How your business can use privacy-preserving data for growth

It’s no secret that today’s modern businesses use and depend upon a lot of technology. These days, technology makes it possible to better communicate with customers and suppliers, devise strategies and learn more about target audiences.

One of the bugbears of today’s digital world is that it can be challenging to get access to data without falling afoul of privacy laws. If your business needs data, and it hasn’t got enough of it (or none at all), how can your organisation grow?

Firstly, you can’t steal data because that’s illegal. Secondly, some people might object if you ask to use some personal information for your projects. What if there was a way to generate the data you need that doesn’t infringe on a person’s privacy?

One neat way to achieve that goal is by using synthetic data. In a nutshell, synthetic data refers to information generated programmatically or artificially. It mimics real user data and helps businesses overcome data unavailability whilst maintaining legal compliance.

There are no security issues to consider. Plus, it’s considerably more cost-effective to work with synthetic data because you don’t have to pay for existing information.

Synthetic data has scores of practical uses for businesses looking to grow their brands. Take a look at some of the ways your organisation could use such privacy-preserving data to its advantage:

Populating a social platform with profile data

If your business is creating a social network, such as a social media platform or even a dating website, you will undoubtedly want to test the user experience of it. As you can appreciate, you can’t do any testing without having some profile data.

You want to make sure that your social platform is ready to use before launching it to your target audience. But, you can’t do that without having some data populated on the platform. Thankfully, synthetic data can help your business achieve that goal.

It’s possible to use synthetic data to populate various profile fields and even use AI-generated text to create sample posts or other content. Even testing image galleries is no problem, as you can synthetically create photos of people that mimic real humans.

You may want to invite actual humans to beta-test your firm’s new social platform, of course, but you can firstly use synthetic data to check that everything works as intended before getting to that stage.

Testing cloud migration

Another scenario where synthetic data can benefit your business is when you need to migrate an in-house database solution to the cloud. For example, you might have an in-house CRM system with plenty of customer data.

However, you don’t want to migrate anything until you’re absolutely sure your new cloud database solution is secure and works as intended. After all: the consequences of creating an open “back door” to your customer data can potentially be catastrophic.

That’s why it makes sense to work with sample information generated with a synthetic data process. You can use it to populate your new cloud solution with a rich dataset comprising thousands or even millions of records.

Once you’ve done that, you can check all the data is secure and that all operational and reporting functions in your new system work correctly. Once you’re satisfied your new cloud solution cannot get compromised, you may then migrate real data to the cloud.

Data retention

Let’s assume that part of your business processes involves reporting on and analysing customer data going back several years or more. As you know, GDPR and other local regulations insist that organisations limit the data they store on people.

Data retention can causes businesses with such requirements many compliance headaches, especially when they work with big datasets. Privacy-preserving synthetic data can help plug the gap between actual data and no data in several ways.

Firstly, it can populate any tabular data with artificially generated information like names, geographical information, and other data that falls under GDPR rules. Secondly, it enables businesses like yours to continue with long-term data analysis without using sensitive data.

Thirdly, it costs less than buying existing data and means companies can gain a true insight into their operations without using assumed data purchased from third parties, whether it contains personally identifiable information or not.

AI and ML model training

If your business is developing AI (artificial intelligence) or ML (machine learning) systems, it will undoubtedly need a lot of data to assist with model training. It’s a challenge for many industries, considering the privacy laws surrounding personal data and how it gets used.

Synthetic privacy-preserving data is a practical way to train AI and ML systems and produce meaningful results. It’s also fully scalable so that you can work with small or large synthetic datasets to build and train models using realistic data.

Industries such as insurance, technology and finance are just a few examples that use synthetic data for AI and ML model training.

Ethical hacking

Let’s say that you’ve created a cloud-based, database-driven service. You have undoubtedly invested a lot of time and money into the project, and you feel that it offers potential users a secure platform to the best of your knowledge.

But, how can you really tell that your platform is hacker-proof? The sad truth is that many companies end up with compromised databases thanks to the continued efforts of unscrupulous cybercriminals.

One way to test the robustness of your new cloud-based platform is through ethical hacking. In a nutshell, ethical hacking involves deliberately trying to find security holes in a system. It offers organisations and system users alike peace of mind.

You wouldn’t want to expose any real data to potential hackers, so it makes sense to populate a database with synthetic data. That way, it doesn’t matter if the data becomes compromised during the exercise.

Final thoughts

Privacy-preserving synthetic data undoubtedly has many practical uses and applications in the business world. It’s already used by some of the world’s leading brands, such as Amazon and Google, and it’s a concept you should seriously consider for your business.