Privacy Matters in a Data-Driven Economy

Lori MacVittie Miniature
Lori MacVittie
Published June 15, 2020

Organizations are investing in AI and ML today in significant numbers. According to research from O'Reilly, 85% of respondent organizations are evaluating AI or using it in production. More than half of those identify their AI practice as "mature." 

AI use cases are found in every aspect of business, from IT to security to finance to marketing. The ability to analyze large data sets and discover patterns and relationships in them uncovers insights that can lead to competitive advantage, new business models, and a better digital experience. This is the driving force behind our embarkation on the path to a multi-purpose application analytics platform. We believe there is a need to provide higher value insights than the top ten talkers on the network or biggest bandwidth consumers. Those insights require analysis of data in significant volumes.

The key here is data. Data comes from a variety of sources across the code-to-customer path. Applications. Platforms. Application services. Infrastructure. Devices. All are capable of emitting data that can be turned into business value by the right solution. 

The elephant in the room during any discussion on the use of data is, of course, privacy.

Governments around the globe are wrestling with that elephant right now, with respect to our ongoing response to COVID-19. The use of data through contact tracing to balance health and well-being of entire populations versus individual privacy often results in heated debate.

Global Head of AI at F5, Shuman Ghosemajumder, tackled this tricky topic in a commentary published on Dark Reading. In it, he asks and answers a question that is generally applicable to any solution that relies on data:

How can technology help in a way that doesn't fundamentally violate our expectations around privacy?

While Shuman focuses on a narrow use case—contact tracing—the broader question regarding privacy is one that every organization planning on adopting AI and data-driven solutions should be asking and, more importantly, answering.