Best Synthetic Data Generation Tools
Synthetic data is disrupting machine learning, testing, and privacy. In place of working with real-world data—which is noisy, expensive, and privacy-intrusive—numerous firms and researchers are going over to artificially created datasets. These tools create realistic-looking information that mimics true models without exposing confidential information. If you are looking to get an idea of the best tools available on the market today, you are in the right place. We’ll explore the best tools available for generating synthetic data, how they work, and what might be the best tool for you.
What Makes a Good Synthetic Data Tool?
Before we get on with the tools themselves, let’s talk about what makes a synthetic data generator useful. The best tools don’t throw out random text or numbers. They need to:
- Produce high-quality, realistic-looking data reflective of true-world tendencies
- Ensure privacy through avoiding the re-identification of a person
- Be scalable in order to handle large datasets
- Accommodate a broad range of data including structured (e.g., databases) and unstructured (e.g., text and images)
- Offer personalization in order to personalize parameters in accordance with specific requirements
With that in mind, let’s examine a handful of the greatest tools currently available today for producing synthetic data.
1. K2View
K2View is a heavyweight in the space of data management and with the ability to create synthetic data, a leader in the space. All other tools are really only looking at how they are going to create a synthetic dataset, but what is special about what K2View is doing is how they are embedding generating synthetic data in a DataOps platform. It is not only creating fake data but how efficiently they are doing and how they are getting the data where it is going.
One of the major advantages of K2View is the way in which it goes about synthetic data generation in real time. In a departure from having access to pre-populated databases, it produces synthetic records in real time based on rules and structure. It is especially useful in banking, telephony, and health care where there is a requirement for real-time testing and regulatory compliance.
Another major benefit is its entity-oriented method. K2View doesn’t examine rows and columns; it views data in the way we think about them – as related entities, e.g., customer, transactions, or devices. What this produces is synthesized data with preserved relations and patterns, rendering it much more realistic and useful in tests and analytics.
2.MOSTLY ARTIFICIAL
MOSTLY AI is one of the biggest names in synthetic data. If what you need is high-grade, AI-powered data anonymised but appearing and performing in every other way identically to real data, then here is the answer. It is applicable in finance, health, and telephony sectors where privacy laws are strict and real data is hard to harness.
MOSTLY AI uses deep learning to understand the structure of real-world datasets and produces brand-new data with the same statistical properties. The result? Synthetic data that performs in every way identically without the privacy risks. And because it is compatible with pipelines, it is a viable solution for companies.
3.Synthetic Data Vault (SDV)
If you’re looking for a free and open-source solution, SDV is a top pick. It was developed in the Data To AI Lab at MIT and is packed with features to design, analyze, and evaluate synthetic data.
One of the biggest benefits of SDV is that it is compatible with a variety of models, e.g., relational databases, time-series, and single-table. Since it’s open-source, you are free to customize it to fit what you need. And while there is a little coding, it’s a useful tool in the hands of anyone who wants the power and flexibility.
4. Tonic.ai
Tonic.ai calls itself the “fake data company,” but don’t let the name misguide you. It is a legitimate platform for generating high-grade synthesized data. It is mostly used in software development and testing, where the programmers need realistic data without exposing customer information.
Tonic.ai works by taking existing datasets and reconstructing them in a synthesized state with the same structure and distribution. What this makes possible is allowing developers to test their apps with realistic-looking data without having to care about compliance. It is a DevOps darling and supports CI/CD pipelines nicely.
5. YData Fabric
YData Fabric is a viable solution for data scientists who would prefer having more control over synthesizing data. It is based on data quality and is aimed at allowing users to generate high-quality synthesized datasets that boost machine learning model performance.
What makes YData Fabric unique is how it caters to imbalanced datasets—so in the event there are gaps and biases in your live data, the tool is capable of correcting them with added synthesized data. It’s particularly helpful in the process of developing AI models, where a balanced and diverse dataset is a key indicator in how accurate a model is.
6. Synthea
If you’re in the health care field, Synthea is a tool you need to get familiar with. It’s a master at creating realistic-looking patient data in the guise of true electronic health records (EHRs). The catch? It’s fully open-source and made to mimic life in every way.
Synthea doesn’t just produce random names and diseases; it simulates patient health over a lifetime with medical histories, treatments, and outcomes. It is useful in a variety of ways in health care research, medical machine learning, and simulation of policies. Since patient information is under lock and key under strict privacy laws, Synthea fills in the gaps with a realistic and secure substitute.
7.Gretel.ai
Gretel.ai is yet another AI-powered synthetic data platform with a focus on privacy and security. It is built with and intended for developers, data scientists, and businesses who need to safely generate and share synthetic data.
One of Gretel’s greatest strengths is text data generation, something other tools really struggle with. If synthesized structured data, time-series data, even text-based datasets are what you need, Gretel’s got them. And with available APIs, integrating with other tools is painless and seamless.
Bottom Line
No matter what tool you choose, synthetic data is here to stay. It is revolutionizing industries with information made available, secure, and easy to manage. If developing AI, software testing, or research is what you are doing, there is a tool with synthetic data available for what you are looking for. Got a favorite synthetic data generator we didn’t list? We’d love to hear about it!
