Synthetic Data: Pros and Cons

How can you be sure the data you gathered accurately reflects the insights gleaned from actual people in a world where data is abundant? How can we use this information in safeguarding your security and privacy? In this blog, let's discuss and learn more about the benefits and limitations of synthetic data.

What is Synthetic Data?

Synthetic data is the information that is artificially produced rather than derived from actual occurrences. It is used to collaborate with machine learning (ML) models, validate numerical models, and serve as a stand-in for test datasets of production data.

In addition, synthetic data places a high priority on privacy, scalability, and flexibility. Aside from market research, other industries that use synthetic data include telecommunications, healthcare, insurance, and finance.

According to Precedence Research, the global synthetic data generation market size reached around $432.08 million in 2024 and is expected to reach $8.87 billion by 2034.

The Advantages and Benefits of Synthetic Data

Synthetic data transforms how businesses use traditional data by offering a flexible, scalable, and morally sound alternative. Its expanding use will make improvements in artificial intelligence, machine learning, and other domains possible. The following are some benefits of synthetic data:

Data Privacy and Availability

Synthetic data can easily be generated without any real user information. It removes the need to store or process personally identifiable information (PII), which reduces privacy risks. Users and organizations can follow data privacy rules like CCPA, GDPR, and HIPAA by making data that looks like real-world trends but does not include real user information.

Some organizations like healthcare and finance use synthetic data to exchange valuable data-driven information with partners, vendors, or researchers without worrying about privacy violations.

Key takeaway

Since synthetic data provides a privacy-preserving alternative to accurate data, organizations can use it to analyze and disseminate information without infringing on people's privacy or running the risk of legal action.

Cost-effective

Collecting, cleaning, and segmenting real-world data, especially for large-scale datasets, can be time-consuming and expensive. By generating synthetic data, you can save money and still obtain efficient and accurate data.

Synthetic data is often pre-labeled as it is generated to save the time and expense of manual annotation. Furthermore, creating on-demand synthetic data and tailoring it to your respondents' criteria can be more cost-effective than recruiting actual respondents.

Key takeaway

Synthetic data is especially helpful for projects with limited funds or short iteration cycles because it enables companies to obtain extensive, high quality datasets at an affordable price.

Improve Diversity

Through synthetic data, you can generate a diverse dataset representative of your target audience. Synthetic data also lets you present a more accurate report, enhancing model robustness and delivering the information needed to understand your consumer better.

By creating data points that represent demographic or psychographic factors that might not be adequately represented in real-world data, you can purposefully broaden the diversity of your datasets.

Without diversity, a dataset may not represent the target population. For this reason, AI tools that produce data mirroring your consumer’s voice are essential.

Limitations of Synthetic Data

Although synthetic data is helpful in many situations, mainly when accurate data is scarce or sensitive to privacy concerns, there are better replacements that must be thoroughly verified for practical uses. Some parts also need to be toned to reach the limits of synthetic data.

Lack of Realism and Accuracy

The need for more accuracy and realism is one of the most significant drawbacks of synthetic data. Users need help to create realistic synthetic data that captures the subtleties of real-world data, even though it replicates patterns and captures correlations. This is especially true if the data generation model needs to be more accurately calibrated or accurately reflect the distribution of the actual data. Applications using synthetic data may oversimplify intricate relationships between variables like social behavior and environmental conditions.

Synthetic data generation methods can also introduce artifacts or unrealistic features that may negatively affect the performance of models trained on such data. These might change the model and get it to recognize patterns that aren't there in real-world scenarios, making it less capable of making wise choices.

Key takeaway

It may be necessary to revise synthetic data for this, requiring high realism. It is most effectively used in conjunction with actual data validity to maximize its benefits and minimize the drawbacks of realism.

Potential Biases

As part of the limits of synthetic data, there are still unknowns and new risks concerning data ethics and privacy associated with using sensitive data. Research from AI Multiple suggests that synthetic data may be limited, biased, or deceptive because it lacks variability and correlation. Improper handling and data leaks can lead to biases and poor decision-making.

