Synthetic Data Is Better Than Anonymised Data

Data is an important aspect in modern technology. It is the fuel that drives businesses in this highly competitive digitalised era we are in. If you are keen on data science, artificial intelligence, machine learning,high chances are that you have come across the term synthetic data and anonymised data.

Synthetic data is data that is artificially generated by a system. It replicates real-world data without the inclusion of sensitive user information. On the other hand, anonymised data is real data that has personal identifiable information scrubbed off from it.

Of the two, data scientists have come to embrace the use of synthetic data. Unless of course, you are running a commercial application that touches on, for instance, consumer behaviour or statistics that requires 99.9% data accuracy.

In this article, we are going to look at the various ways the generation of synthetic data is better than anonymised data.

Let’s delve into specifics.

Synthetic Data Offers Privacy

As earlier stated, anonymised data makes use of real data minus a few personal identifiers. The problem with this is that, with the kind of technology at our disposal, people can quickly re-identify this information and trace it back to the original user.

With such possibilities, the risk of bypassing email and bank account password recovery systems increases. It can also lead to exposure of sensitive and embarrassing information that a user did not want to be disclosed to friends, family, or work colleagues. This allows blackmailers and other unscrupulous individuals to access a person’s information: information that was not intended for them in the first place.

However, with synthetic data, this is different.

Synthetic data is not fake data, as some would think.It is creating statistical data from scratch and a generalised point of view. The fact that synthetic data eliminates the use of personal information goes a long way in assuring the security of people’s data.

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It Is Cost-Effective

The data collection process is a hefty one. Data collection companies incur exorbitant coststo record, store, organise, and prepare data. This data also needs to be verified.

When it comes to generating synthetic data, that whole process is non-existent as it makes use of generalised data.

What’s more, unlike anonymised data, synthetic data is the only viable solution in instances where real data does not exist or is unavailable.

Synthetic Data Makes It Easy to Train Artificial Intelligence

Currently, the majority of data scientists are using synthetic data to ensure the success of their applications that rely on artificial intelligence and machine learning.

For instance, authoritative companies like Facebook have adopted the use of synthetic data to train algorithms on ways to detect bullying language on the platform. This is a measure that spearheads the safety of Facebook users by protecting them from online predators; something that cannot be said for anonymised data.

All in all, the use of synthetic data is not new in the technological world. It has been around for quite a while, but the sad thing is, many companies are yet to embrace it and reap its benefits.

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