Strict data privacy regulations have compelled companies to transition to using synthetic data, the ideal substitute for real data, containing similar insights and properties yet is more privacy-safe compared to the latter. It is a form of AI-generated information created from real data samples. The AI first learns from the sample data's patterns, correlations, and statistical properties. Once trained, it creates statistically identical, synthetic data.
What Is the Purpose of Synthetic Data?
The purpose of synthetic data is to serve as a substitute for real-world data, particularly to address concerns related to privacy. Some recent cases involving AI firm OpenAI and tech giant Microsoft have highlighted the risks of collecting private and personal information from unsuspecting individuals.
Companies typically turn to traditional data anonymization techniques, which compromise the utility of the real data due to the encryption of private information. However, with AI-generated synthetic data, privacy is preserved along with its utility.
Synthetic data generators provide companies with the flexibility to generate scalable and diverse synthetic datasets for analysis and modeling without being constrained by existing legal regulations. This versatility allows companies to leverage the right tools or platforms to generate synthetic data to overcome privacy restrictions and offer maximum use for its insights and correlations.
By using synthetic data software, companies can create substantial copies of sensitive and valuable data assets without trading off the utility for data anonymization, like the data assets in healthcare or finances. Synthetic data offers accessibility without overstepping the strict privacy regulations set by social institutions, enabling companies to share and collaborate safely without exposing privacy or sacrificing the datasets' utility.
Listed below are the top 5 best synthetic data software of 2023:
1 MOSTLY AI
Overview
Established in 2017, MOSTLY AI revolutionizes data optimization by leveraging synthetic data. The company is at the forefront of structured synthetic data creation, introducing a groundbreaking concept known as smart data. Smart synthetic data offers features beyond privacy-enhancing, such as data augmentation, smart imputation, rebalancing, and downsizing.
This innovation empowers teams to overcome ethical challenges associated with real-world data usage. By harnessing AI-generated synthetic data, organizations can leverage computer-generated information that is both private and scalable while also being cost-effective.
Recognizing the limitations of traditional data anonymization methods, MOSTLY AI has developed an advanced AI-powered platform capable of transforming real-world data into secure and intelligent synthetic data.
Features
MOSTLY AI's synthetic data generator offers the highest accuracy compared to open-source synthetic data generators, like SDV. The platform is designed to be user-friendly, catering to non-technical users with minimal coding knowledge. Regular audits and updates by industry experts ensure data security and privacy.
Data Collaboration with Automated Privacy
MOSTLY AI facilitates safe and instant sharing on its synthetic data generation platform through automated privacy features. This enables data owners to collaborate without compromising the confidentiality of their information, thereby reducing the time required for data sharing while preserving data utility.
Data owners can confidently and efficiently synthesize data directly from databases, as an automated privacy and quality report accompanies each dataset. This report provides comprehensive insights into privacy metrics, statistical distributions, and correlations, enabling data owners to assess the quality of the synthetic data. Additionally, data synthesis can be limited to specific parts of the database, ensuring faster synthetic data generation.
Data Rebalancing for Data Exploration
Synthetic data enables the production of diverse datasets, capturing a wide range of scenarios and outliers beyond real-world data. On MOSTLY AI's platform, data owners can perform data rebalancing to explore the database and its stored data, gaining a deeper understanding.
Smart Data Imputation
MOSTLY AI's synthetic data generator excels in replacing null values through intelligent data imputation. Missing data points are synthetically imputed with relevant values, improving data granularity and readability for better analysis and understanding.
Dataset Flexibility
MOSTLY AI offers flexibility in synthetic data generation. For expedited explorations and reduced resource consumption, data owners can downsize large datasets into statistically identical, smaller counterparts. This approach minimizes costs and energy usage while accelerating analytics and data-centric processes.
Integrations with Enterprise Systems
MOSTLY AI seamlessly integrates its synthetic data generator with enterprise systems, supporting MySQL and connecting with popular cloud storage providers like AWS, GCP, and Azure, as well as cloud databases such as Google Cloud SQL and AWS Aurora.
Extended Support for Different Data Types
MOSTLY AI's platform supports various data types, enabling diverse applications. Data owners can synthesize geolocation data to gain insights into automotive behavior, generate synthetic text, and create mock data for generating test cases beyond the scope of the original data.
In addition to serving the tech industry, MOSTLY AI provides solutions for sectors such as banking, insurance, telecommunications, and healthcare. For instance, in 2022, MOSTLY AI collaborated with InGef, the Institute for Applied Health Research in Berlin, and other institutions to develop a healthcare data platform. The platform enabled access to shareable synthetic health records data for research purposes, addressing challenges related to data accessibility, biases, inaccuracies, and incompleteness in healthcare data.
MOSTLY AI incorporates proprietary technologies that ensure data protection and secure synthetic data generation, benefiting data owners, specialists, and non-technical users worldwide. From data anonymization to AI/ML model applications and collaborations, companies can rely on MOSTLY AI as one of the leading synthetic data software solutions in 2023.