For industries under healthcare, lending, and job applications, for instance, inaccurate data or balanced trends may produce some unfair results. Even though synthetic data has limitations, it can be used to safely resolve privacy and data utility conflicts, ensuring that potential biases are removed and applicable privacy laws are followed.

Regulatory Compliance

Synthetic data may only partially satisfy the requirements of specific regulations like GDPR and HIPAA, mainly if the data generation process still uses real data or identifiable patterns.

For some industries like healthcare and finance, there are strict guidelines for how data should be used, stored, and shared. Compliance issues may arise because synthetic data derived from real data may still contain minute patterns that could be reverse-engineered to identify specific patterns.

Additionally, compliance requirements may need to be more specific because regulatory agencies are still creating frameworks for synthetic data. Organizations can be challenging to ensure they are entirely compliant because of the legal complexity caused by unclear guidelines. Synthetic data is made to keep personal information safe, but some patterns might still be clear enough to expose private details, which can break privacy rules.

Many regulations demand thorough audits and documentation data handling procedures for audit and documentation requirements. Complexity may also increase if organizations use synthetic data because they may still need to prove that it complies with privacy standards and document how it was generated.

Key takeaway

Regulatory compliance must be carefully considered, especially in sensitive applications, even though synthetic data can ease privacy concerns. All regulatory requirements must be met by carefully analyzing and aligning synthetic data.

Dependence on Real Data

The synthetic data will be flawed if the underlying real data is accurate and complete, such as inconsistent features, unrealistic patterns, and overfitting training data. This is especially true when creating synthetic data, which needs to be updated frequently to account for changes over time, maintaining accuracy and dependability.

Users are challenged by the quality of synthetic data, which appears to be noisy, unbalanced, or incomplete compared to real data. These limitations can hinder model performance and reduce the utility of synthetic datasets. With a baseline, synthetic data may only accurately depict entirely novel or developing conditions, such as new diseases or emerging market trends. Since real data is still required, make sure synthetic data is up to date in order to spot emerging trends.

While synthetic data can help fill in gaps or extend data sets, it frequently needs to capture the subtleties and complexity of real-world data. For applications requiring high realism and variability, real-world data collection may still be necessary to capture these specifics.

Key takeaway

Synthetic data is a valuable tool for enhancing real data. However, because it relies on real-world data as a baseline, it must partially replace the necessity of original data collection, especially in dynamic or complex spaces. Remembering sampling biases and statistical noise can still affect even the most sophisticated models and algorithms when creating synthetic datasets, producing inaccurate results.

Uncertain Performance in Production

The complete range of essential variability and minute details in real data are frequently absent from synthetic data. Models trained on fake data might do well in tests but need help with unexpected real-life situations. However, AI models trained only on fake data may need to be more accurate because they must notice slight hints or changes in real situations. This is particularly problematic for sensitive applications like medical or automotive diagnostics, where accuracy is crucial.

There is also a possible synthetic pattern on synthetic data overfitting. Without real-world data exposure, models might overfit particular distributions or patterns found in synthetic data. This overfitting may make the models less reliable and synthetically generated when applied to new, untested data.

Extensive validation with real data is also necessary to guarantee that models are AI trained exclusively on synthetic data. This gives the deployment process an additional layer of testing and quality control. Because synthetic data can create a false sense of confidence in the model's performance, there is also a risk of false confidence. Although the models must catch up in real-world scenarios, they can demonstrate promise in simulated tests.

Technical Complexity

Producing synthetic data often necessitates complex algorithms like statistical properties or generative adversarial networks (GANs), so processing advanced data science knowledge is required. These methods necessitate a thorough understanding of machine learning models and data science, which not all organizations may have.

While synthetic data can be helpful in many situations, bear in mind that mainly when real data is scarce or sensitive to privacy concerns, there are better replacements that need to be thoroughly verified for practical uses.

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