Why enterprises planning to develop and utilize synthesized data must set proper boundaries to secure private user data.
The tangible world we were born into is steadily becoming more homogenized with the digital world we’ve created. Gone are the days when your most sensitive information, like your Social Security number or bank account details, were merely locked in a safe in your bedroom closet. Now, private data can become vulnerable if not properly cared for.
This is the issue we face today in the landscape populated by career hackers whose full-time jobs are picking into your data streams and stealing your identity, money or proprietary information.
Although digitization has helped us make great strides, it also presents new issues related to privacy and security, even for data that isn’t wholly “real.”
In fact, the advent of synthetic data to inform AI processes and streamline workflows has been a huge leap in many verticals. But synthetic data, much like real data, isn’t as generalized as you might think.What is synthetic data, and why is it useful?
Synthetic data is, as it sounds, made of information produced by patterns of real data. It’s a statistical prediction from real data that can be generated en masse. Its primary application is to inform AI technologies so they can perform their functions more efficiently.
Like any pattern, AI can discern real happenings and generate data based on historical data. The Fibonacci sequence is a classic mathematical pattern where each number in the sequence adds the prior two numbers in the sequence together to derive the next number. For example, if I give you the sequence “1,1,2,3,5,8” a trained algorithm could intuit the next numbers in the sequence based on parameters that I’ve set.
This is effectively a simplified and abstract example of synthetic data. If the parameter is that each following number must equal the sum of the previous two numbers, then the algorithm should render “13, 21, 34” and so on.
