Top 7 Synthetic Data Companies Plus Criteria for Choosing the Best Provider
Table of Contents
Identify the required types of synthetic data
Synthetic data generation is a process of using artificial intelligence (AI) algorithms to produce mock data, fully artificial or based on real data, for the purposes of analytics. These are the most popular types of synthetic data generation:
- Fully AI-generated synthetic data is created from scratch using machine learning to mimic the statistical properties of the original data while ensuring anonymity. It’s especially useful for model training and data sharing.
- Rule-based synthetic data is generated based on predefined rules and constraints to meet specific business needs. Most often, it’s used for advanced analytics that requires controlled data qualities.
- Synthetic mock data mimics real data’s structure and format without using actual information. Requiring minimal investments, it often serves as test data for software development.
Synthetic datasets are free from personally identifiable information (PII). Since it can’t be linked back to specific individuals, synthetic data isn’t subject to regulations like the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA).
Before you start scouting for synthetic data companies, you should figure out whether you need to generate structured or unstructured data. As well as define the ROI of Synthetic data.
Structured data
Structured data consists of organized, quantitative datasets in a tabular format with interconnected data points. It’s often categorized chronologically for efficient analysis of human-based behavior, financial data, and time-based trends.
Examples include:
- Names
- Addresses
- Contact data
- Payment information (credit card numbers, invoices, etc.)
- Financial performance
To produce synthetic structured data, a generative machine learning model is used on a relational database with real data. This model is designed to create a new dataset that mirrors the original in mathematical or statistical terms.
Unstructured data
Unstructured data is qualitative data without a predefined format. Unlike structured data, it does not fit neatly into traditional database fields and cannot be processed quickly. Managing this type of data requires using non-relational (NoSQL) databases designed to handle less structured information.
Companies use advanced machine learning, computer vision, NLP, and Generative Adversarial Networks models to extract patterns and insights from unstructured data.
Examples include:
- Textual data: emails, social media posts, web pages.
- Images: visual information contained in images and photographs.
- Audio: speech or music recordings.
- Video: video data, combinations of images, and audio.
- Sensor data: temperature and accelerometer data, IoT sensor outputs.
- Social media content: posts, comments, images, videos.
After deciding on structured vs. unstructured data, the next step is to clarify exactly why the company needs synthetic data generation.
Determine your company’s synthetic data requirements
A synthetic data provider you choose should align with your analytical, operational, and data privacy requirements. Different use cases call for different synthetic data approaches; however, many providers support limited methods. Only a few cover multiple synthesis techniques.
Identify the use cases
As we said, structured and unstructured datasets serve different purposes. Let’s look at the potential use cases for synthetic data depending on the data type.
Structured synthetic data use cases:
- Test data. Synthetic data is used to create realistic test environments for software development and quality assurance without the risk of compromising real data.
- Data twins. Organizations can produce life-like data to mirror real system performance and use it for quality control (identifying the most efficient configurations, manufacturing conditions, application settings, etc).
- Algorithm improvement. Synthetic generation data can be used to train algorithms to detect threats, prevent fraud, and offer personalized recommendations.
- Product demos. Companies use synthetic data to showcase their product’s capabilities without exposing real customer data.
- Data sharing. Synthetic data facilitates safe internal and external sharing for collaboration and innovation.
- Clinical research. In this area, synthetic data helps analyze trends, demographics, and treatment outcomes while safeguarding patient privacy.
Unstructured synthetic data use cases:
Natural language processing (NLP) and training. Synthetic data is crucial for training and fine-tuning machine learning models for text and speech recognition and generation without collecting real-world data.
- Computer vision (video). Organizations can train computer vision software with a broad range of artificial image and video data.
- Audio. Synthetic data generates realistic yet artificial sounds or speech patterns for training and testing voice recognition and sound analysis systems.
Using various methods, you can tweak or expand the produced data to make training datasets more diverse and reduce the risk of algorithmic bias.
Also, it’s important to think about how practical artificial data is.
