Many consumer-facing industries rely on their ability to collect and process customer data. It has become a key element for corporate innovation and digital transformation. However 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 and use case for synthetic data that could help businesses maintain a competitive advantage:
In a previous post, we talked about the benefits that privacy-preserving synthetic data brought to enterprises, notably in terms of data agility and value creation. The underlying reason is that, 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.
Among these heavily regulated industries, we find the healthcare and medical industry, where data is historically highly sensitive. In Europe, the GDPR strictly regulates the processing of health data. In addition to the standard requirements for the processing of personal data, health data is often subject to an additional layer of protection. In the US for example, a specific set of regulations protects health data. The Health Insurance Portability and Accountability Act (HIPAA) and the Health Information Technology for Economic and Clinical Health Act (HITECH) both regulate the storing and processing of personally identifiable medical data.
User-centered industries such as the insurance industry are also subject to strong data protection laws. In a recent post, we described the challenges faced by Swiss insurance companies with the on-going revision of the Swiss Federal Act on Data Protection (FADP).
The financial and banking industries aren’t spared. All financial organizations in Europe are subject to the GDPR requirements since 2018. Its rollout wasn’t a paradigm shift for financial organizations, already used to privacy laws set by financial regulators such as MiFID II in Europe.
In the US too, strict regulations govern the use of financial data as well. Financial institutions must comply with federal laws such as the California Consumer Privacy Act (CCPA). Additionally, industry-specific standards apply to them, for example, the Payment Card Industry Data Security Standard (PCI DSS) or the Gramm-Leach-Bliley Act (GLBA).
Companies in these fields must be able to develop their data operations while respecting these data protections. Failure to do so drastically limits their ability to innovate and remain competitive. They must be able to maintain their ability to work with data safely and efficiently. In recent years, the interest in synthetic data in that intent rose a lot.
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.
Moving sensitive data to cloud infrastructures involves 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 to enterprises in search of greater 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 turn, this helps data-driven enterprises take better decisions.
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. Because it mimics the statistical property of production data, synthetic data can be used to test new products and services, validate models or test performances. This resource is easily and quickly accessible, allowing for greater data agility and faster time-to-production in software development.
On one side, using partially masked data can impact the quality of analysis and presents strong re-identification risks. On the other side, getting systematic consent for secondary use of data is a tedious process, especially considering today’s volumes of data and the prevailing consumer sentiment toward data processing. Privacy-preserving synthetic data helps balance this privacy and utility dilemma. Enterprises can run analysis on synthetic data generated in a privacy-preserving way from customer data without privacy or quality concerns.
With the same logic, finding significant volumes of compliant data to train machine learning models is a challenge in many industries. Using privacy-preserving synthetic data to power machine learning models can be a more scalable approach that also preserves data privacy. Multiple businesses already validated the use of privacy-preserving machine learning, producing meaningful results when building and training models with synthetic data. This an opportunity for enterprises to scale the use of machine learning and benefits from it in a secure way.
Data is an essential resource for product and service development. Once privacy-preserving synthetic data has been made available into an enterprise warehouse, engineers and data scientists can easily access and use it. Enterprises can create and make available data repositories that don’t represent a privacy breach, making resources available for product and service development. This in turn generates value for them as they are able to capitalize on their existing data to develop and innovate.
Packaging and selling data to third parties is now strongly regulated. Privacy-preserving synthetic data offers an opportunity to build revenue from data streams that are otherwise too sensitive to use for such purposes under normal circumstances. Organizations get to build new data-derived revenue streams at will, without risking individual privacy.
Exchanging data with third parties is part of what is driving enterprises’ innovation today. But whether to share analytics with clients, co-develop products with partners, or being able to send data to offshore sites, enterprises often struggle with the inherent challenges of sensitive data sharing. To avoid these time-consuming processes and increase their agility, enterprises can use privacy-preserving synthetic data.
For enterprises hosting hackathons or seeking to share data with external stakeholders, it is crucial to ensure that no personal information is exposed. The infamous Netflix prize case illustrates the risks of releasing poorly anonymized data. With privacy-preserving synthetic data, enterprises have a guarantee of safeguarding the privacy of individuals.
In today’s highly regulated environment, enterprises must find ways of unlocking the value of data if they want to remain competitive. Privacy-preserving synthetic data is a safe and compliant alternative to the use of sensitive data that can give enterprises a significant competitive advantage. From internal data sharing to data monetization, enterprises can generate additional value, which can be decisive in competitive markets.
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