As a data extraction expert with over a decade of experience, I‘ve seen firsthand the rising interest in synthetic data across industries. In this comprehensive analysis, I‘ll share my insider perspective on the key differences, benefits, challenges and ideal use cases for synthetic vs real-world data.
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Synthetic data refers to artificially generated data that mimics real-world data, as opposed to being gathered from actual events or surveys. With advances in generative AI and complex algorithms, high-quality synthetic data can now be created at scale.
According to Gartner, by 2030 synthetic data use will surpass real data in AI systems. This massive growth is being fueled by synthetic data‘s ability to enable simulation, bypass privacy restrictions, reduce costs, and accelerate development cycles.
As an expert in web data extraction, I‘ve worked on projects utilizing both real and synthetic datasets. In one recent client engagement for an e-commerce firm, we used a combination of real customer data from their databases along with supplemental synthetic data to provide the scale needed to train deep learning product recommendation models.
Based on my hands-on experience, here is an in-depth look at the key differences, benefits, challenges and ideal use cases for synthetic vs real data in 2024.
What is Synthetic Data?
There are a few core methods for algorithmically creating synthetic data:
- Generative AI – Models trained on real data that generate new examples.
- Parametric Models – Manually define data parameters to sample from.
- Logic-driven – Simulate entity interactions and events.
| Method | Description | Example |
| Generative AI | Train AI on real data, generate new | Healthcare records |
| Parametric | Sample data from statistical models | Customer profiles |
| Logic-driven | Simulate logical entity interactions | Fraud patterns |
The goal is to create synthetic data that retains the complexity, variability, and statistical patterns of real-world data, without exposing actual personal information.
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Properly engineered synthetic data can be remarkably similar to real data in terms of distributions and relationships while not containing any real personal data. However, there are still limitations in its ability to capture rare edge cases and outliers.
Key Benefits of Synthetic Data
Based on my professional experience, here are some of the top advantages of using synthetic over real data:
1. Bypasses privacy restrictions
Privacy regulations make it difficult to leverage real user data for many applications. Synthetic data avoids this by not containing actual personal information.
2. Enables simulation
Synthetic data can simulate scenarios lacking real-world examples. This enables testing complex edge cases.
3. Avoids statistical issues
Real data requires significant cleaning to fix gaps, errors, bias. Synthetic data is pristine.
4. Allows easy manipulation
Synthetic data can be engineered to have specific characteristics, unlike immutable real data.
5. Reduces costs
No need for ongoing data collection. Synthetic data can be reproduced on demand.
6. Speeds up development
Avoid delays from acquiring and cleaning real-world data.
7. Improves AI/ML training
Synthetic data helps overcome insufficient training data limitations.
According to Experian, using synthetic customer payment data increased models‘ ability to identify fraud patterns by up to 20%. Synthetic data is enabling breakthroughs across industries.
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While promising, synthetic data does have some inherent limitations to be aware of when considering its use.
Key Challenges with Synthetic Data
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No outliers – Synthetic data lacks rare edge cases present in real data.
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Questionable accuracy – Potential deviations if generation algorithms are flawed.
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Requires real data – Algorithms need some real data for initial training.
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Can introduce bias – Biases in training data get amplified.
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Hard to interpret – Patterns in synthetic data can be mysterious.
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Consumer skepticism – More education needed on sources of synthetic data.
Based on your use case, these limitations may or may not be prohibitive. Rigorous evaluation of synthetic data accuracy and potential bias is required.
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So when should you use real vs synthetic data? Here are some best practices based on use case:
Guidelines for Synthetic vs Real Data
Use real data when:
- Absolute accuracy is critical
- Small details are very important
- Leveraging complexity is key
Use synthetic data when:
- Data privacy is needed
- Requiring massive data scale
- Speed is important
- Simulating edge cases
For example, self-driving car training would benefit from leveraging both real driving data as well as supplemental synthetic data for rare events like accidents. On the other hand, testing a retail recommendation engine can rely more heavily on synthetic shopper data.
Choose the right type of data for your specific needs. In many cases, a blended approach is ideal.
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The future is bright for synthetic data across industries. According to Tractable, synthetic visual data could save over $2B annually for US auto insurers alone by enhancing vehicle damage AI models. Let‘s look at the outlook for synthetic data.
The Future of Synthetic Data
Adoption of synthetic data is accelerating with improvements in generative AI. According to Gartner, by 2030 synthetic data will surpass real data in AI systems. Some leading-edge applications include:
- Synthetic patient data – For clinical trials and medical research.
- Synthetic finance data – Allowing secure data sharing.
- Synthetic e-commerce data – Testing product recommendations.
- Synthetic sensor data – Training edge devices and IoT.
| Industry | Synthetic Data Use Cases |
| Healthcare | Patient records, clinical trials |
| Finance | Secure data sharing, fraud detection |
| Retail | Recommendation testing, ad targeting |
| Autonomous Vehicles | Training simulations, edge cases |
As techniques continue maturing, we‘ll see synthetic data drive breakthroughs across sectors as its advantages outweigh real data limitations. When generated responsibly, synthetic data can accelerate innovation.
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To wrap up, synthetic data presents game-changing advantages in bypassing data privacy restrictions, enabling large-scale simulations, accelerating development, and reducing costs. However, it is not a magic bullet and still has limitations around accuracy and interpretability compared to real-world data.
As an experienced data science practitioner, I recommend assessing your specific project needs and employing a blended real + synthetic data approach when feasible. With responsible use, synthetic data promises to enable tremendous innovation across industries in the years ahead.
To learn more about how synthetic data can transform your business, feel free to contact me to discuss your data strategy. The future of AI will be synthesized!