What is synthetic data?
A working definition from MIT Sloan
synthetic data (noun)
Information created by an algorithm that can be used as a stand-in for real data.
Companies committed to data-driven decisions share common concerns about privacy, data integrity, and a lack of sufficient data.
Synthetic data is one promising solution. A synthetic data set has the same mathematical properties as the real-world data it’s standing in for, but doesn’t contain any of the same information.
Synthetic data is generated by taking a relational database, creating a machine learning model for it, and generating a second set of data. It can be used to test machine learning models or build and test software applications without compromising real, personal data.
Besides protecting privacy, synthetic data can remove speed bumps and bottlenecks that slow down data work, according to Kalyan Veeramachaneni, a principal research scientist with MIT’s Schwarzman College of Computing. He and his research team developed the Synthetic Data Vault, an open-source software tool for creating and using synthetic data sets. The researchers found “no significant difference” between predictive models generated on synthetic data and the real thing.
What is synthetic data — and how can it help you competitively?
Working Definitions: Data
MIT Sloan's Working Definitions explore the words and phrases behind emerging management ideas.
Strategy, Survival, and Success in the Age of Industrial AI
In person at MIT Sloan
Register now
Action items for AI decision makers in 2026
AI industry watchers Thomas Davenport and Randy Bean expect the AI hype cycle to slow as organizations focus on infrastructure and strategy.
5 ‘heavy lifts’ of deploying AI agents
New research provides insights for using AI agents in clinical settings.
Achieve big value with smaller AI efforts
Organizations see success by starting with smaller AI transformations. Aiming for incremental value builds a foundation for sustainable results.
AI hiring perpetuates familiar biases. Here’s how to avoid that trap
The AI hiring revolution doesn’t have to be a story of automated bias, argues MIT Sloan’s Emilio J. Castilla. Tough questions and constant monitoring can lead to fairer systems.
What is a data democracy, and how can your company build one?
Leaders who actively design for the widespread use of data assets generate three times the revenue from data monetization compared with their peers.
Large language models can help professionals identify customer needs
A study found that trained LLMs can identify what customers want as well as expert market reach analysts, who are freed up to apply their expertise to high-leverage tasks.
What’s ahead for platforms in 2026
Digital platforms have already changed how value is created and exchanged. Their next wave — spanning physical assets, AI, and automation — promises new efficiencies but also new risks.
Flexible data centers can reduce costs — if not emissions
Data centers that shift workload to different times of day save money, but the environmental impact depends on the local grid.
How to boost your organization’s AI maturity level
New research highlights four areas leaders must address as they embed AI across their business.
How to succeed with industrial AI
Applying systems dynamics principles to industrial AI can ensure faster and more impactful business outcomes.