Synthetic Data vs Real Data: A Deep Dive into the Benefits and Challenges

As an AI consultant with years of experience working with data, I often get asked – should we use real or synthetic data for building machine learning models and performing analytics? In this comprehensive guide, I‘ll provide a detailed look at the differences between real and synthetic data, the upsides and downsides of each, and when one approach is better suited than the other for your needs.

Let‘s start by defining what we mean by real and synthetic data:

Real data refers to data gathered from real-world sources, such as customers interacting with a business, sensors recording information, lab results, website traffic, etc. It reflects actual events and transactions.

Synthetic data is artificially generated from computer programs and algorithms without direct real-world measurement. However, it is engineered to statistically resemble real data.

Both data types have their merits and limitations depending on the use case. In the following sections, I‘ll do a deep dive into how synthetic data is created, key benefits it provides, challenges to consider, suitable use cases, and an overview of synthetic data tools and vendors.

How is Synthetic Data Created?

Synthetic data aims to capture the statistical properties and relationships seen in real datasets without replicating the actual raw data. This allows generating unlimited artificial data while preserving the complexity and variability of real-world data.

There are a few primary ways synthetic data is algorithmically generated:

Generative AI Models

This method uses AI algorithms like generative adversarial networks (GANs) and variational autoencoders (VAEs) to analyze the patterns in real data distributions and then generate new sample data with similar properties.

GANs involve training two neural networks against each other to produce increasingly realistic synthetic data. A generator network creates artificial samples, while the discriminator tries to identify the generated data from real data. The two networks compete in this training process until the generator can reliably produce synthetic data that fools the discriminator.

VAEs work differently by learning to encode data into a lower dimensional latent space and then decode random samples from that space into synthetic data. The encoder and decoder networks are trained together.

These deep learning techniques allow automatically capturing highly complex distributions and dependencies within real data. The resulting synthetic data can be remarkably realistic.

Parametric Models

This technique relies on fitting parameterized statistical models onto real data distributions and then sampling randomly from those models to generate synthetic data. The models try to emulate properties like the mean, variance, correlations, etc. Simple examples would be sampling from Gaussian distributions or simulating time series data with autoregressive models.

The advantage is that parametric models are faster and have less data requirements compared to AI models. But they make simplifying assumptions and may miss capturing intricate structures.

Rule-based Models

In this approach, software engineers explicitly define schema, dependencies, constraints, and probabilistic rules that are used to randomly generate synthetic data. It involves carefully designing data tables, relationships, and generation logic.

The benefit is full control over the synthesized data characteristics. However, writing robust generation rules requires deep domain expertise and significant manual effort. This limits the complexity of data that can be simulated.

In practice, a combination of techniques is often used. The tradeoff is between data complexity and generation speed. AI models like GANs can produce highly realistic data but require more compute resources. On the flip side, parametric and rule-based models are relatively fast but result in less variability.

The choice depends on use case needs – for some like financial fraud detection, highly realistic synthetic transaction data is critical. For others like testing software on large-scale datasets, synthetic data diversity may be less important than generation speed.

The Many Benefits of Using Synthetic Data

Synthetic data provides a number of valuable advantages over relying solely on real-world data:

1. Bypasses Data Privacy Requirements

Real data containing personal information often comes under strict regulations like GDPR and HIPAA on what you can do with it. Synthetic data avoids this issue entirely since no actual personal data is used.

This means synthetic data can be freely used for secondary applications like improving machine learning models for your business without legal hurdles. You don‘t have to try anonymizing real data which runs the risk of re-identification.

2. Enables Predictive Modeling of New Scenarios

A key use of synthetic data is having datasets for situations that have not yet occurred historically, don‘t have sufficient real data, or would be infeasible to collect data for.

Examples include predicting pandemic spread under various public health measures, simulating rare biological events, or generating accident scenarios for autonomous vehicle testing. Synthetic data makes simulating these alternative realities possible.

3. Avoids Common Statistical Issues in Real Data

Real-world data often suffers from problems like:

  • Missing values
  • Measurement errors
  • Duplicate records
  • Outliers
  • Sparse classes
  • Sampling bias

These impede getting robust insights from analytics and machine learning unless painstaking data wrangling is done.

Synthetic data can be generated programmatically to be complete, realistic, and avoid these statistical pitfalls. This enables training more accurate models compared to models receiving messy real-world data.

4. Faster Than Collecting Data

Whereas obtaining real data can take months or years depending on the methods and sample size needed, modern synthetic data platforms can generate customizable datasets on demand.

