Thanks to the ubiquity of the smart device and the evolution towards digital-first purchasing and engagement, there has been a rapid increase in the amount of consumer data generated in recent years. This has understandably led to questions about user privacy and how that data is used by organizations.
Organizations like Google have tried to solve this by aggregating potentially sensitive data for the purpose of ads, though many questions have been raised about the robustness of this approach and its protection of identifiable data.
To coincide with this year’s Martech Summit, in this blog we will explore the ways in which other industries could follow a smarter route, one that allows them to seize the opportunity that lies between federated learning and consumer data.
Navigating public and owned data for privacy and profit
Federated learning is an important technology that allows businesses to develop models based on federated infrastructure, therefore allowing data to be distributed and control to remain with the custodian. As such, it is an essential tool in realizing the opportunities presented in the use of available and third-party data.
Marketers are no strangers to innovative and industry-leading technologies that aid their marketing efforts and help them plan and execute their digital campaigns. Unfortunately for them, however, new and intricate privacy restrictions and regulations protecting consumer data have meant that traditional ad targeting, tracking, and measurement has become far less effective than it once was.
Martech and adtech providers can utilize federated learning to effectively utilize consumer data, including data that spans geographical borders, so that brands have the analytics, for example, they need without potentially breaching data privacy regulation.
In this instance, federated learning is an effective alternative to centralizing data in the name of machine learning. It allows martech and adtech organizations to adhere to new privacy regulations as they connect data in a way that favors privacy by keeping data in its original location and, when combined with additional privacy controls, sees results aggregated without the need to expose any individual’s data.
The result? Consumer data remains private, while marketers continue to benefit from understanding specific characteristics that allow them to segment their audiences and tailor their campaigns accordingly.
Pharmaceutical companies have a similar challenge, whereby they require massive amounts of data to inform and help them develop life-saving treatments and drugs. Privacy regulations, such as GDPR or HIPAA, prevent healthcare organizations from sharing patient data sets to achieve this, but federated learning can help them find success.
Here, rather than share their data with pharma companies, healthcare groups can provide computational access to their data. Combined with necessary privacy and security controls set by the data custodian, the pharma company can then access the healthcare data without exposing any individual's data that risks breaching regulation.
Of course, the combination of owned and public data to drive business success spans beyond the marketing and healthcare sectors. By utilizing augmented distributed data sets through the combination of existing, third-party, and publicly available data, along with additional AI training examples or annotations built to improve model training time and accuracy, all data-driven modern organizations – from finance to manufacturing to automotive and beyond – can benefit.
The result is a series of federated, complementary data sets, each of which remains within the custodian organization’s environment yet is accessible to a determined network of partners. In execution, this allows businesses to understand, attract and serve their customer base without risking the security or privacy of their data.
What is federated ML and analytics and why does it matter?