Most people associate data collection with real data. Harvested from websites. Cleaned and tagged so that AI models can best make use of it. This article covers the difference between traditionally scraped web data and synthetic data.
When you’ve finished reading, you’ll be able to answer the following questions.
- What is synthetic data?
- What is web scraping?
- What are the pros and cons of both?
- Why do some teams use both data sources?
- Where are these data sources headed in the future?
Why data quality can make or break your AI project
AI has moved far past lab experiments and simple chatbot interfaces. AI models are generating product recommendations, summarizing legal cases and even handling customers in real-time with intelligence, not hardcoded, brittle logic.
Each day, our infrastructure becomes more and more intertwined with AI. Whether you’re training an AI model or building a Retrieval-Augmented Generation (RAG) model, you need quality data to meet your requirements. Each project comes with different needs. Traditionally scraped web data is often the right choice. In times where security and privacy are paramount, sometimes we need to depend solely on synthetic data.
In many cases, it’s appropriate to use both. Let’s explore the who, what, where, when and why for each of these data sources.
Two data sources driving modern AI models
| Feature | Synthetic Data | Scraped Data |
|---|---|---|
| Source | Artificially generated from patterns | Collected from live websites |
| Realism | Limited, idealized | High, messy, real-world |
| Compliance risk | Low | Medium–High, depends on data and jurisdiction |
| Use case fit | Testing, edge cases, simulation | Behavior modeling, language, up-to-date info |
| Maintenance | Minimal | Requires scraping infrastructure |
| Cost | Low once set up | Varies by scale and tooling |
What is synthetic data?
Synthetic motor oil doesn’t come from real crude oil. Synthetic data doesn’t come from real world data. All synthetic products are made by people. Synthesized to mimic the real thing and fulfill special use cases.
Generative models are trained on real-world data. They’re not designed to memorize individual data points. These models learn statistical patterns and relationships within the dataset. Once the patterns are learned, models are able to generate new pieces of data that reflect the original pattern.
There are a variety of use cases for synthetic data.
- Projection: Financial models often need to predict the future. To do this, they analyze past patterns and then project these patterns outward to create new, synthetic data that hasn’t yet come into the real world.
- Augmentation: In many cases, good data is difficult to come by. For instance, certain rare medical conditions may only be diagnosed once a century. From these smaller datasets, a larger, more diverse dataset can be generated for inference.
- Synthetic Environments and Simulations: Autonomous robots train in synthetic environments to avoid obstacles. Retailers often configure their shelving and displays using synthetic traffic and customer walkthroughs. Cybersecurity experts often use synthetic environments to simulate real attack vectors.
- Training and Education: Students and AI models can learn using anonymized datasets based on real patterns. They learn to make decisions without accessing sensitive data — building accuracy and competence before working with the real thing.
What is web scraping?
When we scrape the web, we use automation to collect real-world data. This involves fetching live webpages from the internet and parsing their data. Once the data’s been parsed, it gets cleaned, deduplicated and tagged with additional metadata for AI usage.
Most of your internet usage comes through HyperText Transfer Protocol (HTTP). This protocol involves several types of requests — GET, POST, PUT and DELETE. When scraping, we mainly use the GET request.
When browsing manually, your browser makes a GET to a website. Then, the server sends the HTML page back as a response. When scraping, instead of rendering and viewing the page with a browser, we extract its data using a parser. Sometimes this requires a static parser like BeautifulSoup. Other times we might need to render dynamic content using a headless browser.
Web scraping is the process of harvesting real data from the web. Most data used today is real web data. Scraped data can be either structured or unstructured.
Key considerations when sourcing your data
Before sourcing your data, you need to understand your project requirements. In almost every software engineering or Data Structures and Algorithms (DSA) course, there’s a long droning section about understanding your requirements.
Yes, it’s boring. However, you need to understand your requirements. This is not something to take lightly. In old fashioned waterfall developments and more modern Agile sprints, misunderstood requirements are the most common point of failure. Understand your requirements. Ask yourself questions similar to the ones below.
- Availability: Which datasets exist? If they’re small, unavailable or unreliable, you likely need to generate or collect some new data.
- Privacy: Are you dealing with sensitive data? Synthetic datasets can prevent leakage of sensitive information into your model’s output.
- Domain: Are you looking for domain-specific behavior? Synthetic data can simulate conditions, but often leaves out edge cases only shown in real-world data.
- Maintenance: How often does your data need to be refreshed? How often does new real-world data become available? E-commerce models often need to be updated weekly or more. Financial models often rely on real-time data to make their decisions.
