Synthetic Data: A Reliable Weapon to Modernize Data Management

What is synthetic data? 

Synthetic data is artificially generated data that is a replication of real-world data in terms of its characteristics and statistical properties. Creating such data requires various algorithms such as machine learning models, simulations, or mathematical methods. As opposed to traditional data that is collected from real-world interactions, synthetic data is a fabrication of data that involves mirroring the patterns, distributions and relationships found in real data. 

Comparing synthetic test data with real test data 

S. No.	Detail	Real data	Synthetic data
1	Definition	Collected from actual sources/events.	Created artificially to replicate real data.
2	Privacy	May contain personal information.	Does not include personal information.
3	Availability	Requires considerable time and resources for data generation.	Generates data in enormous quantities, and that too, on demand.
4	Accuracy	Provides real insights to reflect true events.	Depends on how the data is generated.
5	Cost	Expensive for collecting, storing, and managing data.	A cheaper alternative for data generation.
6	Bias	Data collection process may contain biases.	Generates balanced datasets to minimize biases.
7	Application	Validates real-world applications and user behavior.	Suitable for training AI models and experimentation.
8	Ethical considerations	Involves ethical challenges related to data ownership, usage, and consent.	Fewer ethical concerns but requires responsible practices.

Significance of data management: the emergence of synthetic data 

In today’s digital world, data management is a vital function, where data is considered one of the most important assets for organizations across industries. With the ever-increasing volume, variety, and velocity of data, effective data management is necessary for ensuring data quality, accessibility, and security. The rise of synthetic data management techniques represents a significant evolution in how organizations approach data, offering new ways to address traditional data management challenges and unleash their full potential. 
Synthetic data is preferred for the following reasons:

• Enhances data security: Enables safe data sharing and analysis, reducing the risk of breaches to ensure data privacy. 
• Addresses data scarcity: Provides large volumes of data to suit the specific needs of the project. 
• Supports AI and Machine Learning: Allows creation of diverse datasets that can be used to train AI and machine learning (ML) models effectively. 
• Reduces costs: Automates data creation to reduce the operational expenses, streamlines processes, and improves efficiency. 
• Enables safe experimentation: Allows simulation of various conditions, new test scenarios, and validate systems. 
• Avoids biases: Creates diverse and representative datasets to mitigate bias and ensure the systems perform fairly across different scenarios. 
• Speed to market: Accelerated testing speeds up software delivery for better business outcomes. 

Diverse types of synthetic data 

The categorization of synthetic data depends on how the data is generated and the scenarios it serves: 

S. No.	Type of synthetic data	Definition	Use cases
1	Fully synthetic data	Generated from scratch using algorithms and does not include any traces of real data.	Scenarios where real-world data is not available, or if there are privacy concerns.
2	Partially synthetic data	Contains only certain portions of real data, which is usually not sensitive.	For data sharing or analysis which require preservation of individual privacy.
3	Hybrid synthetic data	A combination of both synthetic and real data.	Suitable for scenarios where real data is insufficient.
4	Anonymized synthetic data	Looks and feels like real data, but without any personal identifiable information (PII).	For regulatory requirements which demand strict privacy protections.
5	Statistical synthetic data	Generated using statistical models for capturing the relationships, distributions, and patterns.	Ideal for research environments where analysis requires large datasets.
6	Simulation-based synthetic data	Created using a simulated process.	For scenarios such as robotics, aerospace and more, where collection of real data is either impractical or impossible.

Synthetic data generation techniques 

1) Rule-based method

Employs domain-specific rules to generate synthetic data that aligns with real-world characteristics.

Tools used for rule-based synthetic data generation:

Tonic.ai

Offers rule-based data synthesis with customizable attributes, ensuring that generated data follows user-defined constraints. 

Mostly AI 

This platform provides both rule-based and machine learning-driven synthetic data generation. It is commonly used in highly regulated industries. 

YData 

Allows users to define custom rules for data generation while ensuring statistical properties that mimic real-world datasets. 

2) Simulation method

Creates artificial data by mimicking real-world processes or systems through computer models, in cases where real data is unavailable, sensitive, or costly to obtain. 

Tools used for simulation-based synthetic data generation:

AnyLogic

A multi-method simulation software that allows users to model and simulate complex systems using various modeling approaches, including agent-based, discrete event, and system dynamics. 

Simul8 

A simulation software that allows users to model and analyze complex systems using discrete event simulation, providing a visual modeling environment where users can create simulation models by defining entities, events, and processes. 

MATLAB Simulink 

Used for modeling, simulating, and analyzing dynamic systems, Simulink allows users to create block diagrams and models using a graphical interface, where each block represents a specific function or component of the system. 

NetLogo

A multi-agent programmable modeling environment used for simulating and exploring complex systems, providing a user-friendly interface and a programming language specifically designed for modeling and simulating agent-based systems. 

3) Generative model 

Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and other deep learning techniques are used to generate synthetic data that resembles real data distributions. 

Tools used for generative model-based synthetic data generation:

Synthesia 

A video synthesis platform that uses AI to generate realistic videos of people speaking in different languages and with different expressions, leveraging deep learning techniques to map the movements and expressions of an actor onto a target video. 

