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Furthermore, with synthetic data, a company can quickly train ML models on large datasets, which means faster speed to training, testing, and deploying an AI solution. This addresses a real challenge many companies face: a lack of enough data to train a model.

Scale is a by-product of security and speed. Secure and faster access to data make it possible to expand the amount of data you can analyze and, by extension, the types and numbers of problems you can solve. Every bank is obliged by itself and regulators to identify and stamp out fraud.

And each bank is on its own quest, working independently of others and committing significant resources to the cause, because regulators require it and only the bank itself is allowed to comb through its data to look for suspicious activity. If banks used synthetic data, they could share information about their investigations and analyses. By pooling their synthetic data sets with peers in the industry, they could get a holistic picture of all the people interacting with banks in a particular country, not just each bank, which would help streamline and speed up the detection process and, ultimately, eliminate more fraud using fewer resources.

The benefits of synthetic data are compelling and significant. But realizing them requires more than just plugging in an AI tool to analyze your data sets. Generating synthetic data properly requires people with truly advanced knowledge of AI and specialized skills, as well as very specific, sophisticated frameworks that enable a company to validate that it created what it set out to create. This is a critical point.

This means subsequent modeling efforts might overlook potentially big opportunities or, worse, generate inaccurate insights. If a company creates a synthetic data set that simply copies the original, the new data will have all the same biases.

Therefore, you have to make complex adjustments to the AI models so they can account for bias and create a fairer and more representative synthetic data set. Synthetic data can also be used to generate datasets that agree with a pre-agreed definition of fairness. Using this metric as a constraint to an optimizing model, the new dataset will not only accurately reflect the original one, it will also do so in a way that meets that specific definition of fairness.

As a result, this new fair dataset can be used to train a model, without the need for bias mitigation strategies like algorithmic fairness, which can lead to accuracy trade-offs. AI, for example, has demonstrated its effectiveness on the well-known COMPAS recidivism dataset that fueled racially discriminatory algorithmic outcomes.

Beyond ensuring the actual mechanics of creating synthetic data are sound, most companies also need to get past common cultural resistance to the concept. For companies looking to create and use synthetic data effectively to capitalize on those benefits, there are three fundamental considerations to keep in mind:.

Synthetic data is a new and complicated concept for most people, complete with a lot of misconceptions. Creating synthetic data is a very complex process. You need to determine if your existing data scientists and engineers are capable enough to learn how to do it.

If institutions, universities, governments, and companies open the doors to their data—but in a synthetic way—the potential for the future is thrilling.

This could lead to the development of a thriving synthetic data economy, in which parties create, buy, and sell data—or, in some cases, give it away for a good cause—without the worry that individuals or companies can in any way be compromised. The fact is, if you're able to create synthetic data out of your own data and there's value in sharing, people and organizations will do it—and the world will be better for it.

How quickly it moves from its current state to being applied practically in real-life settings remains to be seen. Harness the power of data and artificial intelligence to accelerate change for your business. Creating meaningful business change with data and AI. Take an interdisciplinary approach that supports agile innovation and ensures governance of your AI systems.

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