The benefits and limitations of generating synthetic data

Data is everywhere. From social media to online shopping, we generate vast amounts of data every day. With its immense potential, high-quality data is used to make informed decisions and shape the future of businesses, organizations, and society.

But what happens when the data is too difficult to collect, too expensive, or too sensitive to be used for research or analytics? Enter synthetic data: a type of computer-generated data that mimics the characteristics and patterns of real-world data, allowing researchers and analysts to gain insights without using actual confidential or sensitive information.

In this blog post, we will explore the benefits and limitations of synthetic data and discuss the best practices for using synthetic data generation techniques to make the most of this valuable tool.

Let's dive in!

Significance of synthetic data

The average data scientist spends more than 60% of their time on collecting, organizing, and cleaning data instead of the actual analysis. This problem is compounded when faced with the need to use sensitive or confidential data, like medical records and credit card information.

In this case, synthetic data is used to replace real-world data, preserving the same patterns and characteristics while eliminating the need for access to confidential or sensitive information. This makes it easier and faster to create high-quality datasets for analytics, research, and machine learning applications without compromising data security, ultimately leading to improved decision-making, accuracy, and insights.

In addition, synthetic data is used to create data sets with greater diversity than the original source, allowing for more representative and accurate analysis. This is especially beneficial for businesses that need to analyze data from regions or populations with limited information available. Using synthetic data to create more diverse datasets and generate novel data points that may not exist in the real world helps researchers and analysts gain a better understanding of the problem they are trying to solve.

Synthetic data also helps bridge the gap between data science teams and the business side of an organization. By generating realistic data sets, it is easier to perform experiments and simulations that are more representative of real-world scenarios. This helps data scientists better understand the needs of their stakeholders while also providing the business side with an understanding of the data science process.

Benefits of generating synthetic data

Synthetic data is a lifesaver for organizations that work with confidential or sensitive data. Its power to replicate the characteristics and patterns of real-world data without exposing confidential information helps preserve data security while still allowing researchers, analysts, and decision-makers to gain valuable insights.

In addition, generating synthetic data offers several other benefits to organizations:

Lower costs associated with data management and analysis

Traditional data collection methods are costly, time-consuming, and resource-intensive. By using synthetic data, organizations reduce the costs associated with data collection and storage. This is especially beneficial for smaller organizations or startups with limited resources, as it allows them to perform analyses that would otherwise be too expensive or time-consuming.

Additionally, synthetic data is much easier to store and manipulate, eliminating the need for expensive hardware and software. This helps organizations save money on data storage and maintenance costs, allowing them to focus their resources on other aspects of their business.

Faster turnaround time for development workflows and projects

Data gathering and preparation is often a bottleneck in development workflows. By using synthetic data, organizations rapidly create high-quality datasets to use in experiments and simulations. This speeds up the development process and allows teams to focus their efforts on the analysis rather than data gathering.

Synthetic data is also used to generate data sets for projects with short timelines, such as A/B testing or rapid prototyping. This way, organizations quickly and accurately test different scenarios, create and deploy experiments and simulations quickly, and better understand their customers, products, or services.

Greater control over the quality and format of the dataset

With traditional data collection methods, companies are often limited to the data that is available to them, which may not be in the format or quality they need. Synthetic data, on the other hand, is generated to meet specific quality and format requirements, ensuring that the data is suitable for a particular use case or scenario.

This allows organizations to control and customize the characteristics and patterns of their dataset and tailor it to meet their needs and specifications, ultimately leading to more accurate and reliable analyses. Additionally, synthetic data is easily modified or adjusted as needed, allowing data teams to test and refine their models without the need for additional data collection.

Better performance in machine learning algorithms

Synthetic data allows organizations to generate large amounts of diverse data, which helps machine learning algorithms learn and generalize better. Additionally, it addresses issues such as overfitting, where the model performs well on the training data but poorly on new, unseen data. By synthesizing new data points, synthetic data helps prevent overfitting and improves the generalization capabilities of machine learning models.

