Best Synthetic Data Software (2024) for your needs

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My personal experience with data privacy legislation has shown me that firms are swiftly shifting towards the use of synthetic data. This change has been brought about by recent legislative developments. This shift came about as a direct result of recently enacted policies and laws, which are to blame for the development. Because it delivers insights and qualities that are equivalent to those of real data while also prioritising the preservation of users’ privacy, synthetic data is an excellent substitute for genuine data.

This is because synthetic data gives insights and qualities that are equivalent to those of real data. This form of data, which is derived from actual data samples, is produced by artificial intelligence (AI), which is responsible for its development. In practise, AI starts with an examination into the patterns, correlations, and statistical traits included within these sample data sets. This enquiry can be thought of as the groundwork for AI.

After undergoing a rigors training method, it is capable of generate synthetic data that nearly replicates the statistical properties of the original data while also protecting the identity of individual users. This is accomplished while maintaining the integrity of the original data. In the face of the challenges that are posed by severe regulations for data privacy, businesses have discovered that adopting this strategy is an efficient method to overcome such obstacles.

What is Synthetic Data?

A mathematical and statistical process known as synthetic data generation is carried out by machine learning models that have been trained using real-world examples of things, people, and environments. The output data, on the other hand, does not hold any sensitive information but does maintain the behavioural characteristics of the genuine data. Not only is the development of synthetic data an innovation, but it’s also a solution to problems with data modelling that are precise, secure, and cost effective.

Best Synthetic Data Software Comparison Table

Data generated by Amazon alone is over 1000 petabytes daily. Other IT and social media companies gather significant user data. Only a few tech giants own these real data. However, startups and smaller firms lack such abundance. Using synthetic data to train prototypes and generate models can be profitable. Sure, here’s the table with rows and columns exchanged:

Feature	Type	Data types supported	Deployment options	Key features	Website Link
Hazy	Synthetic data platform	Structured, unstructured	Cloud, on-prem	Real-time data generation, data masking, data anonymization, data augmentation	Visit Website
MOSTLY AI	Synthetic data platform	Structured, unstructured	Cloud	Real-time data generation, data masking, data anonymization, data augmentation	Visit Website
MDClone	Synthetic data platform	Structured, unstructured	Cloud	Real-time data generation, data masking, data anonymization, data augmentation	Visit Website
CA Test Data Manager	Synthetic data platform	Structured	Cloud, on-prem	Data subsetting, data masking, synthetic data generation	Visit Website
Synthetic Data Vault	Synthetic data platform	Structured	Cloud	Synthetic data generation	Visit Website

Best Synthetic Data Software

Every day, Amazon creates more than a thousand petabytes of data. Data from users is collected in large quantities by other IT and social media companies. Few tech companies have access to the actual information. Startups and smaller businesses, however, face a scarcity of these resources. Synthetic data can be useful for training prototypes and creating models.

Hazy

Feature	Description
Data Anonymization	Hazy offers robust data anonymization capabilities.
Privacy-Preserving AI	It enables the creation of privacy-preserving AI models.
Data Masking	Hazy supports data masking for sensitive information.
Synthetic Data Generation	The platform can generate synthetic data for testing.
Compliance Tools	It provides tools to ensure compliance with regulations.

In my own experience with Hazy, I’ve found that it to be extremely outstanding. Hazy is a pioneer in the field of AI that protects users’ privacy and specialises in the development of synthetic data. Because of their technology, the organisations I’ve worked with have been given the ability to capitalise on the promise of data while also ensuring stringent data protection and compliance, particularly with rules such as GDPR.

The capacity of Hazy to generate synthetic data that closely resembles the distributions of real data is something that struck me as very impressive about the company. Because of this, we have been able to carry out secure testing, analysis, and development without ever putting sensitive information in jeopardy.

MOSTLY AI

Feature	Description
Synthetic Data Generation	MOSTLY AI excels in generating high-quality synthetic data.
Data Privacy	It prioritizes data privacy and GDPR compliance.
AI Integration	Easily integrate synthetic data into AI development.
Data Quality Control	Ensure data quality with built-in control mechanisms.
Versatile Use Cases	Applicable in various domains like healthcare, finance, etc.

I’ve had the chance to use the synthetic data platform that MOSTLY AI provides, and I can honestly say that it’s changed the game. MOSTLY AI is a frontrunner in this space, and the solutions they provide have allowed my team to maximise the use of our data while maintaining strict confidentiality standards.

