Synthetic data is artificially generated information that mimics the statistical properties and patterns of real data, but doesn't contain any actual records from real people or transactions. Think of it like creating practice invoices for training purposes. These invoices look realistic, with proper formatting, line items, dates, and amounts, but they don't represent any real purchase your company made.
Businesses use synthetic data primarily for two reasons: training AI systems and testing software without exposing sensitive information. For example, if you want to train an AI agent to process invoices, you could use synthetic invoices that look just like your real ones, complete with typical vendor names, product descriptions, and pricing patterns. This way, you're not risking exposure of actual vendor relationships, pricing agreements, or payment terms.
Synthetic data has become increasingly important as privacy regulations like GDPR and CCPA have made it harder to use real customer or financial data for testing and development. Instead of spending months getting legal approval to use real data, or risking a data breach during testing, companies can generate synthetic datasets that preserve the complexity and variety of real data without the compliance headaches.
For AI training specifically, synthetic data can also help address gaps in your real data. If your company rarely encounters certain types of exceptions or edge cases, you can generate synthetic examples of those scenarios to make sure your AI agent knows how to handle them when they eventually occur.
Synthetic data isn't just random numbers thrown together. It's generated using algorithms that analyze the patterns, relationships, and statistical properties of real data, then create new records that follow those same patterns. For example, if your real invoice data shows that 80% of invoices are under $1,000 and typically have 3-5 line items, synthetic invoice data would maintain those proportions. Random data wouldn't preserve these realistic patterns and would be useless for training AI or testing systems.
It depends on the use case. Synthetic data works well when you need volume, variety, or want to create specific scenarios that are rare in real data. For instance, if you want to train an AI agent to handle invoice discrepancies, but your actual data only has a 2% error rate, you can generate synthetic examples with various types of discrepancies. However, synthetic data should typically supplement real data rather than replace it entirely, because real data captures unexpected complexities and edge cases that are hard to anticipate when generating synthetic records.
The main risk is that synthetic data might not capture all the messy complexity of real-world data. Real invoices might have typos, inconsistent formatting, missing fields, or unusual vendor-specific quirks that synthetic data generators don't anticipate. If an AI agent is trained primarily on clean synthetic data, it might struggle when it encounters these real-world imperfections. Another risk is that poorly generated synthetic data could inadvertently recreate patterns that allow someone to reverse-engineer information about the original dataset.
Zamp addresses this by training AI agents on a combination of real customer data, properly anonymized data from across our customer base, and synthetic data to fill gaps. Our Knowledge Base also lets you teach agents how to handle specific quirks in your data by describing them in plain language, like "Vendor ABC always abbreviates 'quantity' as 'qty' in their invoices." This ensures agents can handle both the statistical patterns captured in synthetic data and the real-world exceptions that only actual data reveals.
Not necessarily. Accuracy depends on how well the synthetic data represents the real scenarios your AI will encounter. In some cases, synthetic data can actually improve AI performance by providing balanced examples of different scenarios. For instance, if your real data has 1,000 standard invoices but only 10 examples of credit memos, an AI trained only on real data might not learn to handle credit memos well. Adding synthetic credit memo examples creates a more balanced training set. The key is ensuring the synthetic data generation process is based on sound understanding of your actual data patterns.
Costs vary widely depending on complexity and volume. Simple synthetic data generation for testing purposes might cost a few hundred dollars using off-the-shelf tools. More sophisticated synthetic data that accurately mimics complex business processes, preserves relationships between fields, and handles edge cases can cost thousands to tens of thousands of dollars, especially if you need custom algorithms developed. Many modern AI platforms include synthetic data generation as part of their training pipeline, which can make it effectively free if you're already using those platforms.
Yes, this is one of its biggest advantages. Since synthetic data doesn't contain any real individual records, it typically falls outside the scope of privacy regulations like GDPR, CCPA, and HIPAA. This means you can use synthetic data for testing, development, and even sharing with third-party vendors without the same compliance requirements that apply to real data. However, you should still have your legal team review, because if synthetic data is generated poorly, it might still be possible to infer information about real individuals, which could create compliance issues.
Consider synthetic data when you're facing any of these situations: You want to test new software or AI systems but can't use production data due to privacy concerns. You need more training examples for rare scenarios that don't occur frequently in your real data. You want to share data with vendors or partners for development purposes without exposing sensitive information. You're building AI agents for a new process and don't have historical data yet. Or you need to create demo environments that look realistic but don't contain any actual business information.
Validation typically involves statistical comparison between synthetic and real data. You'd check whether the synthetic data has similar distributions, correlations, and patterns as your real data. For example, if real invoices show that office supplies vendors typically have lower dollar amounts than equipment vendors, your synthetic data should show the same pattern. You should also test your AI agent or system on both synthetic and real data to see if performance is comparable. If the agent performs well on synthetic data but poorly on real data, your synthetic data isn't capturing important real-world characteristics.