Synthetic data meaning: it is artificially generated data that mimics the characteristics and patterns of real-world data. It is created using algorithms or models based on existing data, without containing any actual information from individuals or entities.
Synthetic data is commonly used in various fields, including machine learning, data analysis, and software testing, to protect privacy, enhance data security, and overcome limitations in accessing or sharing real data.
Three synthetic data generation methods do exist within
the synthetic data umbrella
Mimic the statistical patterns, relationships, and characteristics of real world data in synthetic data with the power of artificial intelligence (AI) algorithms.
The AI algorithm learns patterns and relationships from real-world data to generate new, synthetic data that mimics these characteristics closely. This synthetic data is so accurate that it can be used for advanced analytics, acting as a “synthetic data twin” that functions like real-world data.
Use a Smart de-identification approach and allying mockers for substitution of sensitive PII, PHI, and other identifiers that follow business logic and patterns.
Syntho supports +150 different mockers that are also available in different languages and alphabets. Syntho supports default mockers like first name, last name, and phone numbers, but also more advanced mockers to generate mock data that could follow your defined business rules.
Use a Smart de-identification approach and allying mockers for substitution of sensitive PII, PHI, and other identifiers that follow business logic and patterns.
Syntho supports +150 different mockers that are also available in different languages and alphabets. Syntho supports default mockers like first name, last name, and phone numbers, but also more advanced mockers to generate mock data that could follow your defined business rules.
Dummy data, devoid of meaningful information, occupies space intended for genuine data without containing any valuable insights.
It serves as a placeholder in various contexts, including testing and operational scenarios. During testing, such data acts as placeholders or padding, ensuring comprehensive coverage of variables and data fields to prevent software testing complications.
What is synthetic data?
How does it work?
Why do organizations use it?
How to start?
Synthetic data is essential for addressing various challenges
in data-driven fields
Unlock data and valuable insights
Modern organizations gather extensive data amounts, but not all of the data is used due to its sensitive nature and personal identifiers. This addresses a significant challenge since the effectiveness of data-driven technologies depends on data availability. AI-generated synthetic data emerges as a solution to overcome this challenge. It offers a new approach to synthetic data that looks like real data.
Gain digital trust
Clients looking for assurance that their personal information remains secure and protected, and they value transparency and integrity from the businesses they engage with. Employing synthetic data is one solution through which organizations can foster digital trust and credibility.
Drive industry collaborations
Organizations continually seek opportunities for internal and external collaboration to drive innovation and maintain a competitive advantage. Challenges such as data privacy and data fragmentation slow down data sharing across various departments, organizations, and sectors.
Depending on your use-case, a combination of mock data, rule-based generated synthetic data or AI-generated is advised. This overview provides you with a first indication of which type of synthetic data to use.
The Syntho platform offers various artificially generated text data methods tailored for diverse scenarios, taking into account the data’s nature, privacy concerns, and specific use cases, allowing users to select the most appropriate options. A summary table provides an overview of these methods, detailing their relevance and use-case scenarios.
| Data generation method | Relevance | Example use case |
|---|---|---|
| AI-generated synthetic data | When statistical accuracy and maximum privacy are needed. | ML model training for feature dataset. |
| AI-generated synthetic time series data | When statistical accuracy and maximum privacy are needed for sequential data. | ML model training for time series dataset. |
| De-identification using Mockers | When working with large and complex databases for internal purposes. | Testing & development for production databases. |
| Rule-based-synthetic data (using Mockers and Calculated Columns) | When there is no real world data available yet, or to define custom business logic. | Simple test cases, or complex test cases that are not in production data. |
Syntho supports any form of tabular data and also supports complex data types. Tabular data is a type of structured data that is organized in rows and columns, typically in the form of a table. Most of the time, you see this type of data in databases, spreadsheets, and other data management systems.
Mimic (sensitive) data with AI to generate synthetic data twins
