Synthetic data - Beginners Guide 2025

By 2026, 75% of businesses are expected to use generative AI to create synthetic customer data—a massive increase from less than 5% in 2023.

Synthetic data empowers organizations to:

• Simulate real-world environments for product development and prototyping.

• Innovate in regulated industries without breaching privacy laws.

• Accelerate time-to-market with fast prototyping for software, digital products, and hybrid experiences.

For industries facing data scarcity or regulatory constraints, synthetic data is a vital tool for testing and refining strategies while minimizing risks and unlocking new opportunities.

What is synthetic data?

Synthetic data is artificially generated data created using real data and a trained model that replicates the patterns and structure of the original data. It is designed to produce similar results to the original data when analyzed.

The process of creating synthetic data, called synthesis, uses various techniques such as decision trees or deep learning algorithms. For example, Generative Adversarial Networks (GANs)—a method often used in image recognition—use two neural networks that work together: one generates synthetic images, and the other tries to differentiate them from real ones. This iterative process creates highly realistic data.

Synthetic data can be categorized into three types based on its source:

1. From real datasets – Built directly using existing data.

2. From expert knowledge – Based on insights and rules defined by analysts.

3. Hybrid – A mix of both real data and expert input.

Synthetic data is valuable because collecting real-world data can be challenging, expensive, and time-consuming. By contrast, synthetic data can be generated quickly, at scale, and tailored to specific needs. It is widely used in machine learning to train models that require large amounts of labeled data, especially when real data is limited or restricted by privacy regulations. It’s also useful for software testing, quality assurance, and transfer learning (pre-training machine learning models on synthetic data before using real data).

Privacy assurance is a critical step in synthetic data creation. This ensures that the synthetic data does not inadvertently reveal personal information from the original dataset. The process evaluates how well the synthetic data protects individual identities and prevents the leakage of sensitive details.

With its ability to provide customized, scalable, and privacy-safe alternatives to real-world data, synthetic data is rapidly gaining popularity in machine learning and other data-driven fields.

What are use cases for synthetic data?

There are tremendous use cases for synthetic data. In this article, we’ll focus more on the marketing and business aspects. Synthetic data can be used to better understand your audience, including their purchasing power, preferences, desires, emotions, and sentiments.

It can also reveal what factors they consider before making a decision, their struggles, the type of content they consume, and when and how often they engage with it, among other insights. The examples within marketing alone are nearly infinite, limited only by the creativity of the analyst or data modeler.

Real life examples of synthetic data projects 

Enäks Market Intelligence uses synthetic data to better understand their customers and develop messaging that resonates more effectively with their audience, focusing on emotional impact and triggers. Enäks Market Intelligence has made one of their audience simulators public to help professionals in the SaaS industry gain deeper insights into the preferences, opinions, and sentiments of their audience. The simulator, called SaaSy by Enäks, generates synthetic data that simulates real C-level managers in the SaaS industry with 96% precision. Marketers can leverage these insights to better serve their customers’ needs. This synthetic data is publicly available and free to use.

Erste Bank used synthetic test data to develop, and build their mobile banking app. The synthetic data used by Erste Bank helped the company develop a mobile banking app that actually resonates with their customer needs and preferences making the app more engaging from a user perspective since they had the chance to anticipate any potential blockers or bad UX/UI that might have happened in real time once they app were launched. Also, Erste Bank saved a lot of money, time and energy by using synthetic data and consumer insights instead of actually conducting user research. This synthetic data is private and not available to the public. 

JPMorgan leverages a synthetic data sandbox to speed up data-intensive proof-of-concept (POC) projects with third-party vendors, enabling faster experimentation, validation, and development while ensuring compliance with privacy regulations. This synthetic data is private and not available to the public. 

Anthem uses synthetic data to detect fraud by simulating real-world scenarios to identify anomalies, suspicious patterns, and potential risks. This helps improve the accuracy and efficiency of fraud detection, reducing false positives and ensuring quicker response times. Additionally, Anthem leverages synthetic data to enhance personalized service for members by generating realistic insights into individual behaviors, preferences, and needs. This allows for more targeted communication, tailored recommendations, and improved customer experiences, ultimately driving higher satisfaction and engagement. This synthetic data is private and not available to the public. 

Key Methods for Generating Synthetic Data

1. Generative AI-Based Methods

Generative AI models use real data as training input to create realistic synthetic data that retains statistical properties of the original dataset. These methods are particularly effective for complex data types such as images, text, and tabular data.

• Generative Adversarial Networks (GANs): Two neural networks compete against each other: a generator creates synthetic data, while a discriminator evaluates its authenticity. Over time, the generator produces highly realistic data.

Example: GANs can create lifelike images for computer vision applications or simulate customer purchase behaviors.

Pros: Excellent for high-dimensional data like images and videos; can produce highly realistic samples.

Cons: Requires extensive computational resources and large datasets; prone to instability during training.

• Variational Autoencoders (VAEs): These models learn compressed representations of data and decode them into realistic outputs. VAEs are ideal for generating text or structured data.

