AI is driven by data. But what if your data is safely locked away and you can’t get to it easily for your data science projects? What good is your data for AI then?
In these situations, trying to use your insight-rich, sensitive data for machine learning (ML) and predictive analytics may actually slow down the progress of your projects. The hassles and paperwork of freeing the data up so you can put it to work greatly increase model time to deployment.
And that’s if you can even use the data in the way you intended – or at all. If the disclosure risks are deemed too great, or your proposed strategy breaks privacy regulations like GDPR, your project could be dead in the water.
Thankfully, all is not lost. These limitations apply to AI using your original data, but not synthetic data AI. Using a technique of artificial data generation, you can produce a brand new dataset that closely replicates all the statistical insight of the original, but without putting anyone at risk. This means you don’t have to wait around for permission to get your ML projects off the ground. You can start on your AI journey much faster, accelerating your progress.
But it gets better. Synthetic data generation – which includes synthetic financial data and training data generation – doesn’t only offer an easy way out of your privacy headaches. It also addresses other shortcomings that can otherwise hold back AI projects, including data availability and scalability.
Since you control how your synthetic datasets are generated, you can ensure they’re labeled or annotated correctly and are compatible with one another. This potentially saves you a ton of time in the data preparation stage.
You can also focus exclusively on generating data you actually need for your project. For example, if you’re trying to model rare events to make more accurate predictions about what causes them, it follows that you probably won’t have enough data in your original dataset to train a model. That’s the thing about rare events: they’re rare. Which makes it tricky to gather data on them.
Trying to acquire enough of this data can be an arduous, expensive, time-consuming process. Instead, you can generate larger volumes of synthetic training data from the data that you do have. In other words, synthetic data generation lets you scale up your data resources to jump ahead and kickstart your AI project.
Another great perk is that you can effectively hold onto personal data for much longer than many privacy regulations allow. When you collect user data from your website and other online sources, for example, under GDPR you don’t just need to get express permission to use it for your intended purpose. You also need to periodically remove the data if you’re no longer using it for that purpose.
This can make it really difficult to get enough historical data to model long-term trends, while obliging you to acquire data all over again for use in your ML projects when you’re ready to embark on them.
Once you’ve created a synthetic dataset from this time-limited data, though, you can hold onto the artificial equivalent for as long as you like. This means that, as soon as you want to explore a new AI concept, you have the data you need at your fingertips. You don’t have to go looking for it all over again.
Finally, if you’re opting for this kind of “synthesis AI” approach, you don’t have to keep everything in-house. You’re free to collaborate across departments in your organization or even form lucrative partnerships with exciting external vendors, fintechs and other technology companies.
You don’t have to worry that sharing your datasets with them incurs privacy and security risks. You don’t have to wait for months to get sign-off to free up your data, purely so you can ask these potential partners to demo what they can do.
This means that you can steam ahead with compelling new projects faster. It also means, of course, that you can get the best brains in the business working on your ML projects – driving them forward towards success. Your horizons get broader and your pathway to AI accelerates. That’s the true potential of state-of-the-art synthetic data for AI.