Furthermore, synthetic data is used to balance class distributions, address missing values, and create new features that may be relevant to the task at hand. By using it to augment or replace real-world data, organizations improve the performance and accuracy of their machine learning algorithms, ultimately leading to better results and more effective decision-making.

Greater flexibility and increased collaboration

Due to its privacy-preserving properties, synthetic data is easily distributed between teams and organizations, enabling greater collaboration and promoting knowledge sharing. This allows teams to collaborate on data in a completely anonymized and secure manner while still preserving the integrity of the dataset. 

Additionally, synthetic data is used to create virtual replicas of datasets, which are then explored, tested, and shared with stakeholders. That way, teams experiment in a secure and controlled environment with greater flexibility and control over the data they use. 

Reduced bias and improved data security

Generating synthetic data has a transformative impact on organizations by reducing bias and improving data security. Synthetic data allows organizations to create balanced or representative samples that better reflect the underlying population, reducing the risk of discriminatory outcomes and promoting fairness and equity in decision-making. For example, a bank might use synthetic data to train a credit scoring model that incorporates a more diverse set of features, reducing the risk of bias against historically marginalized groups.

Synthetic data also enables organizations to preserve data security by replicating the characteristics and patterns of real-world data without exposing confidential information. For instance, a healthcare organization might use synthetic data to train a machine-learning model for diagnosing diseases without sharing actual patient data.

By using synthetic data to augment or replace real-world data, organizations increase trust and transparency in their decision-making processes while reducing the cost and complexity of data collection. 

Limitations of generating synthetic data

If you don't know much about synthetic data generation, you probably asked yourself at least once, what's the catch? If synthetic data is so powerful and useful, why not use it exclusively? 

Well, while synthetic data provides numerous benefits, there are certain limitations to be aware of.

Lack of realism and accuracy

The lack of realism and accuracy is perhaps the biggest limitation of synthetic data. While it replicates patterns and captures correlations, generating realistic synthetic data that captures the nuances of real-world data is a challenging task. This is especially true in cases where the data generation model is not well calibrated or does not accurately capture the underlying distribution of the real-world data.

Also, synthetic data may not capture the complexity of real-world datasets and can potentially omit important details or relationships needed for accurate predictions. For instance, a healthcare organization might generate synthetic patient data for training an AI model for predicting disease progression, but due to its lack of realism, the model may not be able to accurately predict said disease progression from the synthetic data.

Difficulty in generating complex data

Synthetic data generation techniques work best when the generated data is simple and can be described by a set of rules or patterns. Generating complex data, such as natural language text or images, is much more difficult and requires some more sophisticated techniques.

For example, natural language text generation is challenging because the generated sentences must be syntactically correct, follow certain grammar and punctuation rules, and convey the right meaning. Similarly, generating realistic images requires accurately capturing the nuances and details of the underlying images, specialized models, and a large dataset of real-world images to be trained on.

Difficulty validating synthetic data

Another limitation of synthetic data is the difficulty in validating its accuracy. While a synthetic dataset may look realistic and accurate, it is difficult to know for sure if it accurately captures the underlying trends of real-world data. Therefore, there is no guarantee that a model trained on synthetic data will be accurate when applied to the real world.

Synthetic data generative models look for common trends and patterns in real-world data but may miss out on subtle nuances or potential anomalies that are present in the real data. As a result, the synthetic data generated may not be completely accurate or reliable. 

Dependency on the real data

Synthetic data generation depends heavily on the underlying real-world data. If the real-world data is incomplete or inaccurate, then the synthetic data generated from it won't be perfect either. Furthermore, if the real-world data changes over time, then the synthetic data generated from it must be regularly checked and updated to ensure accuracy and reliability. 

However, having an automated data ingestion and generation system, such as the one used by our platform, can help overcome this limitation. With tools like Syntheticus, organizations can automatically generate new synthetic data if needed, ensuring accuracy and reliability even as the real-world data changes over time. 

