The future of data analytics is constrained only by its privacy-preservation shortcomings.
Data analytics has come a long way. The ability to analyse vast amounts of information and steer smarter decisions is a hallmark of our times. From enhancing humanity’s reality grasp to enabling casually bespoke interactions, data analytics has made our worlds significantly more accurate and relevant. Alas, it has also come with a cost.
The unwavering pursuit of larger – and more valuable – data-centric intelligence has frontally collided with personal privacy. A growing ability to produce ever-deeper and more granular insights has also unearthed extremely sensitive risks to individual rights. Ultimately, pushing forward with analytics while protecting the people to whom data belongs have become seemingly antagonistic concepts.
Nevertheless, the march of data cannot stop. There is way too much impact still to be derived from it for businesses, governments and our day-to-day lives. The human species simply cannot forgo the benefits lying beyond the data surfaces we have just barely scratched.
In Niometrics, we believe that data exploration and privacy preserving capabilities must be fused together into one single, inseparable platform in which more powerful knowledge will materialise not in spite of, but because of more robust privacy principles.
“Progress in data analytics must come at no cost to privacy.”
Data analytics and privacy engineering are, thus, bound to become necessary sides of the same coin. Complementary forces that can only produce results when tightly coupled together. In the same vein that cars could only accommodate more powerful engines when new safety devices were created for them. Or that nuclear energy could only be harnessed when advances in other engineering fields enabled its power to be tapped under controlled conditions.
Progress in data analytics must come at no cost to privacy. And advances in privacy protection must not exact any toll on data analytics. Both must drive each other forward in mutually reinforcing fashion. Breakthroughs in data analytics must trigger matching advances in privacy engineering, while new discoveries in privacy engineering must pave the way for even more sophisticated data analyses to be made.
Only this carefully weaved balance can secure the right path to progress. This is what our societies should aim at: to reconcile their ambitions and fears without having to give up on any benefits and rights. Not an easy task, for sure. But one that we at Niometrics are definitely very excited with, especially within the network analytics domain.