Data has established itself as one of the most important elements in both business and science. With the advent of big data services, companies can get access to previously unattainable advanced analytics, and researchers can find almost any imaginable dataset to draw insights from. However, all this potential is hindered by ever-pressing data privacy regulations. This is why the new concept of synthetic data is now gaining more traction.
Synthetic data is exactly what it sounds like. It’s data that has the same mathematical and statistical properties as authentic data but doesn’t put user privacy at stake. In other words, such data can be used to accurately train machine learning models and make statistics-based conclusions without revealing personally identifiable information. Interestingly enough, synthetic data is also generated by an AI algorithm, which has been trained on a real data set.
Advantages of Synthetic Data
The most important benefit of synthetic data is that it doesn’t expose sensitive data of companies and individuals in any way. This is why highly privacy-concerned industries like finance and health care can reap the most benefits in this regard. For example, let’s imagine a health care institution that wants to build an automated diagnostic system. To train the underlying algorithm, data scientists would most certainly need access to highly sensitive medical data. Synthetic data helps to overcome this barrier entirely. Similarly, financial institutions can use it to train their fraud detection systems.
Also, synthetic data eliminates the bureaucratic burden associated with gaining access to sensitive data. Even for internal use, companies often need months to justify the need for access to a specific dataset. With synthetic data, companies can gain insights much quicker.
Given that the privacy aspect is removed, the training of machine learning models becomes much more effective and less cumbersome. Far too often, companies can’t get their hands on the exact amount of data needed for thorough model training. Large synthetic data sets allow engineers to ensure much higher model accuracy and accelerate the model training overall. Moreover, buying synthetic data from third parties is much more affordable than buying real data, which lowers the entry barrier for data analytics and AI projects.
What’s the Catch?
While synthetic data provides many benefits, using it correctly is rather complex. It’s especially difficult to ensure that it is as reliable to use as real-world data. Currently, when it comes to complicated data sets with a large number of different variables, it’s quite possible to make a synthetic data set that doesn’t properly represent real-world conditions. This can lead to false insight generation and, consequentially, to erroneous decision-making.
Moreover, synthetic data doesn’t eliminate bias, one of the biggest problems with using data in general. Given that it is posed to reflect the qualities of real-world data, bias can easily creep in. While synthetic data is supposed to alleviate the burden of collecting and organizing data, it’s still crucial for companies to weed out all possible bias, which is an extremely hard task. This, in a way, defeats its promise to make data analytics more accessible. In any shape or form, working with data requires specialized knowledge and skills.
Lastly, it can still be possible to link synthetic data to real people, especially if replication hasn’t been properly done. This can be a lavish opportunity for wrongdoers, as synthetic data sets will most likely relate to highly sensitive personal information. Currently, it’s rather unlikely for such a scenario to unfold, but in a future where synthetic data is the norm, it can become a real concern.
Organizations are now trying to establish standardized frameworks that will allow them to accurately assess the reliability of synthetic data sets. As of now, it’s not surprising that companies are rather hesitant to use synthetic data, as it still hasn’t been proven reliable and there is little to no room for mistake. To add to this, before we will see the use of synthetic data on a larger scale, it will take a lot of work to persuade C-suites of the effectiveness of this solution.