Synthetic Data vs Real Data: Which Is the Better Choice?
The synthetic data vs real data question is a crucial one for data specialists across industries like finance, insurance, healthcare, and e-government. This decision can significantly impact the success of machine learning models and data analytics projects.
Even with vast amounts of real-world data flowing in from countless sources, many organizations still struggle to turn that data into actionable insights. Real datasets often remain siloed, unstandardized, and constrained by data security and privacy regulations, making it difficult to unlock their full potential.
Synthetic data offers an opportunity to create datasets that simulate real-world scenarios, overcoming barriers like privacy concerns and data shortages. However, it is still perceived with some skepticism and a lack of understanding in certain circles.
If you find yourself caught in the dilemma of real vs. synthetic data, let us help you sort it out. In this article, we’ll break down the benefits, challenges, and key considerations to help you make an informed decision.
Table of Contents
Understanding Real Data
Real data captures true occurrences gathered directly from real-world activities and interactions. It’s sourced from production systems, vendors, public records, or other datasets that contain operational information. For example, it might include a decade-old backup with details about real individuals or transactions or a set of public records acquired for testing purposes.
Because real data mirrors actual events and interactions, it’s crucial for applications where precision and authenticity are essential. Its data points accurately represent real-world contexts, making it a reliable foundation for analytics and to train machine learning models.
However, real data has its challenges. It often includes noise, inconsistencies, and biases that reflect the messy nature of the real world. Managing real data also raises significant privacy and compliance concerns, as it frequently contains personally identifiable information (PII) that must be handled carefully under strict regulations.
The Pros and Cons of Real Data
We’ve touched on why it has long been practical to use real-world data in software development and analytical contexts, as well as some of its inherent limitations. To fully grasp its role, let’s explore its advantages and challenges.
The advantages of using real-world data
The ability of real data to capture the complexities and nuances of actual environments makes it a powerful tool to train artificial intelligence (AI) and to provide valuable insights through analytics. Here are several benefits that make its use especially advantageous:
- Authenticity: Real data accurately reflects real-world scenarios, making it invaluable for understanding user behaviors, market trends, and business operations. Its genuine nature allows analysts to derive insights that are grounded in reality.
- Richness of Detail: Real-world data includes natural variations, outliers, and subtle patterns that other types of data might miss. This richness can reveal unique insights, especially in fields like healthcare or finance, where even minor variations can significantly impact analysis results.
- High Relevance: Data sourced from real-world activities is directly applicable to the specific conditions it represents, making it ideal for training machine learning models and developing applications that are well-suited to real environments.
However, there’s always a flip side to the coin…
The drawbacks of real-world data
Since the entire machine learning process relies heavily on the data used to train and test models, it’s important to recognize the challenges that come with using real-world data—and they aren’t always easy to overcome:
- Privacy and Compliance Risks: Real data often includes sensitive information like PII, which requires strict adherence to data privacy laws, potentially limiting access and use.
- Data Quality Issues: It can be noisy, contain errors, and have inherent biases, which can distort analysis if not properly managed.
- Limited Availability: Obtaining real-world data, especially in large quantities, is no walk in the park. Even when you manage to gather it, the data may not cover all possible scenarios, making it less effective for broader applications.
- Hidden Costs: Real data is rare and often lacks transparency until after purchase, making it challenging, time-consuming, and potentially costly to assess its value entirely.
Considering these challenges, a practical alternative worth exploring is synthetic data.
Understanding Synthetic Data
Synthetic data is artificially generated, designed to closely replicate the characteristics and patterns of real-world data. It’s created using algorithms or models that simulate the statistical patterns and business logic of the original data, without containing any information directly tied to individuals or entities. This approach ensures that while the synthetic data retains the structure and insights of the original dataset, it remains free from privacy risks.
By the end of 2024, Gartner predicts that 60% of data used for AI will be synthetic, a significant increase from just 1% in 2021. This growth reflects the critical role of synthetic data in simulating reality, modeling future scenarios, and minimizing risks in AI development.
But what makes synthetic data such a promising and widely embraced solution? For many industries, enhanced data privacy is one of the most significant advantages of synthetic data. However, its benefits extend beyond privacy. In the next section, we’ll explore the key advantages that make synthetic data an increasingly valuable tool, taking into account its limitations and the ways to address them.
The Benefits of Synthetic Data
Synthetic data is transforming how organizations manage and analyze information by providing a safe, efficient alternative to traditional data sources. Let’s back up this statement with facts.
Greater control over the quality and format of the dataset
Synthetic data offers organizations the flexibility to create synthetic datasets that match their specific needs, ensuring both consistency and coverage of rare scenarios that might be missing in real-world data.
