It’s no secret that businesses face challenges in acquiring and sharing high-quality data. Synthetic data generation is a practical solution that helps produce large artificial datasets and high-quality test data without privacy risks or red tape.
Synthetic datasets can be created using a variety of methods, offering diverse applications. When properly evaluated, synthetic datasets generated using advanced algorithms help organizations speed up their analytics, research, and testing. So let’s take a closer look.
This article introduces you to synthetic data, including the main types, differences from anonymized datasets, and regulatory nuances. You’ll learn how artificially generated data solves critical data problems and minimizes certain risks. We’ll also discuss its applications across industries, accompanied by examples from our case studies.
Synthetic data: definition and market statistics
Synthetic data is artificially generated information devoid of confidential content, and it serves as an alternative to real datasets. Data scientists often call AI-generated synthetic data a synthetic data twin because of its high statistical accuracy in mimicking real data.
Artificial datasets are created using artificial intelligence (AI) algorithms and simulations that maintain the patterns and correlations of the original data. This data can include text, tables, and pictures. The algorithms replace personally identifiable information (PII) with mock data.
Grand View Research forecasts that the market for synthetic data generation with Generative AI will grow from $1.63 billion in 2022 to around $13.5 billion by 2030 at a CAGR of 35%. According to Gartner, 60% of data used for AI in 2024 will be synthetic — that’s 60 times more than in 2021.
Synthetic data platforms are on the rise, too. Market Statesville expects the global synthetic data platform market to grow from $218 million in 2022 to $3.7 billion by 2033.
Why is artificial data on the rise? One driving factor is freedom from regulatory oversight.
Do privacy laws regulate AI-generated synthetic data?
Many US and EU data security and privacy regulations apply to identifiable personal data.
But those regulations don’t apply to synthetic data — synthetic data is treated similarly to anonymized data. They form the so-called “core” of other legal rules.
For example, recital 26 of the GDPR says that privacy protection rules only apply to data that relates to an identifiable person. If your synthetic data is generated such that it can’t be traced back to identifiable individuals, it’s exempt from regulatory oversight. Regulatory oversight aside, there are other obstacles to using real data that drive businesses to generate synthetic data.
Key challenges of using real data
Many companies have a hard time finding and using relevant, high-quality data, especially in sufficient amounts for AI algorithms training. Even when they do find it, sharing or utilizing the datasets can be challenging due to privacy risks and compatibility issues. This section outlines the key challenges synthetic data can solve.
Privacy risks hinder data usage and sharing
Data security and privacy regulations, such as GDPR and HIPAA, introduce bureaucratic obstacles to data sharing and utilization. In industries like healthcare, even sharing PII between departments within the same organization can be time-consuming due to governance checks. Sharing data with external entities is even more challenging and carries more security risks.
Research from Fortune Business Insights identifies rising privacy risks as a primary catalyst for adopting synthetic data practices. The more data you store, the more you risk compromising privacy. According to the 2023 IBM Security Cost of a Data Breach Report, the average data breach cost in the US was $9.48 million. Worldwide, the average cost was $4.45 million; companies with less than 500 workers lose $3.31 million per breach. And that doesn’t account for reputational damage.
Difficulties finding high-quality data
A 2022 survey of 500 data professionals revealed that 77% of engineers, analysts, and data scientists faced data quality issues. According to the report, data quality hinders a company’s financial performance and productivity and makes achieving a holistic view of its services hardly attainable.
Companies may lack enough data from specific demographics to train their machine learning (ML) models properly. And datasets often contain inconsistencies, inaccuracies, and missing values. If you train your AI platforms with ML models on low-quality data lacking demographic diversity, it will make inaccurate, biased predictions. Similarly, like anonymized data generation, unrefined algorithms can produce unreliable artificial datasets that affect the outcome of data analysis.
Upsampling with synthetic data can enhance data quality by addressing imbalances in datasets. This ensures that underrepresented classes receive more proportional representation and reduces bias. A more robust and representative dataset yields improved analysis results and model training.
Dataset incompatibilities
Datasets sourced from various origins or within multi-table databases can introduce incompatibilities, creating complexities in data processing and analysis and hindering innovation.
For instance, data aggregation in healthcare involves electronic health records (EHRs), wearables, proprietary software, and third-party tools. Each source may utilize distinct data formats and information systems, leading to disparities in data formats, structures, or units during integration. The use of synthetic data can address this challenge, ensuring compatibility by generating data in the desired format.
Anonymization is insufficient
Anonymization techniques aren’t enough to overcome privacy risks or data quality problems. Moreover, masking or removing identifiers can strip away details needed for in-depth analysis in large datasets.
Besides, anonymized data can be re-identified and traced back to individuals. Malicious actors can use advanced analytics to uncover time-based patterns that compromise the anonymity of seemingly de-identified data. Synthetic data is superior to anonymized data in that regard.
Unlike anonymization, synthetic data doesn’t change existing datasets but generates new data that resembles the characteristics and structure of the original, preserving its utility. It’s an entirely new dataset containing no personally identifiable information.
But it’s more nuanced than that. There are several types of synthetic data you can generate.
Types of synthetic data generation
Synthetic data creation processes vary based on the type of data required. Synthetic data types include fully AI-generated, rule-based, and mock data — each meeting a different need.
Fully AI-generated synthetic data
AI-generated synthetic data is built from scratch using ML algorithms. The ML model trains on real data to learn about the data’s structure, patterns, and relationships. Generative AI then uses this knowledge to generate new data that closely resembles the original’s statistical properties (again, while making it unidentifiable).
This type of synthetic data is useful for AI model training and is good enough to be used as if it is real data. It’s especially beneficial when you cannot share your datasets due to contractual privacy agreements. However, to generate the synthetic data, you need a significant amount of original data as a starting point for model training.