Nonetheless, it's important to note that even the most advanced algorithms and models used to generate synthetic datasets are still susceptible to statistical noise and sampling biases, which can lead to inaccurate results.

Bias and privacy concerns

Another limitation of synthetic data is the potential for bias and privacy concerns. Generative models are often trained on existing datasets, which may contain biases or inaccuracies that can be propagated into the synthetic data. If these biases are not addressed, they can lead to inaccurate results and unfair decisions.

In addition, the lack of clear standards on privacy metrics can create uncertainty around how to best protect sensitive information in synthetic datasets. Syntheticus recognizes this challenge and is actively participating in the IEEE Standards Association, which has set up an IC Expert Group to set a standard for structured privacy-preserving synthetic data.

Furthermore, since synthetic datasets are generated from real-world data, there is a risk of exposing private and sensitive information if the data is not properly secured. For example, a healthcare organization may be able to generate synthetic patient data for training an AI model. However, the generated dataset could still contain sensitive information that needs to be protected. 

Since humans are still involved in the data synthesis process, the potential for human bias and privacy issues cannot be ignored. Therefore, it is crucial for organizations to prioritize data ethics and privacy issues to ensure that synthetic datasets do not expose sensitive information or propagate biases.

Best practices for generating synthetic data

To minimize the risks associated with synthetic data limitations, organizations should keep in mind a few best practices that will help ensure the accuracy and reliability of their synthetic datasets.

Ensure diversity and variety in the generated data

A dataset with limited diversity may not accurately represent the target population and may not provide sufficient coverage of different scenarios and situations. Therefore, it is important to use data generators that will produce data with different characteristics, such as age, gender, ethnicity, and socioeconomic status.

Additionally, the data should be generated with different distributions and patterns so that the synthetic dataset reflects the complexity and variability of the real-world data. By having a larger variety of data, the risk of bias and inaccuracy will be reduced, the synthetic dataset becomes more representative of real-world data, and it can be used to build more accurate and robust models.

Use appropriate data metrics

Data metrics such as accuracy, precision, and recall should be used to evaluate the quality of the synthetic datasets. These metrics help organizations analyze and assess the accuracy of the synthetic dataset and identify any potential issues or biases that may be present.

Organizations should also measure the performance of models trained on synthetic datasets against those trained on real-world data. This will help ensure that the generated data is accurate and reliable and can be used to make fair decisions.

Test the generated data thoroughly

Before using it for training or testing AI models, organizations should test the generated data to ensure it matches the characteristics of the real-world data and that it is free of any biases or inaccuracies. 

To do so, they can use a variety of statistical tests and metrics to analyze the generated data, such as statistical similarity, Kolmogorov-Smirnov, and total variation distance tests for distribution comparison, correlation and contingency tests for relationships between variables, and so on. This will help organizations identify potential issues or inaccuracies in the dataset before they are used for model training or testing.

Regularly monitor changes in the real-world data

Data is always changing, and the real-world data used to generate synthetic datasets may not stay constant over time. Organizations must regularly monitor changes in real-world data and update the synthetic datasets accordingly. This will ensure that the generated data is up-to-date and reflects the latest trends in the real world.

Final words

The usage of sensitive data still introduces new hazards and risks related to privacy and data ethics. Leaking or mishandling data is getting easier, thus introducing potential biases and leading to incorrect decisions. Therefore, organizations should practice caution when generating synthetic datasets and adhere to the best practices outlined above to ensure the accuracy and reliability of their generated data.

Synthetic data, though not perfect, undoubtedly have a part to play in resolving the issues between data utility and privacy concerns. It helps organizations get the data they need while ensuring privacy regulations are respected and potential biases are eliminated. Synthetic data generation tools, such as Syntheticus, will significantly improve the accuracy and quality of synthetic data by providing highly customizable and realistic datasets.

As with any new technology, generating synthetic data has its growing pains, but following these best practices and using the right tools, will positively impact many aspects of data science and artificial intelligence.