This accelerates development cycles – for example, data scientists can iterate on different machine learning model designs and parameters in a fraction of the usual time.

5. More Consistent Than Real Data

Real data tends to have inherent randomness and variability across samples and batches. Synthetic data can be generated with finely controlled levels of noise and consistency.

Controlled levels of variability in the training data enables machine learning algorithms to learn more robust patterns, avoiding overfitting to noise. This improves model generalization on new data.

6. Easier to Manipulate and Modify

Synthetic datasets can be altered and simulated in different ways to generate derived datasets. Real data is often fixed once collected.

Having this flexible data allows testing machine learning models under varying conditions. For example, generating synthetic patient records with different comorbidities added allows analyzing the impact on disease predictions.

7. Cost Effective for Obtaining Large Volumes

While upfront investment is needed to build effective synthetic data models, once ready they enable virtually unlimited data at little incremental cost. Real data collection has fixed ongoing costs proportional to the data volume needed.

This low marginal cost allows affordable scaling of data as needed for data-hungry deep learning algorithms to maximize performance.

8. Ideal for Machine Learning Model Development

High-quality synthetic data leads to better model generalization in areas like computer vision, speech recognition, and natural language processing.

Unlike real data, synthetic data can be tailored to improve training – adding more diversity, balancing classes, simulating edge cases, etc. This extra data also reduces risks of overfitting.

According to recent Gartner surveys, 80% of data scientists using AI techniques agree that synthetic data is essential for training machine learning models. The ability to simulate limitless scenarios provides a data bounty that would be infeasible with real data alone.

"We are using synthetic data to enable training AI across use cases that are prohibitive with real data alone, such as predicting rare disease risks. This expands what‘s possible with AI." – Michael Docktor, Head of Synthetic Data Products at LifeData

Common Challenges and Limitations

However, synthetic data does come with some important caveats to be aware of:

Biased or Misleading Results

The AI and statistical models used to generate synthetic data may incorrectly capture patterns in the real data. Subtle characteristics could be missing or false relationships introduced.

Poorly constructed synthetic data can lead to biased analytics outcomes if the data fails to sufficiently reflect real-world complexity and diversity. Ongoing comparisons between synthetic and real data are needed.

Lower Accuracy

Even advanced synthetic data techniques tend to have lower fidelity compared to real data. Outliers and edge cases are often missed. The generated data remains an approximation.

Certain use cases like drug trials require very high accuracy and small effects detection. Synthetic data alone may be insufficient without being paired with real data.

Significant Computational Resources Required

Although the marginal cost per data point is low, creating the initial synthetic data models can require extensive data, expertise, and compute power. Iteratively improving the algorithms also takes applied research.

It may not be feasible for smaller organizations without big data infrastructure to build their own synthetic data capabilities from scratch.

Heavily Dependent on Real Data Quality

Since synthetic data aims to mimic real data characteristics, it inherits any flaws and biases present in the original training data. Low quality real data results in low quality synthetic data.

Organizations must be careful in assessing biases when collecting real-world datasets to train synthetic data models. Representativeness is key.

Consumer and Stakeholder Skepticism

As synthetic data use expands, public and regulatory scrutiny around its trustworthiness may increase, especially in sensitive domains like healthcare.

Transparency into data generation processes and auditability will become more important to address concerns over data accuracy and privacy. Demonstrating safeguards can increase consumer confidence.

When Should You Use Synthetic vs Real Data?

So when should you rely on synthetic data versus sourcing real-world data? Here are some general guidelines:

Synthetic Data Works Best When:

  • You need to simulate hypothetical scenarios with no historical examples
  • Regulations prohibit use of real personal data
  • Extremely large training datasets are required (millions to billions of points)
  • Absolute data accuracy is less critical than insights into general patterns
  • Rapid iteration on different simulated conditions is valuable
  • You want complete control over the data characteristics and balances

Real Data Is Better Suited For:

  • Highly accurate predictions are needed for your problem
  • Detecting small effects or outliers is key
  • Studying relationships between variables observed in the real-world
  • The training data requirements are moderately sized
  • You want to reproduce the exact distribution and variability of actual data
  • Lack of representation in the real data limits synthetic data effectiveness

Let‘s consider a couple examples:

  • For an insurance firm looking to model customer risk, synthetic data could effectively train predictive models while avoiding regulatory hurdles of sharing real customer data. However, real claims data may still be needed to accurately capture outliers and rare events.
  • An autonomous vehicle company needs to train computer vision algorithms on billions of images to recognize pedestrians, traffic lights, etc. across a wide diversity of environments. In this case, synthetic image generation is far more feasible than collecting so much real data.

