When I say we are what they call us, I mean we are our data ourselves. You fill out a hospital form; you put yourself in the data form. Max is calling customer service; he tells them who he is – now he is there in the form of data. But what about his privacy? Your privacy? You gave up on your info; what if they misuse it or use it in any health science or data science experiment?
To address this concern, data scientists are developing a technique known as synthetic data generation. Synthetic data generation creates real, fake data that replicates real information. In other words, it creates fictional people with characteristics similar to real people but without any real personal information.
Need for synthetic data generation
Every day, data scientists are performing hundreds of experiments, training AI models, and developing new software. For all this to happen in an uninterrupted and smooth flow, they need a clean and continuous supply of data sets. That has no privacy concerns.
To perform experiments, test software, train models, and for deep learning with synthetic data, data science is trusting this technique. Not only because it produces a large set of data in no time, but it also sticks with privacy regulations like GDPR – General Data Protection Regulation and CPPA – Canadian Consumer Protection Act. According to these laws, one must make sure not to involve the personal data of any individual for experimental purposes and maintain transparency.
Let’s discuss the top 11 tools or platforms that are being used to generate synthetic data. First, let’s roll down to the basics.
What is synthetic data?
Synthetic data is machine-generated data. This artificially generated data set has all the statistical and mathematical characteristics of real data. It can be available in a structured format like emails or an unstructured format like images and text. This data is useful in data science experiments and to train and feed AI/ML models because this data doesn’t have any privacy concerns.
The emerging need for synthetic data generation tools
Due to the increased debate on privacy concerns regarding the data used to train AI models and perform experiments. Businesses are diverting their focus to generating easy, valuable, and clean data that will be available in large sets and has no privacy allegations.
With synthetic data generation, businesses are not only avoiding the privacy concerns of using sensitive data but also producing valuable and quick results. For all this to happen, there are certain tools available for free or at a cost in the market, synthetic data generation tools. Also, some platforms provide these data generation services to businesses.
11 tools that will help generate synthetic data; Under your budget
There are a lot of tools that assist businesses in generating synthetic data sets economically. Out of them, we have shortlisted 11 majorly used tools that are producing synthetic data for experiments, algorithm development, analysis purposes, and performing deep learning with synthetic data.
1- MOSTLY AI
MOST.AI is a no-code platform for synthetic data generation. Most of the privacy-sensitive industries like insurance, banking, and telecommunications are using MOSTLY.AI for synthetic data generation because it creates data that is most closely identical to real data. They claim to be the pioneers in this synthetic data generation industry. They have a user-friendly interface that allows you to generate data as per your consideration. With MOSTLY.AI, you are not only producing artificial data with the highest accuracy but also mitigating risks, reducing analogy detection and biases.
With its powerful APIs and easy integration, MOSTLY.AI makes it easy to integrate synthetic data into existing workflows, making data more useful and accessible across organizations.
2- MDClone
MDClone solves data analysis challenges in the healthcare industry by providing powerful synthetic data generation solutions. It solves privacy issues related to the use of real patient data through the creation of synthetic datasets that protect patient privacy.
MDClon is built on the ADAMS framework, which allows for collaboration, research, and innovation without sacrificing data sensitivity. It supports structured as well as unstructured data. Healthcare professionals can use medical terminologies interchangeably and analyze the results through visualizations. With MDClon’s ability to create unlimited synthetic data, healthcare professionals can conduct extensive research and share their findings.
3- Ydata
YData’s data-centric platform accelerates the development of your AI solutions and maximizes your return on investment (ROI). With YData, you can enhance your training datasets and increase their robustness and performance.
Data scientists can leverage automated data quality analysis, as well as advanced synthetic data generation techniques, to enhance the performance of your datasets.
YData goes above and beyond in terms of data quality and data biases by generating data that is free from any personally identifiable information (PII).
It protects your privacy and complies with PCI DSS regulations that reduces identity leakage and the risk of re-identification during inference attacks.
4- Sogati
Sogeti’s Artificial Data Amplifier (ADA) solves the problem of creating real, anonymous data for different applications. This AI-driven tool goes beyond mere data generation.
ADA can analyze and comprehend any type of data (structured or unstructured) to create synthetic replicas that retain the original data’s key characteristics. Unlike its competitors, ADA uses deep learning for unparalleled accuracy.
Do you need large datasets for engineering projects, quality assurance, or research? ADA generates them efficiently while complying with stringent privacy regulations such as GDPR. With ADA, you receive high-quality, anonymous data customized to your specific requirements, all without manual configuration.