Consider data utility
Synthetic data must replicate the patterns, distributions, and qualities of the original datasets. When choosing a provider, double-check that the data they generate can stand in for the actual data. The tool must be useful for the intended practical purposes, like machine learning training or clinical research.
Generated data must preserve referential integrity and keep the statistical and structural characteristics of the original dataset, while protecting sensitive information. The Syntho platform, equipped with smart de-identification features and consistent mapping, makes this level of data transformation possible.
Before fully committing, it’s wise to test a sample of artificial data. Inspect the created datasets for potential errors and inaccuracies, as well as consistency and reliability for different dataset sizes. Automated assessment tools can help you spot discrepancies between the generated and real data.
The platform should be flexible enough to handle all kinds of scenarios, even those beyond its original intended use. Have your team experiment with different use cases before committing. For example, a clinical research team might also want to have artificial datasets tested for marketing purposes or security algorithm training.
Synthetic data companies on your list should support different file formats and database types. Most business software can handle traditional formats like CVS, JSON, and XML, as well as SQL and NoSQL databases. But it’s always a good idea to double-check the documentation or confirm it with the provider. Some companies also offer APIs to integrate their platform with your existing workflows and formats.
Focus on data privacy requirements
Synthetic data is entirely artificial and contains no trace of the original PII. This means it’s not subject to GDPR (UK-GDPR), HIPAA, and the California Consumer Privacy Act.
How to confirm that? Request documentation on the company’s synthetic data generation process. Make sure the provider has relevant certifications and undergoes regular third-party audits.
Another smart move is testing the generated output to check for original identifiers. As an extra precaution, try to re-identify the artificial data by looking at combinations of attributes and alongside other datasets.
Check for the ease of use
A user-friendly interface is a must for synthetic data software. Look for a provider that makes it easy to generate synthetic data on different operating systems, even if you’re not a coding expert. We recommend focusing on software with drag-and-drop features and AI-enhanced scanners to identify PII in datasets automatically without requiring too much manual input.
The software should integrate with your existing IT infrastructure and business tools with minimal disruptions or refactoring. Ideally, the synthetic data company you partner with should offer assistance during setup to ensure it aligns with your workflow.
Expect your provider to offer detailed manuals and training to help your employees use the tool effectively. And don’t forget about technical support, which should be easily accessible whenever you need it.
Synthetic data providers vs. open-source vs. custom software: key considerations
Each option presents its own considerations and trade-offs, addressing diverse needs and priorities within organizations. So, let’s explore how commercial software and custom tools stack up against open-source tools in synthetic data generation.
Open-source tools
Free, open-source synthetic data generation tools are the most budget-friendly. Another major perk is that you can modify the code to fit your needs. Open-source projects often boast active developer communities where users can seek advice and share solutions.
However, even though open-source tools are low-cost and handy, they don’t always provide high-quality data. They also lack the advanced automation capabilities found in their commercial counterparts. For example, they rarely offer built-in features to assess or optimize the generated output.
What’s more, these tools are complex and usually demand a certain level of coding skills. You will probably need a dedicated IT expert to set up, configure, and maintain them.
By the way, at Syntho, we recently conducted a comprehensive comparative analysis of our platform vs. open-source synthetic data generators. You can read about the criteria and conclusions in this article.
Commercial software
Commercial synthetic data software caters to business needs. It’s usually designed for users without deep technical expertise. Business-focused solutions often have intuitive interfaces, pre-built workflows, and templates.
Synthetic data companies make sure their software integrates with other IT infrastructure and CI/CD tools. Vendors also offer ongoing technical support and take care of software maintenance so it remains effective and secure over time.
These platforms can be deployed on-premise or accessed through cloud-based subscription services. The implementation process can differ depending on your company’s size and complexity. Finally, business tools offer a range of pre-built customization options, but they might not cover all possible use cases.
Custom development
Organizations might consider building synthetic data generation tools to meet their unique operational needs. However, this route makes practical sense only if existing synthetic data solutions don’t work with their specific data types, formats, or data governance standards.
Developing a tool like this takes time and money. And after it’s built, you must take care of its maintenance and updates. Worse, there’s no guarantee that your custom machine-learning algorithm will generate compliant, high-quality data.
