10 use-cases for privacy-preserving synthetic data
Fast-evolving data protection laws are constantly reshaping the data landscape. The organizational ability to overcome sensitive data usage restrictions while safeguarding customer privacy will be a key driver of tomorrow’s successful businesses. This blog presents ten concrete applications for privacy-preserving synthetic data that could help businesses maintain a competitive advantage:
With the appropriate privacy guarantees, privacy-preserving synthetic data is a type of anonymized data. Thus, it falls out of the scope of personal data protection laws. This, in turn, reduces for organizations the restrictions associated with the use of sensitive data while safeguarding individuals’ privacy. It’s particularly valuable in heavily regulated industries, as we’ll see through the following use-cases.
More and more, data is becoming the central element driving value and growth within enterprises. In almost every data silo, and at every stage of the data lifecycle, enterprises have the ability to generate value. However, data hardly flows inside organizations, hindered by burdensome compliance and data governance processes. As a result, the use of synthetic data stretches along the data lifecycle. From data integration to data dissemination, it brings an alternative to leverage data.
As data move through the collection, integration, processing, and dissemination stages, enterprises can generate value. However, a large part of the potential value remains untapped because of strict privacy regulations.
Moving sensitive data to cloud infrastructures involve intricate compliance processes for enterprises. Assuring data safety, while guaranteeing its integrity for upcoming uses, can be time-intensive and costly, when possible at all. Because it embeds a privacy-by-design principle, Statice’s synthetic data allows enterprises to migrate samples, or complete data assets into cloud environments more easily. This saves time and money for enterprises that gain in data agility.
Privacy processes and internal controls slow down and sometimes prevent ideal data flows within organizations. Getting internal access to data can take weeks, or even longer when it is not clear which data points are required. The use of synthetic data samples, or complete datasets, liberates enterprises from the hurdles associated with getting sensitive data outside of a given silo. They can share internal sources and aggregate data faster, which in turn leads to a greater ability to leverage data.
The regulation of data retention has been a hot topic in Europe in the last decade. Today, the GDPR insists upon limiting how long and how much personal data businesses store. Additionally, national laws often regulate the retention for data of a certain nature, such as telecommunications or banking information. The problem is that certain analyses require the storage of data for a longer period, infringing on such regulations. For example, annual seasonality analyses would require at least two years of data. In such cases, synthetic data offers a way to comply with data retention laws while enabling otherwise impossible long-term analysis.
In test environments, lacking useful test data can slow down the development of new systems and prevent realistic testing. Here as well, synthetic data offers an alternative to production data.
