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Exploring Synthetic Data: Advantages and Use Cases

Read this guide to discover the benefits and applications of synthetic data and how to apply synthetic test data to your business operations.

In today's data-driven world, businesses of all sizes rely on data to make informed decisions, drive growth, and stay competitive. But sometimes, we don’t have access to the exact real-world datasets we need in order to generate data-driven decisions.

This creates the need for data that is artificially generated but simulates real-world events and patterns, allowing us to have the information and predictive modeling. One increasingly popular approach to leveraging data is through the use of synthetic data.

But what is synthetic data, and how can it benefit your business?

This guide will cover the meaning of synthetic data, the advantages of using synthetic data for your business, and more. Continue reading to learn more about how you can apply synthetic data to your business processes.

What is synthetic data?

Synthetic data is computer-generated data that mimics the characteristics of real-world data. Instead of using authentic data collected from real sources, synthetic data is created using computer simulations. This approach is typically used when real data isn't available or when it's kept private due to data protection laws.

The concept of artificial data has gained significant traction in recent years, with many industries recognizing its potential to revolutionize data-driven decision-making.

Real-world data is collected from authentic sources, such as customer interactions, sensor readings, or financial transactions. While this type of data is valuable for analysis and insights, it can also be challenging to acquire and manage due to privacy concerns, cost, and other constraints. In contrast, synthetic test data can be generated on demand, allowing businesses to bypass these challenges and still gain valuable insights for their decision-making processes.

The real world is full of complexities and nuances that are often difficult to replicate in synthetic data. However, advances in data generation techniques have enabled the creation of synthetic data that is increasingly indistinguishable from real-world data. This has opened up new possibilities for businesses to test and validate their models, systems, and strategies using synthetic test data.

The increasing growth of artificial intelligence helps to minimize user error and human agency in the process of generating synthetic data and greatly increases the speed and efficiency behind generating the right volume and type of data for any given scenario.

In summary, synthetic data has far-reaching implications for businesses across various industries. By leveraging synthetic data, companies can overcome limitations associated with real-world data and access high-quality training data for their machine-learning models. As a result, businesses can develop more accurate and reliable systems, ultimately leading to better decision-making and improved outcomes in the real world.

How do you create synthetic data?

To create synthetic data, data scientists use various synthetic data generation tools and techniques. Synthetic data is computer-generated and closely resembles real-world data in structure, pattern, and statistical properties, without using any actual data points from the real world.

Now that we've established what synthetic data is and how to generate synthetic data, let's explore why it's important and how it can be advantageous to your business.

Why is synthetic data important?

Synthetic data is becoming increasingly important for several reasons, including its potential to overcome limitations associated with real-world data, such as privacy concerns, bias, and cost.

As consumer information and privacy laws become even more stringent and far-reaching, the need for synthetic data not owned by anyone else is becoming increasingly significant. Today’s companies need to be able to operate in various contingencies and have multiple possible outcomes for different user scenarios. Synthetic data allows companies to respond to any potential user situations that arise.

Synthetic training data is a critical component in developing and refining machine learning models. The quality and quantity of training data can significantly impact the performance of these models.

With synthetic data generation, businesses can produce large volumes of high-quality training data that is both diverse and representative of real-world scenarios. This enables data scientists to fine-tune their models more effectively, ultimately leading to better predictions and outcomes.

There are even data models that can assess previously used training data to optimize it for future applications. Data models can also use synthetic data as a refinement or cleaner for existing training data sets to root out potential negative iterations.

Artificial intelligence (AI) plays a pivotal role in generating synthetic data. By using advanced algorithms and machine learning techniques, AI can create data that closely resembles real-world data while preserving privacy and ensuring data diversity.

Advantages of using synthetic data

The ability to generate data that is both realistic and diverse is a key advantage of synthetic data. By creating synthetic data that mimics the characteristics of real-world data, businesses can test their models and systems in a variety of scenarios, ensuring that they are robust and reliable. This can be particularly useful in industries where access to real-world data is limited, expensive, or poses privacy risks, such as healthcare or finance.

Here are some of the key advantages of using synthetic datasets in your business operations:


Generating synthetic data sets can be more cost-efficient than collecting and managing real data, as it doesn't require the same resources, time, or effort. Simply put, a computer can do 10 times the amount of work a human can do at 1/100 of the cost, putting your business at an obvious advantage.

Data privacy and security

Synthetic data helps businesses comply with data privacy regulations and protect sensitive customer data, as it doesn't rely on authentic data to make decisions. Not having to deal with the privacy issues or legal complications that come with real-world data means there are fewer hurdles your company has to jump through to use data.


Synthetic data can be generated in large volumes, providing more opportunities for testing and training machine learning models. With the right algorithms, a training model and an output generator can create infinite synthetic data for ongoing use.

Diversity of data

By generating a wide variety of synthetic data, businesses can test their models and systems across different scenarios and conditions. Synthetic data generation can produce diverse data that represent realistic situations that you probably would not have been able to source from authentic data.

Reduction of bias

Data bias can pose a big issue for your company because it doesn't provide an accurate representation of information. However, data bias can be reduced by generating synthetic data that is carefully designed to be representative and unbiased.

Synthetic data use cases

Various industries and sectors can benefit from using synthetic data. From healthcare to fraud detection systems, synthetic data has applications almost everywhere.

Machine learning

Synthetic data is commonly used to train machine learning models when real data is scarce, expensive, or poses privacy risks.


In a highly regulated industry, synthetic data can help researchers and practitioners access valuable insights without violating patient privacy.


Synthetic data can be used to model and predict financial trends, test trading algorithms, and ensure compliance with regulations.

Retail and marketing

Businesses can use synthetic data to optimize pricing strategies, understand customer behavior, and enhance marketing automations.


Synthetic data is particularly important in developing self-driving vehicles, as it allows for extensive testing and validation without the need for real-world testing.

Challenges of synthetic data

Despite its numerous advantages, synthetic data does have some limitations.

For instance, generating accurate and representative synthetic data can be challenging, and there may be concerns about the validity of the generated data when compared to real-world data.

Furthermore, synthetic data generation tools and techniques are still evolving, which means there may be room for improvement in accuracy and efficiency.

Discover new business opportunities with synthetic data

Synthetic data offers a range of advantages and use cases for businesses of all sizes, from small business owners to large enterprises. By leveraging synthetic data, you can overcome limitations associated with sensitive data, improve data privacy and security, and discover new business opportunities.

Mailchimp provides a variety of tools and services that can help you take advantage of synthetic data in your business. Our data management and data reporting resources can help you understand how to generate and use synthetic data effectively.

Integrating synthetic data into your business strategy can unlock new insights, optimize your operations, and make more informed decisions. With Mailchimp by your side, you can confidently navigate the world of synthetic data and seize the opportunities it presents for your business's growth and success.

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