Synthetic Data vs Real Data: Benefits, Challenges in 2024

 In recent years, there has been a growing interest in the use of synthetic data for various applications, such as machine learning and data analytics. According to Gartner, by 2030, synthetic data use will outweigh real data in AI models.¹ Nevertheless, for some business leaders and executives, it may still be a foreign concept in terms of its differences from real data.

In this article, we will explore:

• what is synthetic data and how it is created
• what are the benefits of synthetic data over real data
• what are some of the challenges with using synthetic data
• which type of data should be used for specific applications

What is synthetic data? How is it created?

Synthetic data is data that has been artificially created by computer algorithms, as opposed to real data that has been collected from natural events.

Although there are other ways to generate synthetic data, AI-generated synthetic data is produced by AI that is trained on complex real-world data, by the power of deep learning algorithms. The merit in using generative AI is that it is capable of automatically detecting patterns, structures, correlations, etc. within real data, and then learning how to generate brand new data with the same patterns. You can see the structural similarity in Figure 1 below.

One popular method is to generate data using a computer algorithm that mimics the behavior of real-world data. This approach can be used to create synthetic data sets that are similar to real data sets in terms of their distribution and variability. Another common method for creating synthetic data is to use a random number generator to create data that is uniform and has no correlation.

For more on what is synthetic data and its benefits, you can check our article.

The benefits of synthetic data over real data

There are several benefits of using synthetic data over real data. Below we listed 8 ways synthetic data can be useful.

1. Overcomes regulatory restrictions: The most important benefit of synthetic data over real data is that it avoids regulatory restrictions on real data. Synthetic data can replicate all important statistical properties of real data without exposing the latter, eliminating any concern about privacy regulations. This feature thus further enables:

• Privacy preservation: It is hard to sustain privacy in classic anonymization methods while preserving the usefulness of the real dataset. You have to choose either protecting the privacy of the people  while diminishing the effectiveness  of that data or getting usefulness while renouncing privacy. With synthetic data, the privacy/usefulness dilemma is resolved since there is no real data that you must protect against leaking.
• Resistance to reidentification:  Real data removes certain information to satisfy anonymization. Yet, reidentifying the data source is still highly possible. As a study shows, sharing only 3 bank transaction information per customer, with the merchant and the date of the transactions, makes 80% of customers identifiable.³
• Aptitude for Innovation and Monetization: As there are no privacy concerns for synthetic data, it is possible to share these datasets with third parties for innovation research and to use them as a monetisation tool.

2. Streamlines simulation: Synthetic data enables the creation of data to simulate conditions that have not yet been encountered. Where real data does not exist, synthetic data is the only solution. For instance, automotive firms may not gather real data for all possible situations to train smart cars.

3. Avoids statistical problems: Synthetic data is immune to some common statistical problems. These can include item nonresponse, skip patterns, and other logical constraints. For example, a synthetic data generation program could be designed to ensure that all items in a survey are answered, and that there are no skip patterns in the responses. This can be done by specifying the rules for generating the data, such as the possible response options for each item and the dependencies between items. By carefully designing these rules, the synthetic data can be generated in a way that avoids common statistical pitfalls.

4. Speeds up the process: Synthetic data can be generated much faster than real data can be collected, saving time and ensuring agility and competitiveness in the market.

5. Achieves higher consistency: Synthetic data can be more uniform and consistent than real data, which can be variable due to its natural origins. This uniformity makes synthetic data more suitable for  performing accurate analyses on synthetic datasets.

6. Ensures easy manipulation: Synthetic data can be more easily manipulated than real data in a controlled way, which can be difficult to alter without compromising accuracy. Therefore, it allows for more precise and controlled testing and training of machine learning models, and it can be generated in large quantities with specific characteristics and biases. This can be useful for improving the performance of machine learning algorithms in a variety of applications.

7. Increases cost-effectiveness: Synthetic data can be more cost-effective than real data. Of course, creating synthetic data is not free. The main cost of synthetic data is an upfront investment in building the simulation. However, real data enforce timely and financial costs every time a new data set is required or an existing one is revised.