Synthetic mock data
Synthetic mocked data refers to artificially created data that imitates the structure and format of real data but doesn’t necessarily reflect actual information. It helps developers ensure their applications can handle various inputs and scenarios without using genuine, sensitive, or private information and, most importantly, without relying on real-world data. This practice is essential for testing functionality and refining software applications in a controlled and secure manner.
When to use it: To replace direct identifiers (PII) or when you currently lack data and prefer not to invest time and energy in defining rules. Developers commonly employ mock data to evaluate the functionality and appearance of applications during the early stages of development, allowing them to identify potential issues or design flaws.
Even though mock data lacks the authenticity of real-world information, it remains a valuable tool for ensuring systems’ proper functioning and visual representation before actual data integration.
Rule-based synthetic data
Rule-based synthetic data is a useful tool for generating customized datasets based on predefined rules, constraints, and logic. This method provides flexibility by allowing users to configure data output according to specific business needs, adjusting parameters such as minimum, maximum, and average values. In contrast to fully AI-generated data, which lacks customization, rule-based synthetic data offers a tailored solution for meeting distinct operational requirements. This approach proves particularly useful in testing, development, and analytics, where precise and controlled data generation is essential.
Each synthetic data generation method has different applications. Syntho’s platform stands out by creating synthetic data twins with little to no effort on your part. You get statistically accurate data for your needs that’s free of compliance overhead.
Most popular synthetic data applications
Artificially generated data opens innovation possibilities for healthcare, retail, manufacturing, finance, and other industries. The primary applications include data upsampling, analytics, testing, and sharing.
Upsampling to enhance datasets
Upsampling means generating larger datasets from smaller ones for scaling and diversification. This method is applied when real data is scarce, imbalanced, or incomplete.
Consider a few examples. For financial institutions, developers can improve the accuracy of fraud detection models by upsampling rare observations and activity patterns in their datasets. Similarly, a marketing agency might upsample to augment data related to underrepresented groups, enhancing segmentation accuracy.
Advanced analytics with AI-generated data
Companies can leverage AI-generated high-quality synthetic data for data modeling, business analytics, and clinical research. This proves to be a viable alternative when acquiring real datasets is either too expensive or time-consuming.
AI-generated synthetic data empowers researchers to conduct in-depth analyses without compromising patient confidentiality. Researchers gain access to patient data, information about clinical conditions, and treatment details, obtaining insights that would be considerably more time-consuming with real data. Moreover, manufacturers can freely share data with suppliers, incorporating manipulated GPS and location data to create algorithms for performance testing or enhance predictive maintenance.
However, synthetic data evaluation is critical. The Syntho Engine’s output is validated by an internal quality assurance team and external experts from the SAS Institute. In a study of predictive modeling, we trained four models on real, anonymized, and synthetic data. Results showed that models trained on our synthetic datasets had the same level of accuracy as those trained on real datasets, while anonymized data reduced the models’ utility.
External and internal data sharing
Synthetic data simplifies data sharing within and across organizations. You can exchange information without risking privacy breaches or regulatory non-compliance. The benefits of synthetic data include accelerated research outcomes and more effective collaboration.
Retail companies can share insights with suppliers or distributors using synthetic data that reflects customer behavior, inventory levels, or other key metrics. However, the information keeps sensitive customer data and corporate secrets private.
Syntho won the 2023 Global SAS Hackathon for our ability to generate and share artificial data effectively and risk-free. We synthesized patient data for multiple hospitals with different patient populations to demonstrate the efficacy of predictive models. Using the combined synthetic datasets was shown to be just as accurate as using real data.
Synthetic test data
Synthetic test data is artificially generated data designed to simulate data testing environments for software development. In addition to reducing privacy risks, synthetic test data enables developers to rigorously assess applications’ performance, security, and functionality across a range of potential scenarios without impacting the real system.
Our collaboration with one of the largest Dutch banks showcases synthetic data benefits for software testing. The Syntho Engine generated production-like datasets that helped the bank speed up software development and bug detection, leading to faster and more secure software releases.
Syntho’s synthetic data generation platform
Our award-winning and constantly evolving solution allows you to accelerate data-related processes without worrying about regulations or contractual terms. Syntho Engine offers various functionality for different needs, such as:
- AI-powered PII scanner that automatically identifies and replaces sensitive information with mock data
- Masking functionality that can obscure potentially sensitive information in non-PII data to eliminate privacy risks
- An upsampling feature that increases data volume to fill gaps and diversify imbalanced datasets
- Subsetting, which involves creating smaller datasets from large databases while maintaining cross-table referential integrity, is particularly valuable for focused testing
- Customizable rules allow you to produce synthetic data for your business logic or specific needs.
The Syntho platform is designed with privacy and ease of use at the forefront. It lets clients create ready-to-share, high-quality data in just fifteen minutes. Crucially, we deploy data within a secure customer infrastructure that we can’t access, ensuring data security and compliance with privacy regulations.
What’s in the future for synthetic data?
Synthetic data generation with generative AI helps create and share high volumes of data, bypassing format compatibility issues, regulatory constraints, and the risk of data breaches.
Unlike anonymization, this data creation technique preserves structural relationships in the data. This makes synthetic data suitable for advanced analytics, research and development, diversification, and testing.
The use of synthetic datasets will expand across industries. Its scope will extend to complex images, audio, and video content. Companies will use it for more advanced simulations and applications.
Our platforms integrate into any cloud or on-premises environment. Moreover, we take care of the planning and deployment. Our team will train your employees to use Syntho Engine effectively, and we’ll provide continuous post-deployment support.
Do you want to learn more about our Syntho platform or artificial data in general? Feel free to schedule an intro call via our website.