Their artificial data is extremely realistic, retaining all of the statistical characteristics of our primary data. This artificial data has proven to be quite useful for a variety of applications, such as the training of machine learning models and the sharing of data, all while adhering to applicable privacy standards.

MDClone

Feature	Description
Data Transformation	MDClone offers advanced data transformation tools.
Healthcare Analytics	Tailored for healthcare, it excels in analytics.
Collaboration	Collaborative features for data sharing and analysis.
Data Governance	Ensure data governance and compliance.
Real-time Data Updates	Real-time updates keep data current.

From what I’ve seen, MDClone has completely transformed the way that healthcare data is utilised. The healthcare organisations that I’ve worked for have been able to generate synthetic, de-identified data copies with the assistance of their platform. It is critical that the confidentiality of the data be maintained, while at the same time allowing for valuable insights and innovative approaches to be taken in healthcare research, analysis, and decision-making.

CA Test Data Manager

Feature	Description
Test Data Generation	Efficient test data generation for software testing.
Data Masking	Protect sensitive information with data masking.
Data Subsetting	Create subsets of data for testing purposes.
Data Compliance	Ensure compliance with data regulations.
Self-service Interface	User-friendly interface for easy data management.

In my experience with test data administration and the development of synthetic data, I have found CA Test Data Manager to be an indispensable tool. The process of creating, masking, and provisioning test data has been significantly simplified as a result of the implementation of this all-encompassing solution. It has given us peace of mind by insuring the safety of sensitive information throughout the phases of project development and testing that we are currently undertaking.

Synthetic Data Vault

Feature	Description
Secure Data Storage	Safe storage and management of synthetic data.
Data Access Control	Granular control over data access and permissions.
Synthetic Data Quality	Focus on maintaining high-quality synthetic data.
Data Versioning	Track and manage different versions of synthetic data.
API Integration	Integration support for seamless data usage.

In the course of my research and work pertaining to data privacy, I have found the Synthetic Data Vault to be an invaluable resource. The storing and management of synthetic data sets that were produced by a variety of tools and platforms has been made much easier thanks to this safe repository. Because it centralised synthetic data assets, it made accessing them, sharing them, and complying with regulations much easier.

As a result, it has become an essential component for businesses like mine that place an emphasis on both the protection of personal data and the development of new products and services.

Key Features to Look for in Synthetic Data Software

• Methods for Creating Data: Look for software that lets you create data in a number of different ways, such as through statistical modelling, machine learning tools, or custom rule-based approaches. It’s important to be able to pick the way that works best for your data.
• Diversity and Realism in Data: The fake data should have a lot of the same features as real data, like statistical distributions, patterns, and connections. It should be varied and show how complicated real-world data is.
• Keeping your privacy: To keep people from being able to find themselves again in the original data, make sure that the software has strong privacy protection features like differential privacy or k-anonymity.
• Ability to grow: Check to see if the software can quickly create fake data for big datasets. Scalability is very important for dealing with big data situations.
• Making changes and having control: Find tools that lets you change how the generation works. You should be able to change the characteristics, correlations, and constraints of data to fit different use cases.
• Support for Data Formats: Make sure the software can handle different types of data, such as organised data (like CSV and SQL databases), unstructured data (like text or images), and semi-structured data (like JSON and XML).

How to Choose the Right Synthetic Data Software

• Write down your goals: Make your goals and aims for using synthetic data very clear. Figure out the exact situations and use cases where you’ll generate fake data.
• Privacy and safety of data: Figure out how private and safe your info needs to be for your project. If you’re working with private or sensitive data, you should look for software that has strong privacy and anonymization features.
• Complexity and Type of Data: Think about whether the data you need to create is organised, semi-structured, or unstructured. Think about how complicated the data is as well, such as how many attributes and connections it has.
• Quality of the Data and Realism: Check to see if the software that creates synthetic data can make data that closely resembles the statistical properties and spread of data from the real world. For modelling and analysis to be correct, the data must be of high quality and reflect reality.
• Making changes and having control: Figure out how much power you want over the process of making the data. You can fine-tune some software by setting up data schemas, constraints, and connections, among other things.
• Ability to grow: Check to see if the program can handle the amount of data you plan to use. When working with big datasets or a lot of info at once, scalability is important.

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