8. Facilitates AI/ML training:  Synthetic data is more enriching for teaching AI/ML models as it has no regulations restricting real data. Also, it has a higher  capacity to create more data, feeding AI much more to learn. For more detail, check our article on the use of synthetic data to improve deep learning models.

Some challenges with using synthetic data against real data

Besides a variety of benefits, there are some challenges with using synthetic data.

• Biased or deceptive results: Synthetic data can be misleading, limited or discriminatory  due to its lack of variability and correlation. 
• Lack of accuracy: Another challenge with synthetic data is that it is often created using a computer algorithm, which may not always be accurate. As a result, synthetic data can occasionally  produce inaccurate results.
• Time-consuming steps: Relatedly, synthetic data requires additional verification steps, such as comparing model results with human-annotated, real-world information. Such efforts take time to complete and prolong the projects. 
• Losing outliers: Synthetic data may not cover some of the outliers present in the original dataset because it can only mimic but not replicate real data.  However, outliers can be relevant for some research. 
• Dependency on the real data: Synthetic data quality often depends  on the real model and the dataset that have been developed for creating synthetic data. Without a desirable and qualitative real dataset, various synthetic datasets that are generated in huge amounts by using the original dataset will end up functioning ineffectively and sometimes even incorrectly.
• Consumer skepticism: As synthetic data use increases, businesses can face consumer skepticism, such as questioning the credibility of the data for reaching conclusions and making products. Consumers might demand assurance for the transparency of the data generation techniques and the privacy of their information. 

Despite these challenges, synthetic data remains an important tool for data analysis. When used correctly, synthetic data can provide valuable insights into the behavior of real-world data.

Which type of data should be used for specific applications? Synthetic or Real?

As we discussed in the section on the benefits of synthetic data, there are various application areas it can be used, while it is impossible to use real data.

For example, synthetic data can be used in radioactive data sets. The term “radioactive” is often used to describe data that is constantly changing and difficult to keep track of. This can be due to a variety of factors, such as the rapid growth of the dataset, the frequent addition of new data points, or the dynamic nature of the data itself. It is highly difficult to keep track of such data in a real data method. 

On the other hand, it is better to use real data rather than synthetic data in cases where the goal is to reproduce the exact distribution of a real-world dataset. In such cases, it is often preferable to use the original dataset rather than a synthetic version.

In cases where the goal is to study the correlation between different variables in a dataset, it is often better to use real-world data instead of synthetic data, which typically does not exhibit any correlation.

Additionally, synthetic data can be difficult to interpret and may not accurately reflect the behavior of real-world data.

Ultimately, the type of data that should be used for a particular application depends on the specific needs of the analysis. When accuracy is key, then real-world data should probably be used. However, in cases where speed or consistency is more important than accuracy, then synthetic data may be a better choice.

For more on synthetic data

If you want to gain more insight on synthetic data, its benefits, use cases, tools, you can check our other articles on the topic:

• Synthetic Data Generation: Techniques, Best Practices & Tools
• Synthetic Data Statistics: Benefits, Vendors, Market Size
• Top 20 Synthetic Data Use Cases & Applications in 2022
• Synthetic Data Tools Selection Guide & Top 7 Vendors
• 4 Synthetic Data Applications to Enable Finance Innovation in ’22
• Synthetic Data for Healthcare: Benefits & Case Studies in 2022

If you have questions regarding synthetic data and real data, feel free to contact us:

External Links

• 1. “Is Synthetic Data the Future of AI?” Gartner, 22 June 2022, https://www.gartner.com/en/newsroom/press-releases/2022-06-22-is-synthetic-data-the-future-of-ai. Accessed 10 December 2022.
• 2. “Synthetic data: Unlocking the power of data and skills for machine learning – Data in government.” Data in government, 20 August 2020, https://dataingovernment.blog.gov.uk/2020/08/20/synthetic-data-unlocking-the-power-of-data-and-skills-for-machine-learning/. Accessed 10 December 2022.
• 3. https://www.science.org/doi/10.1126/science.1256297?sid=9b47355e-d1da-4f56-b745-c876999ea9e3