5- Gretel.ai
What sets Gretel.ai apart from other synthetic data generators is that it combines privacy, control, and versatility. Gretel’s privacy engineering as a service (PaaS) platform creates statistically accurate synthetic data sets that never touch sensitive customer information. Using machine learning, Gretel analyzes and generates fresh data in real-time. And using differential privacy it ensures no original data is leaked.
Gretel combines its neural network model with a privacy-friendly NLP – Natural Language Processing algorithm to protect sensitive information. And this is how its robust APIs handle a wide range of data types, including text and images, as well as time series, relational databases, and more. Due to its strict privacy protection policy, it has been widely used in industries like banking, healthcare, and gaming for synthetic data generation without sacrificing privacy.
6- Tonic.ai
Tonic.ai makes it easy to prepare your data for development and testing. How? Because it protects your privacy by anonymizing the data.
Tonic’s system uses cutting-edge AI, specifically GANs, to automatically anonymize your database while preserving valuable data relationships and patterns. This eliminates manual work and enables data scientists to work on smaller, easier-to-manage datasets through its unique cross-data setting feature. This platform emphasizes security with powerful mathematical techniques such as differential privacy.
7- Gen Rocket
GenRocket is an all-in-one TDM solution with more than 700 data generators for application testing. GenRocket’s modular and flexible design makes it highly scalable and self-serve, so developers and testers can generate synthetic data sets wherever and whenever they want.
GenRocket’s self-serve architecture gives you full control over the data generation process, allowing you to set up several parameters to meet your test case requirements. The tool also supports dynamic data generation based on your test application. With eighteen query generators, you can query data directly from SQL or a NoSQL database in real-time. Genrocket’s generator can generate synthetic historical data as well. It integrates seamlessly with any TDM tool you need.
8- Hazy
Hazy solves one of the biggest challenges FinTech companies face: leveraging data for innovation while preserving customer privacy.
Hazy’s synthetic data generator creates real-time, anonymized financial information that enables you to build and validate analytics with confidence. Hazy’s GDPR-compliant approach with differential privacy means no real customer data is ever compromised, even when sharing important insights.
9- BizDataX
Whether you work as a security officer, banker, or data analyst, you need to protect sensitive data during development and testing. The best method involves using a platform that generates real-world synthetic data to replace real Personally Identifiable Information (PII), complying with GDPR.
Automated features make it easy to identify sensitive data across various databases, apply masking techniques to protect it, and optimize database sizes for testing without sacrificing data integrity.
So, as you develop or test, take comfort in knowing that you’re using a platform that instills confidence through empowerment.
10- Datomize
Datomize is a fast-growing artificial intelligence (AI) and machine learning (ML) platform that specializes in creating high-quality fake client data for banks.
Not only does Datomize solve complex data structures from enterprise servers. It also uses AI/ML to pull behavioral patterns and create completely new datasets that simulate real-world scenarios. Unlike traditional approaches, datomize anonymizes these synthetic twins, protecting privacy while allowing banks to develop and validate AI models efficiently. Datomize’s technical expertise, compliance, and financial industry focus make it an invaluable asset for any bank looking to harness the full power of synthetic data.
11- Synthesized
Synthsized.io is an AI-driven data development platform that simplifies DataOps and allows users to generate high-quality synthetic data. It solves issues such as missing value, sensitive information, and data bias with its core functions: Data Provisioning, Augmentation, Secure sharing, and Collaboration.
The Synthesized SDK is one of the key features of the platform. It is an automated, data-generating model that allows you to transform and anonymize your data while keeping it relevant and bias-free. The SDK integrates seamlessly with the Google Colab platform, making it a great place for data scientists to collaborate with engineers and use deep learning libraries together.
The Open-Source FairLens library is another key feature of the SDK. It helps data scientists gain insights, detect bias, and promote the fair use of data.
The platform caters to different user groups. Research and development teams, as well as enterprise customers, can use the platform to learn more about synthetic data creation. Data engineers can use functions such as data anonymization to repurpose data.
Synthesized has become popular in insurance and banking where data scientists get faster and better results compared to traditional methods.
How GeRocket help in synthetic data generation
GenRocket is a self-serve platform that enables software test engineers (STEs) and developers (developers) to create synthetic data in real-time. Gen Rocket’s self-serve model helps you in on-demand synthetic data generation based on your test case’s specific needs.
Unlike other platforms, GenRocket doesn’t rely on data masking or profiling to create synthetic data. All data is generated synthetically. This means that you don’t have to worry about data privacy when creating synthetic data. GenRocket also eliminates the need for self-serve provisioning.