For instance, Syntho supports creating synthetic data across various complex data types, including time-series data and large multi-table datasets. Generating data with such a high level of flexibility allows businesses to simulate diverse time-based scenarios while handling structured tabular data typically found in databases and spreadsheets. Users can define specific conditions to produce datasets that closely match their unique needs, whether they require data in multiple languages, support for different alphabets, or geographic location data like GPS coordinates. This way, synthetic data works effectively for test data management, helping create realistic non-production environments that mirror actual data without risking the exposure of sensitive information.
In collaboration with SAS and the Dutch AI Coalition, Syntho analyzed the significance of using synthetic data to enhance data quality and improve the predictive capabilities of artificial intelligence across various applications.
The findings indicate that synthetic data holds not only basic patterns but also captures deep “hidden” statistical patterns necessary for advanced analytics tasks. The model trained on synthetic data demonstrates performance similar to using a real dataset, offering a scalable method for generating large datasets without the associated privacy risks.
Increased privacy and security for sensitive data sources
Synthetic data significantly enhances privacy and security, particularly in sectors like healthcare, where safeguarding personal information is essential. By generating data that reflects the statistical properties of real datasets without disclosing actual personal details, organizations can conduct analyses, develop AI models, and test applications without privacy risks. This “fake data” doesn’t relate to real individuals, minimizing the risk of sensitive data leaks.
The legal landscape for data privacy varies by jurisdiction, with numerous laws and regulations aimed at protecting personal data. While many are familiar with the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA), by 2023, there were 162 national data privacy laws and 20 active bills. Using synthetic data allows companies to reduce the risk of legal violations while still deriving valuable insights.
At Syntho, we also offer PII Scanner integration. This tool identifies and flags sensitive data within datasets, ensuring that real data is effectively managed and replaced with synthetic alternatives, further enhancing privacy and supporting compliance efforts.
Refined performance of machine learning algorithms
Synthetic data enhances machine learning performance by creating balanced datasets without exposing sensitive information, complementing real data. For instance, AI-driven fraud detection in finance often faces the challenge of data imbalance and limited fraud examples, making it hard for models to spot new threats.
A common solution is upsampling, which increases minority class instances to improve training. The use of synthetic data makes this process more effective by generating additional samples similar to real fraud cases while maintaining privacy. This provides models with diverse, realistic training data, significantly improving fraud detection in real-world scenarios.
A great solution when obtaining real-world data is challenging
Let’s take rare events, like specific medical conditions or niche market behaviors; collecting enough real data to train models can be nearly impossible. In finance, for instance, fraud cases are infrequent, often constituting just 7-10% of all transactions. This imbalance makes it hard to train AI models effectively, as most available data represents non-fraudulent activity.
Additionally, ethical and legal constraints in regulated industries can complicate data collection, further limiting access to essential datasets. Synthetic data steps in as a practical solution, allowing organizations to simulate and analyze scenarios without the logistical headache of gathering data in the real world.
Facilitates collaboration without exposing sensitive information
In healthcare, researchers can share insights derived from synthetic datasets that mimic real patient data without revealing actual identities, thus promoting cooperation on studies while adhering to regulations like HIPAA. Similarly, in finance, companies can use synthetic data to analyze market trends or customer behavior, enabling collaboration without exposing sensitive financial details.
By using synthetic alternatives, organizations can exchange relevant insights and data structures without the fear of compromising sensitive information. This builds trust among collaborators, fosters innovation, and supports joint ventures while maintaining compliance with data protection regulations.
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The Challenges of Using Synthetic Data
While synthetic data brings many benefits, it’s essential to recognize the challenges that may arise when you decide to create synthetic data for your projects. At Syntho, we understand these challenges intimately and have developed comprehensive strategies to address them effectively:
- Dependency on Real Data Quality: The effectiveness of synthetic data heavily relies on the quality and diversity of the real dataset it’s modeled after. If the original dataset lacks quality, the synthetic data generated will likely be flawed, resulting in ineffective outcomes.
- Accuracy and Representation Issues: Not all tools that allow you to generate synthetic data will ensure you preserve the statistical properties and referential integrity of real data. This shortcoming can lead to inaccurate predictions and misguided analyses. Organizations must conduct a thorough validation, comparing model outputs and performing stress tests to ensure reliability.
- Generative AI Hallucinations: AI algorithms used to generate synthetic data can sometimes “hallucinate,” producing misleading or incorrect data points that appear statistically sound. Regular human reviews in your data strategy are vital to catch these anomalies.
- Amplified Anomalies in Datasets: If the original data contains anomalies or outliers, there’s a risk synthetic versions could either exaggerate these issues or obscure them. This can lead to models that are overly sensitive to rare patterns, struggle to generalize to broader datasets, or miss critical events altogether.
