Synthetic data is transforming machine learning, software testing, and data protection. Instead of dealing with real data—messy, expensive, and replete with privacy issues—numerous researchers and businesses are now using artificially created data. There are such software that create data that appears genuine and adheres to real patterns without exposing sensitive data.
If you are searching for the best synthetic data software out there, you’re at the right place. Below are seven of the top options, with information on how they work, and which might suit your needs.
What Makes a Great Synthetic Data Tool?
Before we get into the list proper, let’s talk about how a good data generation software differs. It’s not just a question of throwing random words and numbers. The best ones need to:
- Protect privacy by avoiding re-identification of persons.
- Create realistic data that mirrors real-world trends.
- Be efficient with managing large data.
- Support various data types from structured databases to unstructured text and image data.
- Offer users customization for tailoring the data based on their needs.
With the basics now covered, let’s review some of the leading synthetic data generation tools available today.
1. K2view
K2view isn’t just a synthetics data generator—it’s a full DataOps platform. Most solutions focus on generating fake data alone, but K2view takes it a step further by incorporating data creation into end-to-end data management operations.
One of the most impressive aspects is the real-time data generation. Instead of employing existing sets of data, K2view dynamically produces synthetic records based upon some set of rules and schema. It’s a game-changer for industries like banking, telecommunications, and healthcare where real-time testing and compliance are critical.
One of the biggest benefits K2view has is its model based around entities. Instead of working with data as isolated rows and columns, it organizes them into useful entities like customers, transactions, or devices. This makes the resulting synthetic data significantly more realistic and valuable for analysis.
2. MOSTLY AI
MOSTLY AI is one of the biggest brands when it comes to synthetics data, and it’s no surprise. It uses the capabilities of AI-based technology to produce anonymised data indistinguishable from the real data. If you’re working within a highly regulated space—finance, medical care, or telecommunications are good examples—this is something you’ll want to take into consideration.
With deep learning, MOSTLY AI works with actual-world data sets and produces fake duplicates with the same statistical properties. The result? Data as good as the original but with no privacy issues. And it integrates with data pipelines seamlessly, which makes it a smooth solution for businesses looking to automate their data operations.
3. Synthetic Data Warehouse (SDV)
If you want a free and open-source solution, SDV is the way to go. It is a highly featured tool built by the Data to AI Lab at MIT. One of the biggest benefits of SDV is how versatile it is. It can process various models like relational databases, time-series data, and single-table data. Since it is open-source, it can be tailored to your exact needs. While it does require some coding experience, it’s a very valuable tool for someone who requires absolute control over their data synthetics.
4. Tonic
Tonic.ai positions itself as the “fake data company” but don’t be fooled—this is a serious platform for the creation of high-quality fake data. It’s a favorite among software developers and testers who need realistic data without infringing on customer privacy.
Tonic.ai works very smartly as follows: it converts real data sets into restructured synthetic counterparts with the same patterns and distributions. This way, developers can test apps with data which looks and behaves exactly the way the real data does, without the risk of compliance. It also integrates well with CI/CD pipelines, and therefore is a good choice for DevOps teams.
5. YData Fabric
If you are a data scientist who requires tighter control over the synthesis of data, YData Fabric is a good option. It’s built with a strong focus on data quality and allows users to create high-quality data sets synthetically for improving machine learning model performance.
One of the most striking features of YData Fabric is the way it can handle imbalanced data sets. If your real-world data is missing critical demographics or biased data, YData Fabric fills the gaps with synthesized data. This makes it highly valuable for the creation of AI where balanced and diverse data sets are critical for accuracy.
6. Synthe
If you are within the healthcare industry, you must get familiar with Synthea. It’s an open-source program exclusively developed for the purpose of simulating realistic patients’ data mimicking electronic health records (EHRs). The best part? It does not create random illnesses and names—it mimics the overall patients’ health across a lifespan with medical histories, treatment plans, and outcomes.
As medical data is extremely regulated, Synthea plays a vital role by producing fake data that researchers and developers can use without compromising privacy regulations. Whether developing medical machine learning models, carrying out medical studies, or modeling policy, this software is vital.
7. Gretel
Gretel.ai is a secure and privacy-first platform for artificial data. It’s intended for data scientists, developers, and businesses who need to create and share artificial data securely.
One of the strongest features of Gretel is the capability to generate text-based synthetic data, which most other solutions struggle with. Whether structured data sets are required, time-series data, or text synthetics, it delivers. It also offers API integrations, which are easy to integrate into current workflows.
Closing Thoughts
Synthetic data is changing the game and making data accessible, secure, and easy to use across industries. Whether you’re developing AI models, testing software or apps, or researching, there’s a solution for your requirements with synthetic data. Is there a popular tool we didn’t mention? We would like to hear from you!
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