It’s estimated that by 2025, there will be 175 trillion gigabytes of data in circulation — so much, in fact, that it would take 1.8 billion years to download via an average 25 Mb/s internet connection. That’s a frankly mind-boggling amount of raw information, and it presents a major challenge as we try to think about the future of data privacy. After all, how can you secure something so vast that you can barely even quantify it?
This isn’t just a rhetorical question: today’s businesses are already grappling with these issues. According to Gartner’s latest Hype Cycle for Privacy report, “Enterprises focused on digital business strategies are seizing tremendous opportunities for growth and data monetization by creating value from the increasing velocity, volume and variety of datasets. However, they face huge challenges to mitigate growth in associated business risks.”
In short, the explosion of data represents a big opportunity, but also a major risk factor for companies seeking to leverage new data tools and systems. On the one hand, data is too important not to collect and harness it as much as possible. But on the other, the risks associated with that data are growing as fast as the repositories themselves. Companies are dealing with an asset that’s as volatile as it is valuable.
The bad news is that if data isn’t properly classified and secured, it can be a serious liability. With the passage of the General Data Protection Regulations (GDPR) in Spring 2018, the EU has significantly escalated the war over data privacy, with noncompliant companies now facing maximum fines of 4% of annual revenues. California has passed similar legislation, and India and China are poised to follow suit. Increasingly, privacy will be a mandate, not just a goal — and sheer volume isn’t seen as an excuse for failing to take proper precautions.
Of course, trying to classify and keep tabs on vast amounts of data by hand is a daunting task. But there’s good news: manual data-classification and protection isn’t the only approach. Dathena is now using advanced AI and machine learning solutions to automate the process, allowing organizations to identify private data within vast data sets, and automatically categorize and segregate it for security and compliance purposes.
We leverage best-in-class natural language processing capabilities to identify keywords and contextual clues that reveal if data contains private or otherwise regulated details. Initially, our solution analyzes a small subset of data to understand the relationships between entities. That information then feeds into a knowledge graph that defines the logic underlying those relationships. Finally, that logic “trains” our solution to find all the private information located across all data sources — no matter how vast or how varied.
That means lower costs, less risk of compliance breaches — and the ability to scale up data privacy efforts fast enough to keep pace with the ever-increasing volumes of data we’re now generating and using. Dathena’s solutions are scalable by design, because we understand that privacy today is worthless unless you can also be confident of ensuring privacy tomorrow. We also know that the legal landscape isn’t getting any easier to navigate, so companies urgently need tools that can cope not just with vast amounts of data, but with rapidly changing global regulatory frameworks.
So if you want to see the challenges that lie in store, check out Gartner’s Hype Cycle for Privacy report. After you’re done reading, start asking the hard questions about your own ability to keep data private — and how prepared you are for a world of continuing exponential data growth. Don’t be overwhelmed by tomorrow. Contact Dathena today.
Read more in Dathena’s newsletter, featuring insights from Gartner: https://www.dathena.io/how-the-dpo-journey-drives-dathenas-data-privacy-framework.