What is Synthetic Data?

Imagine you're running a large retail chain, and you want to analyse customer behaviour to improve your services. However, sharing real customer data might violate privacy laws. This is where synthetic data comes in—a type of data that’s artificially generated but mirrors the properties of real data. It allows you to study patterns without risking sensitive information.

 

What is Synthetic Data?

Synthetic data is essentially the data generated by algorithms that replicate the characteristics of real-world data. Despite being artificial, it maintains the statistical relevance and relationships found in the original data. Think of it as a stand-in actor in a movie, delivering a performance that’s nearly identical to the real person it represents. 

 

How is Synthetic Data Generated?

Synthetic data is typically created using advanced techniques like machine learning models, generative adversarial networks (GANs), or simulations. For example, in a retail scenario, a GAN might be trained on real customer purchase histories to generate synthetic ones that mimic the same buying patterns. It's like baking a cake—using the same ingredients but crafting a new, unique dessert every time. Also Read: What is market research? Meaning, Types and Examples

 

What are the Advantages of Synthetic Data?

1. Privacy Protection: Since synthetic data isn’t real, it eliminates privacy concerns. A healthcare company, for instance, could use synthetic patient data to train algorithms without exposing any real patient information.

2. Cost-Effective: Gathering and processing real-world data can be expensive and time-consuming. Synthetic data can be generated faster and with fewer resources.

3. Bias Reduction: Real-world data often comes with biases. Synthetic data can be adjusted to reduce or even eliminate these biases, leading to fairer and more inclusive models.

4. Scalability: If you need more data to train a model, you can easily generate more synthetic data. This is particularly useful in scenarios where acquiring real data is challenging or costly. 

Why is Synthetic Data Used?

Synthetic data is used to solve problems where real data is either unavailable, incomplete or too sensitive to use. Companies might need to simulate rare events, such as fraud detection or natural disasters, to ensure their systems can handle them. For instance, autonomous vehicle companies use synthetic data to simulate thousands of driving scenarios, ensuring their AI systems can safely navigate the real world. Check out this blog: What is Qualitative Research? Definition, Examples and Types

 

Examples of Synthesized Data?

Let’s say a bank wants to test a new fraud detection system. Rather than using actual customer transaction data, which could risk privacy breaches, they generate synthetic transaction data that mimics the real data’s patterns. This synthetic data can then be used to train the system, ensuring it performs well in detecting fraudulent activities without exposing any real customer information Here are three additional examples of synthetic data:

1. Autonomous Vehicles

Companies developing self-driving cars, like Tesla or Waymo, generate synthetic data to simulate various driving conditions. For instance, they create virtual scenarios with different weather conditions, traffic patterns, and road hazards. This data allows them to test how their autonomous systems react without the risks and costs associated with real-world testing. Synthetic pedestrians, other vehicles, and even animals crossing the road can all be part of these simulations.

2. Financial Market Simulations

In the finance industry, synthetic data is used to model market conditions and test trading algorithms. A hedge fund might generate synthetic stock price data that mimics real market volatility and trends. By running simulations on this data, the fund can refine its trading strategies without risking actual capital. This also allows for stress testing under extreme but plausible market scenarios.

3. Healthcare Research

Researchers in healthcare use synthetic data to study the spread of diseases or the effectiveness of new treatments. For example, a pharmaceutical company might create synthetic patient records that mirror the demographics and health conditions of real patients. This data can be used to run trials of new drugs, ensuring that the drugs are tested under a variety of conditions without needing to access sensitive real-world patient data.

 

What is the Difference Between Synthetic and Artificial Data?

While the terms are often used interchangeably, there’s a subtle difference. Synthetic data is generated to closely mimic real data, often used for privacy, scalability, or bias reduction. Artificial data, on the other hand, may not necessarily replicate real data but is created to simulate specific scenarios or outcomes. For example, synthetic data might replicate customer purchase patterns, while artificial data could simulate entirely new, hypothetical customer behaviours. Also Read: What are Consumer Insights: Meaning, Examples and Scope

 

History of Synthetic Data

Synthetic data isn’t a new concept. It dates back to the early days of statistical modelling when researchers used simulated data to test their theories. However, with the rise of big data and machine learning, synthetic data has become more sophisticated and widely adopted. In the 1990s, the healthcare industry began using synthetic data to protect patient privacy. Today, industries ranging from finance to autonomous vehicles rely on synthetic data for developing and testing advanced systems.

 

Use cases of Synthetic Data

1. Privacy-Preserving Data Sharing

• Healthcare: Sharing patient data for research or collaboration without violating privacy laws.

• Finance: Allowing banks to share transaction data with third-party vendors without exposing customer identities.

2. Training Machine Learning Models

• Autonomous Vehicles: Simulating diverse driving scenarios for training AI systems in self-driving cars.

• Retail: Generating customer behavior data to optimize recommendation engines without relying on real purchase histories.

3. Testing and Validating Systems

• Software Development: Testing software systems with large datasets without the need for real user data.

• Cybersecurity: Simulating cyber-attacks and defense mechanisms to improve security systems.

4. Bias Mitigation

• Hiring Algorithms: Creating balanced datasets that remove biases related to gender, race, or age for fairer recruitment processes.

• Loan Approval Systems: Generating synthetic applicant profiles to reduce bias in credit scoring models.

5. Data Augmentation

• Medical Imaging: Enhancing training datasets for AI models by creating additional synthetic medical images, such as X-rays or MRIs.

• Natural Language Processing: Generating synthetic text data to improve the performance of language models in underrepresented languages.

6. Stress Testing

• Financial Markets: Simulating extreme market conditions to test the robustness of trading algorithms or risk management strategies.

• Supply Chain Management: Modeling supply chain disruptions to assess the resilience of logistics and distribution networks.

7. Product Development and Testing

• Consumer Electronics: Simulating user interactions with new devices to test usability and performance.

• Gaming Industry: Creating virtual players or environments to test game mechanics before public release.

8. Regulatory Compliance

• Insurance: Generating synthetic claims data to meet regulatory requirements for data sharing and analysis.

• Telecommunications: Simulating customer call records to ensure compliance with data protection regulations while analyzing service quality.

9. Market Research

• Retail: Simulating customer segments and purchase behaviors to predict market trends and optimize marketing strategies.

• Automotive: Using synthetic buyer personas to gauge interest in new vehicle models or features.

10. Education and Training

• Medical Training: Using synthetic patient data to train medical students in diagnosis and treatment without involving real patients.

• Corporate Training: Simulating business scenarios to train employees on decision-making processes.

These use cases demonstrate how synthetic data can be applied across multiple domains to enhance data-driven decision-making while addressing challenges like privacy, bias, and data scarcity.

 

Closing Note

Synthetic data is a powerful tool that helps industries innovate and operate more efficiently while protecting privacy and managing costs. By creating realistic, artificial data, organizations can test systems, train models, and make decisions without exposing real, sensitive information. As technology continues to advance, the role of synthetic data will likely grow, offering new opportunities for progress and improvement across various fields. Embracing synthetic data can lead to smarter, safer, and more effective solutions in our increasingly data-driven world.

Did you know about our blog on 5 Differences Between Qualitative and Quantitative Research?