2 Synthetic Data Vault
Overview
Synthetic Data Vault (SDV) is the open-source platform of DataCebo for synthetic data generation. Based in Boston, the company comprises industry experts from MIT with years of practice deploying machine learning systems. The SDV consists of libraries designed to help data owners to create tabular synthetic data.
Features
Machine learning models
The SDV contains multiple machine-learning models that allow anyone to create synthetic data. These models range from classical statistical methods to deep learning methods that can be trained to recognize patterns in the existing dataset.
Real Data Comparison
Data owners can compare synthetic against real data against a variety of measures and create quality reports from the evaluation to gain more insights and diagnose missing links.
Control, anonymize, and define constraints
SDV offers different types of anonymization and constraints in its synthetic data generation. Data owners can also control the data processing through synthesizers or machine learning models like Gaussian Copula, Day Z, CTGAN, and TVAE.
Besides synthetic data models, data owners can also leverage SDV for benchmarking and metrics to evaluate their models' output and define the synthetic data's statistics, efficiency, and privacy. While its models may lack creating highly realistic synthetic data in comparison to another platform, SDV is recommended for local performance testing.
3 Statice
Overview
Statice is a synthetic data company acquired by Anonos, a global security software provider specializing in enterprise data privacy, security, and enablement. For over a decade, the provider has spent time developing and validating technology capable of separating identity from its informational value while preserving its accuracy and speed. With Anonos, Statice aims to provide data protection software solutions for enterprises.
Features
Synthetic data generation
Enterprises can leverage the Statice platform to gain access to sensitive data by creating new artificial sets from their data. The platform also contains multiple machine learning models that adapt to any kind of data, including datasets with multiple tables and use cases.
Data usability preservation
The Statice platform maintains the statistical details of the real data. Enterprises can also easily compare the differences between real data and synthetic data in terms of their relationships and properties. Moreover, they can leverage its machine learning evaluations on their synthesis.
Privacy risks mitigation
The platform offers GDPR-compliant protection measures for privacy risk mitigation. Data owners can train the synthetic data to avoid risks of reidentification of real data. It contains PII detection mechanisms to handle all confidential information securely. Moreover, Static contains other risk assessment tools that verify the safety of its synthetic data from privacy threats like leaks, theft, or falsification.
Statice also offers integration into a company's server or cloud infrastructure, allowing automatic synthetic data generations that match its data operations and disclosure content.
4 Betterdata
Overview
Founded in 2021, Betterdata is a Singapore-based startup aiming to make data sharing faster and more secure through its programmatic synthetic data platform. It utilizes generative AI and privacy engineering to create and augment new datasets instead of traditional data-sharing methods that destroy data through data anonymization.
Features
Product Development & Testing
Betterdata improves product development and testing through its fast and realistic synthetic data generation, allowing users to innovate efficiently and deploy quality products.
Data Collaborations
Eliminate sharing sensitive data by using Betterdata to generate synthetic data for optimal data collaborations and reduce cost overhead by up to 70%.
Bias & Imbalance Mitigation
Biases and imbalances are removed with intelligent data rebalancing. Removal of biases indicates the use of fair and transparent AI models that comply with the best ethical practices.
Data Privacy Verification
Betterdata offers privacy-preserving synthetic data instead of heavily anonymized data with gaps in its information. Its AI engine enables data screening for potential threats, allowing users to make informed decisions on its confidentiality and exercise accountability on external auditors.
Overall, users can leverage Betterdata to protect sensitive data in compliance with standard security protocols set by GDPR and HIPAA. It's also the only software that prioritizes privacy-preserving synthetic data instead of heavily anonymized data with gaps in its information.
5 Datomize
Overview
Founded in 2020, Datomize emphasizes the accuracy of its machine-learning models to generate synthetic data with the lowest bias. The company's platform enables users to generate analytical datasets identical to real data, ensuring maximum value for analysts and engineers.
Features
Replicate
Datomize is equipped with AI-powered generative models capable of generating synthetic data extracted from the behavior of real data. It is also equipped with augmentation capabilities that enable users to do limitless resizing. By augmenting, users can improve their source data's overall quality and balance.
Due to its automated synthetic data generation, the platform also allows collaboration among users in need of synthetic replicas. Moreover, Database also contains dynamic validation tools for visualization between synthetic and real data and optional data mapping options for improved structure.
Recharge
Datamize employs a data-centric approach to ensure optimal accuracy in its machine-learning models. Users are guaranteed substantial insights from the synthetic replicas generated by the platform. In addition, Datomize can also display the performance improvement of its classifiers and regressors based on evaluation methods and metrics.
Reinvent
Users can leverage the engine of Datomize to generate the exact analytical dataset needed for any scenario, allowing them to analyze trends, eliminate bias, and make informed decisions based on the analysis provided. By simply defining the rules or the context for the scenario, with the ability of its generative model, users can predict outcomes and solve problems before they can even happen.
Conclusion
Synthetic data is powerful in its ability to replicate and even enhance real data without compromising the quality of its source material. It has a range of applications beneficial for multiple sectors, especially in maintaining the objectivity of the data and confidentiality of information. With these top 5 best software for synthetic data generation, data owners, specialists, and even engineers can enhance privacy in the latest technologies and improve fairness in their service to the general population.
