Synthetic data is artificially created to mimic real-world data without actually containing any real-world observations.

There are a number of ways that synthetic data generation can be beneficial to your research or clinical trials. For one thing, it can be a more cost-effective solution, it can also help with scalability, data privacy and reduction of bias.

Not only that but it can be particularly helpful in fields where access to real-world data is more restricted.

So, if you’re thinking about introducing synthetic data into your research project, here is a step-by-step guide on how to do this effectively to ensure the best and most accurate results.

Define the purpose of your synthetic data

The first thing you need to do is to identify the purpose of synthetic data and why you’re choosing this method. You need to determine the exact role this data will play in your research. For example, is it to augment a limited dataset? Are you testing a model, or do you simply want to create a more diverse dataset?

Alternatively, you could think about your purpose in relation to your wider goals. For example, are you using synthetic data for model training or testing a hypothesis? Is your goal to simulate rare events or generate scenarios where real data collection is impossible?

Having a better understanding of why you’re using synthetic data and the role this will play within your research is crucial for effective application.

Choose the most appropriate generation techniques

There are several ways you can generate your synthetic data, but which technique you choose will also depend on the type of data you are working with, the purpose and the desired outcome.

By having a strong understanding of what you hope to achieve, you can choose the most appropriate and effective method from the following:

Rule-based generation

In order to generate data using this method, you must define the rules and constraints based on domain knowledge and then generate data accordingly. This method is best for when you understand the relationship between the variables.

Statistical methods

Statistical methods are used to generate synthetic data that mirrors real-world data distributions and is best for when your data has clear statistical properties. These tend to be AI-based models that learn patterns from real datasets and generate new data from this to mimic the original.

Noise injection

Taking an existing data set, you can add random noise to it to create synthetic variants. This technique can be useful for augmenting datasets, most often for tasks such as image recognition or speech processing.

Data synthesis tools

Perhaps the easiest way to generate synthetic data is to rely on data generation tools. Cloud-based platforms make it possible to gather existing and emerging data sources from the literature, clinical trials and other real-world data sources to build realistic synthetic populations.

Similarly, machine learning models can be used to generate synthetic data that mimics real-world datasets.

Generate the synthetic data

Once you’ve decided which method is going to be most appropriate for your study, you need to actually generate the data. If you’re using synthetic data generation tools, lots of the hard work is done for you.

If you’re using rule-based or statistical methods, you need to define the parameters and sample data points to generate the most accurate dataset.

Validate your data

Now your synthetic data has been generated, it’s essential that you validate its quality and usability. Again, if you’re using effective generation tools, this may be a lot easier.

If you’re using other methods, you need to compare the properties of the synthetic data to the real data (if available) or expected distributions.

For privacy-sensitive research, you also have to assess whether the synthetic data sufficiently protects the individual’s or group’s privacy.

Validating your data is crucial before you can move on to the next step because if you are not happy with the synthetic dataset you have created, you may wish to generate more data or try a different technique before moving on.

Think about the ethical and legal considerations

Next, you need to ensure that your synthetic data meets any relevant privacy standards, especially if it is derived from sensitive data like healthcare or financial information.

Some synthetic data tools or models may come with usage restrictions, so you also need to check for any licensing requirements for generating, using and publishing your research.

Incorporate the synthetic data into your research

Once you’re happy with the data sets you’ve got and that you’re meeting all regulations, it’s time to incorporate this synthetic data into your research project. There are several applications for this data, depending on the type of study you are working on.

Firstly, you may introduce this data in order to train machine learning models, particularly when you have limited real-world data or need to simulate rare events.

It could also be used for scenario testing, applying the data to test your hypotheses or running scenario analyses. This can be particularly useful in fields like finance or engineering.

What’s more, if you have access to real data from similar studies, you can now compare the results from real and synthetic data to ensure the robustness and reliability of your research and its outcomes.

Document your data generation process

In order to reproduce your results and be completely transparent about your study, you need to thoroughly document the synthetic data generation process from start to finish. This should include details about:

• The method you used for data generation
• Any real data used as input, if this is applicable
• How you validated your methods and results
• Any regulations or issues that you came up against and how you overcame these

Having details of every part of your research and the role synthetic data played in this is vital for validating your study and ensuring the most accurate outcomes.

Publish your findings

The final step is to publish your findings. When doing so, you need to clearly explain the role that synthetic data played in your research. This includes how it was generated and why it was necessary for your project.

You should also discuss the limitations of this data, making sure to be transparent and include details of any potential biases or deviations from real-world data.