The sweet spot is often combining synthetic data with smaller samples of real data, giving you the benefits of both artificial and actual data.

Real-World Synthetic Data Use Cases

Here are a few examples of synthetic data delivering value across different industries:

Healthcare

Many healthcare AI models require abundant training data to achieve robust performance, but privacy laws rightfully restrict access to patient data.

Synthea is an open source synthetic patient generator created by MIT which produces realistic but fake electronic health records, doctor visits, lab tests, prescribed medications, vital signs, etc.

This enables developing healthcare AI models while preserving patient privacy. Startups are now commercializing synthetic patient data.

Banking

Banks want to build AI systems to detect fraudulent transactions, determine credit risk, forecast loan defaults, recommend investments, and personalize financial advice for customers.

But regulations limit sharing actual customer transaction data and credit histories. Synthetic customer profiles and account activity generated by firms like FinGen allows modeling these scenarios without disclosing real identities or account details.

Insurance

Insurers rely on data to assess policies, model risks, and set premiums. Historical data alone makes it hard to underwrite new types of emerging risks.

Companies like AI.Reverie synthesize property, accident, climate, and business data to simulate unforeseen events and diversify risk scenarios beyond past observed claims. This expands insurability.

Retail

Retailers strive to forecast inventory needs, optimize pricing, identify customer microsegments, predict purchasing behavior, and personalize promotions.

Synthetic data incorporating simulated customer demographics, shopping histories, product preferences, pricing, and inventory levels enables developing more nimble demand forecasting and customer analytics models without using actual shopper data which raises privacy concerns.

Scientific Research

Generating synthetic datasets from simulated laws of physics and chemistry accelerates materials discovery and molecular design.

Researchers can train machine learning models to predict properties of new compounds and organic structures prior to running expensive lab experiments. This guides experiments to promising candidates.

An Overview of Synthetic Data Tools

There are a growing number of platforms tailored to different synthetic data needs:

Healthcare

  • Synthea – Leading open source generator of synthetic patient health records
  • Medical Image Annotation – Provides diverse synthesized medical imaging datasets
  • Genesis Healthcare – Simulates clinical, claims, and lab data for life sciences

Financial Services

  • FinGen – Synthetic customer, account, and transaction data for banking
  • LendingClub – Platform for generating synthetic loan applicant profiles and credit data
  • SynFi – Financial instrument and market data simulator for trading firms

Insurance

  • AI.Reverie – Property, accident, climate, and business data generator for insurers
  • Cogitate – Actuarial models to produce synthetic insurance data
  • Socotra – Customizable data platform for P&C insurance providers

Retail & Media

  • DataGen – Retail ecosystem data synthesizer from Experian
  • LatentView – Generates simulated customer data across industries
  • Replicant – Synthetic media content and datasets for publishers

Autonomous Vehicles

  • Udelv – Scalable simulated driving data generator for autonomous vehicles
  • Scale – Provides labeled synthetic LiDAR point clouds for AV sensor algorithms
  • VSI Labs – Generates synthetic radio signal data for training self-driving systems

General Purpose

  • Gretel – Privacy-preserving platform for de-identifying or synthesizing data
  • Mostly AI – Vertical agnostic synthetic data API for developers
  • Twingate – Securely generates synthetic data for mocking production databases

The key is choosing a platform optimized for your particular data needs and use case – whether that‘s patient records, retail transactions, loan applications, sensor feeds or otherwise.

Closing Thoughts on Synthetic vs Real Data

Synthetic data has evolved into a valuable AI enabler – fueling large scale training of machine learning algorithms and simulation of hard-to-collect data scenarios while overcoming many limitations of real-world datasets.

However, it is not a universal solution. Real data remains superior for achieving maximum accuracy and reliably capturing subtle signals when feasible to obtain.

Practitioners should carefully weigh the upsides and downsides of both synthetic and real data based on their specific analytics and product goals. Oftentimes, combining synthesized and actual data together provides the best of both worlds.

As generative AI models continue rapidly improving, synthetic data will open up even more possibilities that were previously constrained by data access. But thoughtfully collected real-world data will always be a grounding complement to guide synthetic data and validate its performance. The future likely involves seamlessly blending artificial and actual data in a privacy-preserving manner.

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