What is synthetic data?
Guide into synthetic data types and meaning
Introduction
What is synthetic data?
Synthetic data meaning: it is artificially generated data that mimics the statistical 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.
Types of synthetic data
Three synthetic data generation methods do exist within the synthetic data umbrella. Those 3 types of synthetic data generation: are fully AI-generated synthetic data, synthetic mock data, and rule-based synthetic data. We shortly explain what the differences.
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 is trained on real world data to learn characteristics, relationships, and statistical patterns. Subsequently, the model generates entirely new data. Key difference, the AI model mimics the characteristics, relationships, and statistical patterns of the actual data in the synthetic data, and to such an extent that the generated synthetic data can even be used for advanced analytics. That is why Syntho refers to this as a synthetic data twin, it is synthetic data that can be used as if it is 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
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 are the benefits of synthetic data?
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.
What type of synthetic data to use?
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. |
Use cases for synthetic data
Challenge
Data-sharing issues (i.e., legal delays, untapped valuable data) cause project setbacks.
Challenge
Your demo data may be suboptimal, leading to missed opportunities during product demonstrations.
Challenge
Data monetization faces significant challenges, including ensuring data privacy and compliance, maintaining data quality and integrity, and implementing robust data governance practices.
Challenge
Model development process. Starting the DS projects requires data access and data understanding. The data scientists do not always have full data access and it is hard to initiate new projects.
Supported data type from Syntho
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.
Complex data support
- Time series data
- Large multi-table datasets and databases
- Any language (Dutch, English etc.)
- Any alphabet (English, Chinese, Japanese etc.)
- Geographic location data (like GPS)
Save your synthetic data guide now!
- What is synthetic data?
- Why do organizations use it?
- Value adding synthetic data client cases
- How to start
