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Businesses Are Synthesizing Their Own Data — Here’s How

The cost and difficulty to acquire and use real sensor data for computer vision is an industry-wide issue. Luckily, synthetic data is an emerging solution to this problem.

Featuring: Chris Andrews, Chief Operating Officer and Head of Product at Rendered.AI

The inability to obtain data

In today’s computer vision-centric world, having accurate, applicable data has never been more important, but the cost of acquiring, labeling, and analyzing that data adds up fast-- especially for startups in the product development phase.

In some industries, such as agriculture, innovative AI applications are inhibited by the cost to obtain diverse datasets for existing species of plants. In other industries, such as aerospace, it’s impossible to obtain real sensor data to test the types of payload coming off of the satellite before it is launched into orbit.

This inability to obtain data was the major catalyst for Dr. Nathan Kundtz and his company,, a Platform as a Service (PaaS) that helps companies empower their data science teams.

As CEO of a satellite hardware company, Kundtz saw the gaps in data availability to train AI and realized that synthesizing that data could be a solution. His observations, among others, are why companies are moving away from traditional data and are increasingly exploring synthetic data to address their data issues.

Today, we’ll continue looking into synthetic data, the commercialization of synthetic data, and how platforms like can help implement AI in your business.

What is synthetic data?

Simply put, synthetic data is engineered data designed for a specific purpose while maintaining characteristics of real-world data.

Typically, synthetic data is derived from one of two sources:

  1. Real-world datasets

  2. Simulations solely focuses on synthetic data derived using physics-based simulation methods. They concentrate on computer vision (CV) data — typically imagery or video — generated by simulating 3D scenes, interactions and properties of objects in the scenes, and the sensors used to capture imagery, lidar, and other content.

Why use simulations for AI?

Simulations in AI are becoming increasingly important for testing, training, and validating machine learning models. Traditionally used for one-off engineering analysis, simulation technology is increasingly being used to generate data for AI training because it is cheaper than real sensor data collection and can be designed for purpose.

Simulations that model real-world systems are already used today to generate tons of synthetic data for training AI. In some cases, companies are even training AI for objects they’ve never actually seen or with simulated data for sensors that don’t exist yet!

Who uses synthetic data?

Any industry can benefit from using synthetic data, but the major players adopting it now are industries with large infrastructure needs.

For example, physics-based synthetic CV data is used in autonomous vehicles, cargo security, Earth observation, and factory management.

The auto industry uses synthetic data to train AI algorithms to recognize scenarios and objects that would be potentially both rare and unethical to reproduce to capture real data - i.e., scenarios around potential collisions.

The aerospace industry is sending thousands of new satellites into orbit and is starting to use synthetic data to build algorithms to extract knowledge from unstructured imagery content.

Other industries use synthetic data to look for rare objects, reduce bias, and improve customer experience.

Synthetic images from helped validate the potential to test human oocyte viability through deep learning

What’s wrong with real data?

Caption: In some industries, such as agriculture, innovative AI applications are inhibited by the cost to obtain diverse datasets for existing species of plants. Photo by ThisIsEngineering’s experienced ‘synthetic data engineers,’ technical experts who understand diverse concepts from 3D modeling to training AI, work with customers who encounter four common issues with real data, all of which can be resolved with synthetic data, of course.

  1. Difficulty capturing rare events and objects, leading to algorithmic bias.

  2. Error and expenses in human labeling of real data; in some cases, humans can’t interpret real sensor data such as radar, lidar, and infrared data.

  3. Inability to obtain data for future sensors or objects, inhibiting innovation.

  4. Privacy and security issues, make archived data impossible to obtain or use.

How to start using synthetic data

Many organizations today treat synthetic data generation as a one-off project or a commodity dataset. Even if your team has simulation and programming expertise and can apply that to building synthetic data channels or applications, most companies don’t have the infrastructure to house this synthetic data.

"We’ve seen over and over that obtaining project-based synthetic data in this way is not successful." - Chris Andrews, COO

  1. Generate it

  2. Generating synthetic data is no small undertaking.

It requires the skills and capabilities to build and run simulations, the computing power to generate thousands of outputs, and a well-rounded team of data scientists, computer vision engineers, and developers who understand how to use synthetic data.

But the benefits come to those who persist. Customers who build their synthetic data strategy on top of a platform, such as, can focus on the iterative trial-and-error workflow required to make synthetic data successful. They can then carry that know-how to the next project when their AI training and validation needs to evolve.

By using tools like, you’re subscribing to a source for your synthetic data and building direct relationships with the innovators in synthetic data as an industry. You’d be at the edge of AI innovation.

  1. Purchase it

You can purchase pre-generated, or static, synthetic datasets for either structured or unstructured data from providing companies. We do not generally find that customers are successful when they use pre-generated or static synthetic datasets, but if you’re not innovating in your space or just wanting to test out a control dataset, static datasets may be useful.

Synthetic data with provides a Platform-as-a-Service (PaaS) for creating synthetic data that provides:

Unlimited compute for data generation

  • Post-processing tools for domain adaptation and annotation creation

  • Open-source tools to build synthetic data channels & integrate synthetic data into AI pipelines

  • Predefined content based on customer use cases

  • Integrations to Esri, NVIDIA, AWS, and other 3rd party tools to expand the content available to customers

  • Workflows to compare real and synthetic data

  • A cloud-based, collaborative environment that allows developers and data scientists to work together to design data for specific problems

  • Expert assistance in setting up your first synthetic data channel

Get started in generating your very first synthetic dataset by using’s example channel. If you’d like more information before getting started, they have a multitude of resources like videos, blogs, support documentation, and an in-house team of synthetic data engineers eager to help get you set up today.


Within the next few years, synthetic data is going to account for 60% of all data used to train AI, with some market analysts estimating up to 95% by 2030.

Furthermore, synthetic data has the potential to become a required tool for AI training to help overcome algorithmic bias and explore criminal investigatory processes, and to ensure privacy in the medical and insurance domains.

Ask your data science team if you’re ready for synthetic data today. Consider questions like:

  • Have we or are we already considering synthetic data?

  • What gaps in coverage do we see in our existing data?

  • What would our ideal dataset contain?

  • Have we declined business opportunities due to inadequate data?

Once your company has its first synthetic data channel, you can then generate thousands of images to build on your initial investment, reducing your overall cost and effort to train and validate your AI systems.

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