Building a Data-Driven Future with Synthetic Data

Exploring the Benefits of Synthetic Data

Governance is one of the strongest cases for synthetic data. Since synthetic data contains the characteristics of the original data, businesses can still use data to drive innovation, such as by sharing data across teams, departments, and other partner organizations. However, this synthetic data doesn’t contain any critical personal or private information, as the synthetic data replaces the original data. 

Organizations can drive innovation and generate value much faster with synthetic data because the barriers and risks to compromising privacy and security are eliminated. Without roadblocks to privacy and security, decision makers can use synthetic data much more easily. 

For example, financial institutions’ data is highly protected, and maintaining customer privacy is essential to the success of these organizations. But with synthetic data, organizations can simulate data sets nearly identical to the original but remove critical private and confidential information from that data set. This helps organizations explore more advanced uses, like fraud detection. 

You can more easily scale when you can access data securely and quickly. Think about organizations that can monetize their data and how many industries and businesses can benefit from sharing and accessing synthetic data. The combination of governance, scalability, and speed makes synthetic data desirable and valuable. That’s why organizations in finance or healthcare can benefit from synthetic data; the data they manufacture contains similar characteristics to the original data without compromising customer and patient confidentiality. 

Creating Synthetic Data

Synthetic data is already being used for various purposes in the same way you would approach building machine learning (ML) models using real-world data sets. 

Sometimes there isn’t any available real-world data or obtaining the data sets companies require is expensive, so organizations can create synthetic data to fill in the gaps of the data they need to train ML models. For example, synthetic data has become popularized in the development of autonomous vehicles to simulate a variety of different driving scenarios. 

Synthetic data can be advantageous because it quickly speeds up model development while collecting real-world training data can be time consuming. Many different simulations can be implemented using synthetic data:

• Such as replacing or augmenting data to enhance predictions when customer behaviors change dramatically
• To test alternative outcomes so organizations can be better prepared for different events and situations 
• To improve software testing and DevOps environments without the security risks of using real-world data 
• To test AI systems for potential bias 

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Synthetic Data for a Better Future

Creating high-quality AI models in the future will not be possible without using synthetic data. Many large enterprises are already exploring the value of synthetic data, and many new startups are entering the space — strictly focused on the synthetic data space.

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