Given all that, partnering with an experienced synthetic data company is typically the best option for most organizations. Below is a shortlist of the top seven providers we recommend for the job.
Top 7 synthetic data generation companies
These companies have been carefully selected based on their expertise, reliability, and effectiveness in providing synthetic data generation services.
Syntho offers a smart synthetic data generation platform, helping organizations turn data into their competitive advantage. By giving access to all synthetic data generation methods on one platform, Syntho offers a comprehensive solution that covers:
- AI-generated synthetic data that mimics statistical patterns of the original data using artificial intelligence.
- Smart de-identification to protect sensitive data by removing or modifying PII.
- Test data management to create, maintain, and control representative test data for non-production environments.
Syntho platforms integrate into any cloud or on-premises environment. The company handles planning and deployment as well as trains the user’s employees to use Syntho Engine effectively. Post-deployment support is offered, too.
- Smart De-Identification features protect sensitive information by removing or modifying PII using intelligent algorithms.
- PII Scanner: Automatically identifies PII, ensuring compliance and privacy protection.
- Synthetic Mock Data: Substitutes sensitive PII, PHI, and other identifiers for the highest level of privacy.
- Consistent mapping: Preserves referential integrity across the data ecosystem for data consistency.
- Test Data Management features preserve referential integrity in the entire relational data ecosystem.
- De-identification and synthetization: Make it possible to create test data that mirrors production data, facilitating thorough testing and development in real-world scenarios.
- Rule-based Synthetic Data: Allows generating synthetic data based on predefined rules and constraints.
- Subsetting: You can trim down records to create smaller, representative subsets of relational databases while maintaining referential integrity.
- AI-Generated Synthetic Data features mimic statistical patterns of original data using artificial intelligence (AI).
- Quality Assurance (QA) report: Helps assess generated synthetic data for accuracy, privacy, and speed.
- External evaluation by SAS: Data experts at SAS evaluate and approve synthetic data to guarantee its reliability and quality.
- Time series synthetic data: You can synthesize accurate time series data that follow the trends and patterns in the original data.
- PII scanner for open texts.
- Connectors seamlessly integrate with both source and target data, supporting an end-to-end integrated approach.
A fixed monthly subscription price will depend on the chosen feature set, and a free demo is available to confirm the high quality of synthetic data before fully committing.
Mostly AI simplifies compliance with data privacy laws when creating artificial data in various formats.
Key features:
- No-code UI: A user-friendly interface lets you create synthetic data without writing code.
- Python integration: Using the API, you can integrate synthetic data generation directly into Python workflows.
- Upload your dataset: After generating synthetic data, the system deletes the real data you preloaded.
Thanks to its intuitive web-based user interface, even users without technical expertise can easily navigate the platform.
There are a few downsides, though. Some features are lacking. You can’t customize the output based on mood ratings or hierarchy. The platform provider offers limited guidance, so mastering its capabilities may take time. Finally, the pricing policy is not fully transparent.
This tool can generate privacy-preserving synthetic data for machine learning and research. The provider supports cloud deployments for scalability and on-premise installations for companies with strict security policies that require extra isolation.
Key features:
- Differential privacy: Tonic AI uses differential privacy techniques to add noise to the data, making it statistically similar to the original data.
- Real-time generation: Teams can generate synthetic data on demand to get a constant stream of test data.
- Explainability: Tonic AI has an explainable machine learning model that allows you to control parameters to get the desired output.
The company offers limited support for certain use cases and specific databases, in particular, Azure SQL. Creating and maintaining custom scripts might require the assistance of dedicated IT professionals.
K2view is a software suite that integrates with relational databases, flat files, and legacy systems. It operates multiple data generation and anonymization techniques to preserve the referential integrity of datasets with minimal adjustments.
Key features:
- A variety of anonymization methods: With K2view, you can create synthetic data using a wide range of anonymization techniques, such as data masking and tokenization.
- Integrations: The company provides manuals and APIs to help you integrate the K2view into your development and machine learning training pipelines.
- Rule-based approach: Massive datasets can be generated on demand to cater to different business needs.