Clear requirements lead to clear choices. When you understand your requirements, you can make better decisions. You can tailor your source data to fit your project needs. If you don’t understand your requirements, your project has already failed.
Pros and cons of synthetic and scraped data
Synthetic data
Synthetic data offers privacy, flexibility and fine-grained control. This level of control is both its highest selling point and its Achilles’ heel. Synthetic data gives you the power to highlight and amplify relationships. It also gives you the power to overlook subtle ones — humans miss things. Selective and accidental blindspots have been a consistent superpower of ours for thousands of years.
Pros
- Privacy
- Customization
- Augmentation, balance and diversification
- No scraping
- Less compliance overhead
- Ideal for edge cases and rare scenarios
- Testing and experimentation
Cons
- Poorly captures messy and organic behavior
- Limited by human bias and oversight
- Mirrors limitations within its training data
Scraped data
Scraped data comes from the real world. It’s messy. It’s real. It’s inflexible (without manipulation). Scraped data requires more work to make it usable, but the payoff is usually worth it. Imagine you’ve got a day trading AI agent. Would you want your agent buying and selling based on fictional pricing?
Pros
- Real-world patterns and language
- Organic variability
- Edge cases and anomalies
- Trains production-grade models
- Continuously updatable when sourced well
- Great for behavior modeling, market trends and RAG pipelines
Cons
- Requires cleaning, filtering and normalization
- Sensitive data can lead to compliance and legal issues
- Contains noise, duplicates and irrelevant data
- Context can vary wildly
- Sensitive data must be properly safeguarded to prevent it from entering the model
In the real world, we sometimes use both
Serious AI teams aren’t monogamous to a single data source. Synthetic and scraped data are often used in tandem, albeit for different and complementary purposes.
Unintended behavior is a universal curse in computer science. Most AI models are essentially massive black-box neural networks. We don’t know exactly why they do what they do.
Progress is slow and unpredictable. We’re similar to a caveman rubbing sticks together until they burn. He doesn’t exactly know why they burn — and we don’t fully understand why neural networks generate such powerful output.
Here’s an oversimplified view of the training loop.
- Give model data
- Model learns from data
- Did the model learn correctly?
- If no, back to step one, with more data. Otherwise move on.
It’s not unlike the caveman starting a fire.
- Rub sticks together
- Sticks get hot
- Smoke appears, but is there a flame?
- If no, back to step one. Otherwise, feed the fire.
Pretraining
- Synthetic: Synthetic data is just now taking off in pretraining. It lets us highlight relationships in much smaller datasets. You can inefficiently train on all of Wikipedia or use a synthetic version with the same relationships at a fraction of the size.
- Scraped: Scraped data gets used to teach the model real-world information. When a model learns language pop culture, history and modern ideas from its training data, it comes from scraped data. Books, articles and code repositories make up the backbone of the model.
Fine-tuning
- Synthetic: Synthetic data fills gaps when fine-tuning. If you want an expert day trading model, you’d give it ideal scenarios from which it can capitalize. When historical data is scarce or non-existent, it’s easier to feed it custom data than to wait for perfect conditions to emerge for scraping.
- Scraped: Models are trained with granular control. A chatbot learning finer points of language — like generational slang and different dialects — needs real-world data. Your model doesn’t decode Gen Alpha speak like “not gonna lie” (ngl) from synthetic data.
RAG pipelines and datafeeds
- Synthetic: After initial training, synthetic data rarely gets used in production. When testing and benchmarking, it’s perfect. It lacks chaos. It’s predictable. Intended output is easy to compare against actual output.
- Scraped: Most real-time datafeeds going into the model utilize real data. If a model needs news updates, it uses scraped data.
Future trends in AI data sourcing
As time goes on, the line between these data sources is starting to blur. Synthetic datasets are now often built using scraped datasets as a base. After scraping and cleaning, teams can synthesize new data to augment, balance or diversify the source data — while still including the original data.
Imagine you’ve got a recruiting bot with gender bias due to past data. Pretend it sees you’ve hired eight men and two women for a specific position. The model might conclude that men are better for the job — even though this conclusion is wrong. You don’t need to remove the men from the training data. You need to add synthetic data to balance out the dataset.
Synthetic data is not a replacement for scraped data. It’s a patch for blind spots and gaps that scraped data can’t cover.
Conclusion
There is no “best” data source for AI. The best one is the one that fits your use case. Hopefully, you wouldn’t use a drill to hammer a nail. You shouldn’t chain yourself to one type of data. You need to use each one when the time is right. Choose the right tool for the right job.