catch suspicious activities with greater accuracy. Anthem also leveraged the synthetic data to personalize services for members — for example, by simulating diverse patient profiles and care journeys, they could develop more tailored health programs. Previously, teams struggled to fight fraud because they lacked enough known fraudulent examples, as legitimate healthcare claims far outnumber fraud cases.

This case study highlights how a large enterprise successfully integrated synthetic data generation into its operations. Anthem’s experience demonstrates the value of collaborating with technology partners (in this case, leveraging Google Cloud’s infrastructure and AI expertise) and the importance of scale, generating petabyte-scale data to solve enterprise-scale problems. It also underscores best practices in action: clear objectives (fraud detection and personalization), a combination of techniques (statistical models to mimic health data), rigorous privacy safeguards, and validation of outcomes (improved fraud detection metrics). Many other organizations, from banks to automotive companies, are following suit and exploring synthetic data generation initiatives to drive innovation without compromising compliance.

Conclusion and future outlook

Synthetic data generation is rapidly becoming a standard tool in the enterprise data toolkit. It is no longer a niche concept and is rapidly moving into mainstream practice as organizations recognize its value. While synthetic data is here and now, the technology and methods behind it are rapidly maturing. We can expect future advancements to make synthetic datasets even more realistic and tailored to specific needs. In fact, experts anticipate that within a few years the quality and utility of synthetic data will approach that of actual data, closing the gap such that AI models might treat both interchangeably.

The business impact of this trend is significant. Imagine faster development cycles because teams no longer wait on data provisioning, or improved machine learning models because they trained on a virtually unlimited pool of scenario-specific data. Those advantages are why analysts predict dramatic growth in adoption. For example, Gartner forecasts that by 2026, 75% of enterprises will use generative AI to create synthetic customer data. From improving customer experiences with better personalization models to safeguarding compliance in data collaborations, synthetic data will underpin many next-generation solutions.

In summary, organizations should approach synthetic data generation not just as a technical task but as a strategic initiative. Enterprises should approach it with a mix of the right tools, robust techniques, and sound best practices, as outlined above. With these in place, organizations can unlock new possibilities—innovating with data at scale and speed, all while keeping ethical and legal considerations in check. Synthetic data is not science fiction; it’s here and now, helping businesses derive real value from data that isn’t “real.” Those who master synthetic data generation today will lead the data-driven economy of tomorrow as the future unfolds.