Synthetic Data is the future

Author

Alex Brogan
May 25, 2024 • Read Time: 5 minutes

Good morning to all new and old readers! Here is your Saturday edition of Capital Ideas, exploring an emerging trend in the world and how you can capitalise on it.

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Today’s edition: Synthetic Data

How to capitalise:

> 1: Horizontal generation
> 2: Vertical-Specific generation
> 3: Consulting services
> 4: Education-angle
> 5: Investment perspective

Cheers,
Alex

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Trend

Synthetic Data

Imagine having all the data you need to train your AI models, test your software, or make critical business decisions - without any of the privacy concerns or regulatory hurdles that come with using real data.

Enter synthetic data: artificially generated information that mimics the characteristics of authentic data. It's like having a digital sandbox where you can play around with realistic data without worrying about compromising sensitive information.

The synthetic data market is set to explode, and we’re already seeing this emerge:

Gartner predicts that synthetic data will make up 60% of the data used by AI and analytics solutions by 2030.

In general, there are a few principles to consider to understand where synthetic data is most useful:

  1. When real data is scarce or expensive to obtain

  • Synthetic data shines when getting real-world data is difficult, costly, or time-consuming

  • Examples: rare medical conditions, disaster scenarios, edge cases in autonomous driving

  1. When data privacy is paramount

  • Synthetic data allows working with realistic data without compromising individual privacy

  • Ideal for heavily regulated industries like healthcare, finance, and government

  1. For data-hungry applications like autonomous systems

  • Self-driving cars, drones, robotics need vast amounts of sensor and visual data

  • Synthetic data can efficiently generate huge virtual datasets to train these systems

The verticals where synthetic data can be applied are virtually unlimited. A few examples include:

Vertical

Detail

Examples

Healthcare and pharmaceutical firms

Require medical data for AI diagnostics, drug discovery, clinical trial simulations, etc.

Synthetic data provides HIPAA-compliant datasets without compromising patient privacy

UC Davis Health using synthetic data to forecast disease incidence 

Insilico Medicine using it to accelerate drug discovery

Financial institutions

Need transaction, market, and customer data for fraud detection, risk modeling, algorithmic trading

Synthetic financial data enables data sharing and predictive analytics while ensuring privacy

JPMorgan Chase Bank built a data mesh solution to streamline secure data sharing

Insurance companies

Require policyholder and claims data for risk assessment, pricing, and product recommendations

Synthetic data allows privacy-safe predictive analytics and saves time on data prep

Provinzial, a German insurer, built a customer analytics engine using synthetic data

With the rapid rise of AI and the increasing importance of data privacy, synthetic data is poised to unlock massive opportunities across industries.

While the field is relatively new and opportunities are emerging, there appear several ways to ride this wave:

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