The company offers custom pricing plans and a free trial to explore its offerings. While the platform does not demand any programming skills, it does come with a steep learning curve.
Hazy can generate synthetic data in various formats, including structured (tabular) data, text, and images.
Key features:
- Metrix suite: Hazy includes a comprehensive range of metrics to evaluate synthetic data’s similarity, utility, and privacy compared to the original data.
- Secure deployment: Their software integrates seamlessly with existing infrastructure and data security measures, keeping production data secure.
The company provides dedicated support and onboarding.
On the downside, its pricing may be more affordable to larger enterprises rather than smaller or mid-sized companies. You’ll need to contact the company directly to get a quote.
Like other synthetic data companies, Statice creates artificial datasets from your original data, preventing re-identification and maintaining data utility. Their SDK offers preset profiles with APIs for easier data generation.
Key features:
- Scalable design: Statice offers a modular architecture that scales to fit your specific IT operational needs.
- Complex data structures support: The platform handles multiple relational tables, time series data, and other formats.
Non-technical users might find the command-line interface too complicated. The pricing is on the higher side, and you must reach out to the company to request a quote.
Gretel.ai allows you to synthesize time-sensitive tabular data and images. This synthetic data company provides a full suite of data management services, from model training to quality control. The company also hosts a community where other developers can share strategies or troubleshooting steps.
Key features:
- Verifiable data quality: Gretel includes reporting mechanisms to evaluate synthetic data quality based on custom metrics like privacy protection and machine learning quality.
- Open development community: The company maintains clear documentation, as well as SDKs and APIs for data scientists and software engineers.
This platform requires extensive customization via APIs or SDKs. Sadly, the company typically does not provide a free trial.
Quick comparison of synthetic data companies
| Company | Best Suited For | Compliance with CCPA, CPRA & more | User Experience | Pricing |
|---|---|---|---|---|
| Syntho | Covers AI-generated and test data management use cases. For companies of all sizes that need high-quality synthetic data, integration support, and training. | Intuitive | Transparent, flexible three feature-based pricing tiers. No consumption-based charges. | |
| Mostly AI | For small and medium-sized businesses that need flexible pricing. | Moderately easy | Flexible credit-based, with a limited free version. | |
| Tonic | For privacy-focused businesses with strict security policies. | Complex | Moderate, pay-as-you-go, and enterprise plans. | |
| K2view | For companies with advanced data testing and research. | Complex | Moderate, pay-as-you-go. | |
| Hazy | Enterprise-grade datasets for fraud modeling, customer engagement, and personalization. | Intuitive | Expensive (negotiated with a company).go. | |
| Statice | For enterprises that need structured data with a high level of privacy. | Moderately easy | Expensive (negotiated with a company). | |
| Gretel.ai | Developer-oriented data generation tool with extensive customization. | Moderately easy | Moderate, credit-based, and free options. |
Partner with time-tested synthetic data companies
Teaming up with experienced synthetic data companies is crucial if you want to integrate synthetic data solutions into your workflow seamlessly. The companies featured in this article have deep expertise and a proven track record in providing reliable, effective synthetic data generation services. You can tap into their industry know-how and tailored solutions to meet your specific data needs by collaborating with reputable synthetic data providers.
The shortlisted companies fine-tuned their offerings to cater to a wide range of industries and use cases—potentially including yours. The selection criteria and other hands-on considerations we described in detail here should help you choose the best provider for your specific needs.
Syntho is pleased to offer a comprehensive solution that covers a wide range of synthetic data generation methods. Our platform provides a package of high-quality synthetic data, de-identification techniques, and data management solutions. Please don’t hesitate to book a demo with our expert if you have any questions about its possibilities or would like to discuss how our product can address your business goals.
About the author
Business Development Manager
Uliana Matyashovksa is a Business Development Executive at Syntho, with international experience in software development and the SaaS industry, and holds a master’s degree in Digital Business and Innovation, from VU Amsterdam. Over the past five years, Uliana has demonstrated a steadfast commitment to exploring AI capabilities and providing strategic business consultancy for AI project implementation.
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