WaveNet 

Uses a deep neural network architecture to generate high-quality and natural-sounding speech waveforms. It is trained on a large amount of speech data and can generate speech in multiple languages and with various voices and styles. 

Gretel.ai

Includes customizable synthetic data generation pipelines, offering flexibility to create time-series, text, and tabular data, using generative models like GANs and VAEs to create privacy-preserving datasets. 

4) Data augmentation

Techniques which are applied to real data to create additional samples by applying transformations. 

Data augmentation synthetic data generation tools:

Albumentations 

An open-source library for image augmentation in machine learning and computer vision tasks, providing a wide range of image transformation techniques, such as random cropping, rotation, scaling, flipping, and color adjustments. 

Augmentor 

An open-source Python library used for data augmentation in ML and computer vision tasks, providing a simple and flexible way to apply various image augmentation techniques such as rotation, flipping, scaling, and adding noise to a dataset. 

Snorkel 

Used for programmatically generating training data for machine learning models, allowing users to label large amounts of data quickly and efficiently by writing labeling functions, which are heuristics or rules that assign labels to unlabeled data. 

5) 3D/2D image generation 

Computer vision and CGI-based image generation methods are used to generate synthetic data that closely mimics real-world data. 

2D/3D image synthetic data generation tools:

Unity Perception 

A computer vision and machine learning toolkit which is designed to enable developers to create intelligent applications and experiences using computer vision and machine learning algorithms. 

Blender 

An open-source 3D modeling and animation tool that can be used to create synthetic 3D image data for training models in computer vision tasks. 

NVIDIA Omniverse Replicator 

Designed to simplify the process of creating and distributing digital assets for virtual environments, enabling users to import, convert, and optimize 3D models, textures, and other assets in real-time simulations, virtual reality experiences, and other interactive applications. 

How is synthetic data implemented across different industries 

Healthcare 

In medical image datasets to augment training for the analysis of MRI and CT scans, X-rays, and disease detection. It helps improve the performance of AI models with limited real patient data while ensuring patient privacy. 

Robotics 

Enables the training of robot control and perception algorithms in simulated environments, reducing the need for extensive real-world data collection. This approach improves safety and provides a more controlled training environment. 

Manufacturing and quality control 

Helps train computer vision systems to identify defects in manufacturing processes and products. It improves quality control and is particularly useful when real-world defect samples are difficult to generate. 

Agriculture and environmental 

Used for detecting unwanted plants, insects, weather patterns, and soil conditions. It helps create models that predict crop yields and aids in environmental cleanup and protection efforts. 

Search and rescue 

Utilized in rescue operations and first responder scenarios to predict the locations of people lost or injured in remote areas. It helps first responders locate individuals faster and save lives. 

Aerospace and drone tracking 

Ideal for drone detection and tracking, both for viewing objects on the ground from above and tracking objects in the sky. It improves detection in challenging conditions such as weak contrast, long-range, and low visibility. 

Warehouse management 

Assists in optimizing warehouse operations by predicting product demand, improving inventory management, and optimizing product placement. 

Gaming and entertainment 

Aids in creating realistic and expressive animations for characters in video games and movies, improving natural language interactions of non-player characters (NPCs), enhancing the gaming experience. 

AR/VR  

Used to generate realistic virtual environments, improving user experience in virtual and augmented reality applications. 

Financial fraud detection 

Simulates diverse types of financial transactions, allowing financial institutions to train and improve fraud detection algorithms without the need for real fraud examples. 

Common myths and realities pertaining to synthetic data

1. Myth: Synthetic data is unrealistic. 

Reality: Because of advances in AI, synthetic data closely mirrors real data, making it valuable for testing and training. 

2. Myth: Synthetic data is biased almost always. 

Reality: Synthetic data can be engineered to reduce biases, creating fairer models. 

3. Myth: Synthetic data is only for testing. 

Reality: It’s also crucial for training, algorithm development, and simulations, especially when real data is scarce or sensitive. 

4. Myth: Synthetic data generation is complicated and costly. 

Reality: New technologies have made synthetic data generation more accessible and affordable. 

5. Myth: Synthetic data is random. 

Reality: Synthetic data is statistically designed to reflect real data, essential for AI training and realistic simulations. 

6. Myth: Synthetic data is not ideal for production. 

Reality: Synthetic data is suitable for privacy-sensitive applications like fraud detection, especially in a production environment. 

7. Myth: Synthetic data will replace real data. 

Reality: Synthetic data complements real data, particularly when data is limited or sensitive. 

Our offerings 

Qualitest’s test data management (TDM) self-service portal for synthetic data is a powerful tool that is powered by Azure OpenAI for ensuring fast, secure, and compliant data access, enabling teams to streamline their testing processes and drive continuous improvement across development cycles.  

We use test data management tools like GenRocket for extensive self-service capabilities, allowing testers to create, copy, and share test data cases, as well as apply different test data rules. These tools also provide a user-friendly, self-service web interface with intuitive navigation, real-time data preview, and built-in help throughout the user interface. By empowering testers with self-service capabilities, organizations can enhance their test data management process and accelerate their testing efforts. 

Read more about it: How Test Data Management is Evolving the Insurance Industry