Reliable platforms like Syntho mitigate these challenges with robust algorithms trained on vetted datasets, ensuring both statistical accuracy and compliance. Additionally, Syntho offers features that allow organizations to adjust synthetic data generation rules, scan for PII, and validate outputs, helping to achieve high standards of synthetic data quality.
What Is the Difference Between Real and Synthetic Data?
Having thoroughly explored the specifics of real data vs synthetic data, we’ve compiled a comparison table to summarize the key differences for your convenience.
| Aspect | Real Data | Synthetic Data |
|---|---|---|
| Definition | Collected directly from real-world events, interactions, or transactions. | AI-generated synthetic data preserves characteristics, statistical properties, and business logic from the real data. |
| Source | Gathered from sensors, user activities, transactions, surveys, etc. | Created using algorithms, simulations, or models like GANs (Generative Adversarial Networks). |
| Accuracy | Represents actual occurrences and real-world conditions, thus highly accurate. | Mimics the statistical patterns of real data. |
| Data Volume | Limited by real-world events and can be time-consuming and costly to collect. | Quickly transforms the existing data, making it ideal for scaling datasets quickly. |
| Privacy and Compliance | Includes Personally Identifiable Information (PII), requiring strict data protection measures (e.g., GDPR). | Free from PII by design, which simplifies compliance with data protection regulations. |
| Bias and Noise | Contains natural noise, biases, and inconsistencies inherent in real-world data collection. | Can be tailored to reduce or eliminate biases, though the risk of model bias still exists if not managed properly. |
| Use Cases | Best suited for applications where real-world precision is critical, like customer behavior analysis or medical diagnosis. | Ideal for testing and development with privacy-compliant test data, enhancing capabilities of data analysis, creating tailored product demos, enabling seamless data sharing without legal hurdles, supporting data monetization efforts, and accelerating AI model training through rapid prototyping and hypothesis validation. |
| Data Quality Control | May require significant preprocessing to clean and standardize. | Quality is dependent on the data generation model; can be customized to desired levels of quality. With Syntho’s Quality Assurance (QA) report, for instance, organizations can ensure their synthetic data is evaluated across three key metrics: accuracy, privacy, and speed. |
| Availability | Limited by the frequency and nature of real-world events; difficult to scale rapidly. | Instantly available once generated and can be scaled to meet the needs of various projects. |
Can Synthetic Data Replace Real Data?
Synthetic data comes with impressive benefits, especially in terms of privacy and streamlining testing and development. It allows organizations to create data that mimics real-world scenarios without putting sensitive information at risk. This makes a difference in finance, healthcare, and insurance industries, where protecting personal data is non-negotiable.
Real data in production poses distinct challenges. Its complex structures and unique edge cases are difficult to replicate fully, often leaving gaps in testing coverage. Real data may also misalign with evolving business rules, leading to inaccurate test results.
Additionally, data from interconnected systems can lack consistency and relational integrity, especially when updated or transferred independently. Handling real data further demands extensive manual work to anonymize and filter information, which drains developer time and increases risks with custom, often unstable solutions.
For more on synthetic data for test data management, read our detailed article here.
That said, the goal shouldn’t be to entirely replace real data. Instead, organizations should use synthetic data alongside real datasets, focusing on quality and representativeness. High-quality data is essential for effectively training machine learning algorithms. Techniques like upsampling can further enhance this blend, ensuring models are trained well and deliver richer insights and outcomes.
Synthetic Data vs Real Data: Which Is the Better Choice?
Synthetic data generation is an effective solution for organizations concerned about privacy, scalability, and rapid access to data for software development, machine learning, and collaboration. It allows you to simulate scenarios while ensuring compliance with privacy regulations, making it especially valuable in sensitive industries like healthcare and finance.
When you generate data synthetically, you create a flexible and secure alternative that can complement real data, which is essential for accuracy and representativeness.
The Syntho platform offers a variety of artificially generated data methods tailored to your specific needs, helping you choose the right synthetic data solution to drive innovation and foster trust in your digital practices. Book a free demo with Syntho today to discover how you can leverage this powerful resource!
About the author
Customer Service Engineer & Data Scientist
Shahin Huseyngulu has a strong academic foundation in Computer Science and Data Science and is an experienced Customer Service Engineer and Data Scientist. Shahin has held key roles in customer service, cloud solutions, and machine learning research, showcasing expertise in Python, SQL, and data analytics. Currently, Shahin excels as a Customer Service Engineer at Syntho, building and optimizing customer service operations while bringing a unique blend of technical and customer service skills to drive innovation and customer satisfaction in the tech industry.
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