Example: VAEs can be used to generate synthetic medical records for research purposes.

Pros: Produces diverse outputs; more stable than GANs.

Cons: Results may lack the fine-grained realism achieved by GANs.

• Transformer Models: Pre-trained AI models like GPT-3 or GPT-4 can generate structured text-based synthetic data. These models excel in tasks requiring language generation or understanding.

Example: SaaSy, an audience simulator by Enäks, uses transformer-based AI to model C-level SaaS decision-makers, generating synthetic customer insights with 96% precision.

Pros: Effective for natural language data; easy to fine-tune for specific tasks.

Cons: May require significant pre-training and fine-tuning efforts.

2. Rule-Based Synthesis

Rule-based synthesis involves generating data based on predefined rules and domain knowledge. It ensures consistency by modeling relationships between data points.

• Mock Data Generators: These tools create datasets based on user-defined schemas, often used for testing software or prototypes.

Example: A mock data generator could create fake e-commerce order histories for testing a CRM system.

Pros: Easy to implement; ensures compliance with business rules.

Cons: Lacks variability and statistical realism unless complex rules are defined.

3. Random Sampling

This method generates data points by randomly selecting values within predefined ranges or distributions. It is commonly used for quick and simple datasets.

Example: Creating synthetic user ages between 18 and 80 for a demographic study.

Pros: Fast and simple; requires minimal resources.

Cons: Data lacks meaningful relationships and realism.

4. Statistical Modeling

Statistical models generate synthetic data by leveraging probability distributions to mimic real-world patterns.

• Normal Distribution: Used for generating data that clusters around a mean value, such as sales figures.

Example: Modeling average monthly revenue for a retail business.

• Exponential Distribution: Useful for scenarios like modeling customer churn rates.

Example: Generating synthetic data on time-to-customer-drop-off in a subscription service.

Pros: Produces statistically meaningful data; maintains underlying patterns.

Cons: May not capture complex relationships in real-world data.

5. Simulation-Based Approaches

Simulation-based methods replicate real-world processes or systems to generate synthetic data. These approaches are often used for system design or predictive analytics.

Example: Simulating website traffic patterns to predict user behavior.

Pros: Captures dynamic interactions and real-world complexity.

Cons: Computationally intensive and time-consuming.

6. Entity Cloning and Data Masking

This approach focuses on anonymizing or transforming real-world data while preserving its structural integrity.

• Entity Cloning: Involves copying real data, anonymizing it, and adding unique identifiers.

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Example: Creating synthetic customer profiles for testing a loyalty program.

• Data Masking: Replaces personally identifiable information (PII) with fictitious but structurally consistent values.

Example: Masking customer names and emails in CRM datasets.

Pros: Maintains data realism; ensures privacy.

Cons: Relies on existing datasets; not ideal for generating entirely new data.

7. Time-Series Generation

Time-series data synthesis involves generating sequences of data points over time, often used in forecasting and trend analysis.

• Models: Techniques like ARIMA or LSTM (Long Short-Term Memory networks) are commonly used.

  
  

Example: Generating synthetic stock market trends for financial modeling.

Pros: Captures temporal dependencies and trends.

Cons: Requires specialized models; computationally demanding for large datasets.

8. Copula Models

Copulas capture dependencies between variables, enabling the creation of correlated synthetic datasets.

Example: Generating synthetic datasets that model customer demographics and their purchasing habits.

Pros: Effective for preserving variable relationships; flexible in handling diverse data types.

Cons: Requires advanced statistical knowledge; can be complex to implement.

9. Data Augmentation

This technique enhances existing datasets by applying transformations, making it especially useful for image or video-based synthetic data generation.

• Common Techniques: Rotation, scaling, flipping, or injecting noise into the data.

Example: Augmenting medical imaging datasets to train diagnostic AI systems.

Pros: Improves model robustness by diversifying training data.

Cons: Limited to enhancing existing data; cannot generate new patterns.

Benefits of Synthetic Data

1. Customizable Data for Any Scenario

Synthetic data can be tailored to meet specific needs that real-world data might not address. For example, businesses can generate datasets for rare conditions or edge cases, like testing how software handles unusual user behavior. It's perfect for customizing data to fit unique requirements without the constraints of availability or privacy concerns.

Benefits:

• Easily adapt data to specific use cases.

• Fill gaps in scenarios where real data isn’t available or feasible.

Example: A healthcare startup creates synthetic patient data to test a diagnostic AI for rare diseases, ensuring robust testing.

2. Cost-Effective Data Solutions

Generating synthetic data is much cheaper than collecting real-world data, especially in industries like automotive or healthcare, where acquiring real data can be prohibitively expensive. For instance, simulating vehicle crashes is far more economical than physically testing crashes.

Benefits:

• Reduces the high costs associated with collecting real-world data.

• Saves money in data-intensive industries.

Example: An automaker generates crash-test data through simulations, cutting research costs.

