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Researchers, businesses, and other individuals need data to make informed decisions. In almost all aspects of work, robust data is a need. However, these professionals may not always have access to real-world data, whether for privacy, cost, or ethical reasons.

This creates the need for data that is artificially generated but simulates real-world events and patterns, providing the necessary information that makes predictive modeling possible.

In sectors like healthcare and finance that handle sensitive information, sharing or using real data can be risky, even internally. But synthetic data mimics the patterns of real data without exposing sensitive details, allowing researchers and companies to uncover insights without violating privacy regulations.

What is Synthetic Data?

While traditional datasets are gathered from surveys, experiments, or observational studies, synthetic data is created through algorithms or models that replicate the statistical properties of real data. This allows researchers to work with large quantities of data to test a hypothesis or validate findings without relying on real-world information that may be difficult to acquire.

For LimeSurvey users, synthetic data can offer innovative solutions to challenges such as limited or sensitive data, enabling better survey research and insights while protecting privacy.

The goal of a synthetic dataset is to replicate the statistical patterns found in real data, making it suitable for testing and training purposes. While it may not represent actual events, it can still provide valuable insights and serve as a foundation for analysis.

Synthetic Dataset vs. Real Data

When deciding whether synthetic data is right for you and your project, it’s important to keep in mind that it is not a substitute for real-world data. There are several key differences—many of which can have a significant impact on the insights and key findings derived. Here are a few areas where it’s especially important to understand how synthetic data differs from real data:

  • Accuracy: While synthetic data can replicate real-world patterns, it is not an exact representation. Some details may be lost or oversimplified, making it less accurate for certain applications.
  • Privacy: Synthetic data offers a clear advantage in terms of privacy, as it does not contain personal information. However, real-world data is more reflective of actual behaviours and outcomes.
  • Cost: Collecting and cleaning real-world data is often costly and time-consuming, while synthetic data can be generated quickly and affordably.

The Benefits of Synthetic Datasets

Once you have a good grasp of how synthetic data differs from real data, you can dive into the benefits of using it—particularly for those in fields related to research, AI, and machine learning.

  • Data availability: Synthetic datasets can be generated in large volumes, providing ample data for training AI models or conducting hypothetical experiments, even when real data is scarce.
  • Control and flexibility: Synthetic datasets allow for precise control over the variables and parameters, enabling researchers to create specific scenarios that would be difficult to capture in real-world data.
  • Data privacy: Since synthetic data is not tied to real individuals, it bypasses privacy concerns and data privacy regulations. This is especially useful for forecasting in industries like healthcare and finance, where regulations are particularly strict.
  • Ethics: When working with sensitive information, synthetic datasets offer a way to avoid the ethical dilemmas associated with using real data while still providing meaningful insights.

Common Use Cases for Synthetic Datasets

As synthetic data cannot replicate real data, there are limitations for how it can be used and when it is appropriate. Researchers, data analysts, and those working with prediction models can apply synthetic datasets in several ways to enhance their efforts, including:

  • Testing survey designs: Synthetic datasets can help users evaluate different survey formats or questions, determining optimal design before launching live surveys.
  • Training machine-learning models: If you’re using LimeSurvey data for machine learning, synthetic datasets can supplement real data to enhance model training without breaching privacy regulations.
  • Simulating outcomes: Researchers can create synthetic versions of survey data to explore potential outcomes based on hypothetical scenarios, enabling more strategic decision-making.
  • Data augmentation: If you’re working with limited survey responses, synthetic data can augment your dataset, providing additional insights.
  • Data anonymisation: In sectors like healthcare, synthetic datasets mimic real patient data without compromising privacy.

How to Create a Synthetic Dataset

Creating a synthetic dataset involves generating data that matches the statistical properties of real data.

To do this, you’ll first need to define the purpose of your dataset, identify the goal, and define your parameters.

From there, you’ll need to leverage a specific model or algorithm to generate the dataset. For the majority of LimeSurvey users, these three techniques are likely the most the useful:

  1. Generative Adversarial Networks (GANs): A generative AI framework, GANs can generate highly realistic synthetic survey data by using two neural networks to replicate real-world responses.
  2. Probabilistic models: These models use statistical distributions to create synthetic data based on patterns observed in real survey datasets.
  3. Resampling methods: Techniques like bootstrapping can be used to generate multiple synthetic datasets from a smaller sample of real survey responses, offering greater flexibility in analysis.

Once you’ve chosen the appropriate algorithm, generate the synthetic dataset by inputting the required variables, such as sample size, distribution, and noise. Then, after the data is generated, compare it to real-world data to ensure that it replicates the desired statistical patterns and behaviours.

How to Evaluate the Quality of Synthetic Datasets

The quality of a synthetic dataset is determined by how closely it mirrors the characteristics of real data. To evaluate the quality of the data you’ve generated, consider the following:

  1. Statistical Accuracy: Does the synthetic data match the distribution, correlations, and variability of real-world data?
  2. Usability: Can the synthetic dataset serve its intended purpose, whether it’s training a model or simulating real-world scenarios?
  3. Bias and Fairness: Does this synthetic data introduce or amplify biases that could skew results?
  4. Privacy and Ethics: Does this dataset inadvertently represent information about real individuals?

Challenges and Limitations of Synthetic Datasets

Despite the advantages of synthetic datasets, they do come with a few challenges. Chief among them is the lack of realism, as the dataset may not capture the full complexity of real data, leading to less reliable results.

Another major concern is whether the algorithm used to generate synthetic data is biased. If so, the resulting dataset will likely also be biased, which can affect outcomes and analyses. Finally, it can be difficult to validate whether a synthetic dataset is truly representative of real-world data, as it lacks the grounding in actual events or behaviors. Thorough testing and comparison with real datasets is necessary to ensure accuracy.

Best Practices for Using Synthetic Datasets

To maximize the benefits of synthetic datasets in your survey, it’s important to follow these best practices:

  1. Validate regularly: Continuously compare synthetic data with real-world data to ensure it accurately replicates the necessary characteristics.
  2. Monitor bias: Regularly check for any unintended biases that may have been introduced during data generation and take corrective measures as needed.
  3. Use ethical frameworks: Always consider privacy and ethical implications when creating and using synthetic datasets, especially if the real-world data contains sensitive information.
  4. Test in multiple scenarios: Use the synthetic dataset in various scenarios to ensure it is versatile and can handle a range of conditions and requirements.

Synthetic datasets provide a powerful solution to many of the challenges associated with real-world data collection and usage. With advantages including data availability, privacy, cost-effectiveness, and ethical flexibility, synthetic data can be an invaluable tool for researchers, developers, and data scientists. However, their use requires careful planning, stringent validation, and wide-ranging ethical considerations.

By understanding the benefits, challenges, and best practices for using synthetic data, you can enhance your LimeSurvey projects while safeguarding privacy and improving research outcomes.

If your organization wants to stay compliant with data privacy regulations while gathering meaningful insights, synthetic datasets are an option. Use LimeSurvey to gather, analyze, and extract information from your dataset to elevate your research, while prioritizing privacy.