3. Faster Data Production

Synthetic data can be generated quickly using the right tools, bypassing the delays of traditional data collection. This speed enables faster prototyping, development, and decision-making, allowing businesses to stay agile.

Benefits:

• Accelerates data availability.

• Shortens development cycles for AI and analytics projects.

Example: A retail company uses synthetic customer data to simulate holiday shopping behaviors, enabling faster marketing campaign adjustments.

4. Enhanced Privacy and Compliance

Synthetic data mimics real-world data without revealing sensitive details, making it a privacy-friendly solution. Industries like healthcare and finance benefit by complying with strict regulations while maintaining data utility for analysis and innovation.

Benefits:

• Ensures data privacy and anonymity.

• Supports compliance with privacy laws like GDPR and HIPAA.

Example: A bank uses synthetic transaction data to train fraud-detection models without exposing customer information.

5. Data Diversity for Robust Models

Synthetic data allows businesses to create diverse datasets, covering edge cases and rare events that real data may lack. This diversity leads to more comprehensive training for machine learning models and improved performance in complex scenarios.

Benefits:

• Expands data coverage for rare events or edge cases.

• Builds more robust and reliable AI models.

Example: A self-driving car company simulates diverse road conditions, such as fog, heavy rain, and unexpected pedestrian behavior, to train its AI.

6. Complete Control Over Data

With synthetic data, businesses have full control over dataset features. They can manipulate variables, event frequency, and data structures to match their exact needs, ensuring consistency and precision.

Benefits:

• Enables fine-tuning of data properties.

• Provides flexibility for model training and testing.

Example: A logistics company simulates shipping data to optimize delivery routes under varying conditions like high traffic or extreme weather.

7. Fully Annotated Datasets

Synthetic data often comes with perfect labeling, eliminating the need for time-consuming manual annotation. This is particularly beneficial in machine learning tasks where accurate labels are crucial for training effective models.

Benefits:

• Saves time on manual labeling.

• Improves accuracy and consistency in annotations.

Example: An AI company generates synthetic images of damaged goods with precise annotations for automated quality control systems.

8. Flexible Data Augmentation

Synthetic data enhances existing datasets through augmentation techniques, like adding noise or transformations. This flexibility helps correct biases, balance classes, or increase dataset size without additional data collection.

Benefits:

• Balances datasets for unbiased AI models.

• Scales datasets effortlessly.

Example: An e-commerce platform augments customer review data to include sentiment variations, improving sentiment analysis algorithms.

9. Facilitates Collaboration Across Industries

Synthetic data enables safe data sharing between organizations without compromising privacy. This fosters collaboration and innovation, especially in regulated industries like pharmaceuticals or finance.

Benefits:

• Encourages cross-industry innovation.

• Breaks down silos while ensuring privacy.

Example: Two pharmaceutical companies collaborate by sharing synthetic patient data to co-develop a new drug while maintaining confidentiality.

10. Improved Agility and Efficiency

Synthetic data reduces the bureaucracy and delays associated with accessing and preparing real data. Data scientists and analysts can access what they need instantly, focusing more on value creation than data preparation.

Benefits:

• Speeds up workflows.

• Frees up resources for strategic tasks.

Example: A marketing team uses synthetic customer profiles to test ad campaigns, reducing their campaign setup time.

Synthetic Data in Marketing

Synthetic data is reshaping the landscape of marketing, and at Enäks, we are at the forefront of this transformation. While the use of synthetic data in marketing is still evolving, we've harnessed its potential to empower our clients with innovative strategies that drive results. Let's explore how brands are leveraging synthetic data and how Enäks is embracing these techniques to revolutionize marketing.

1. Customer Behavior Modeling

Brands, particularly in e-commerce and retail, have begun using synthetic data to model customer behavior. This approach helps predict future trends and preferences, boosting customer satisfaction and sales. For instance, a leading retailer used synthetic data to create a Digital Twin of its store and predict shopper behaviors at checkout lines.

2. Market Simulation

Synthetic data enables companies to simulate market conditions and assess potential changes in consumer behavior without exposing sensitive information. Financial services and retail industries have already applied this to test scenarios like economic shifts or competitive pricing.

3. A/B Testing and Optimization

Synthetic data enhances A/B testing by enabling brands to explore a wider range of strategies. By augmenting real-world data, marketers can test different campaign elements more effectively and at scale.

4. Ad Performance and Placement

Optimizing ad performance through synthetic data is gaining traction in digital marketing. By simulating audience reactions, brands can refine targeting, placements, and messaging to maximize ROI.

How Enäks is Driving Innovation with Synthetic Data

At Enäks, synthetic data is central to our mission of making marketing smarter, faster, and more effective. We use synthetic data to simulate market conditions, model customer behaviors, and optimize marketing strategies without compromising privacy. Our solutions empower marketers to test ideas, understand audience preferences, and execute data-driven campaigns with precision.

By embracing synthetic data, we’re enabling businesses to unlock insights, improve decision-making, and stay ahead in an ever-evolving market landscape.