Risk management is crucial for any data-driven business. Former FBI Director Robert Mueller famously said, “There are only two types of companies: those that have been hacked and those that will be.” This statement struck a chord when first spoken in 2012, and the strings are still ringing.
As data continues to be more deeply intertwined in our day-to-day lives, the associated risks are growing in number and severity. So, there’s increasing scrutiny on organizations’ data governance practices – and for good reason.
Governmental scrutiny, in particular, is gearing up. The General Data Protection Regulation (GDPR) introduces strict formality in the way data is governed across the European Union, including organizations outside the EU that wish to do business with its member nations.
But in certain sectors, public scrutiny is just as – if not more – important to consider. We’ve been talking since September about the data breach at Equifax, which has just been hit with a 50-state, class-action lawsuit.
And we just learned that Uber was hacked, resulting in the personal data of 57 million customers and Uber drivers being stolen. What’s more, the company concealed the breach for more than a year.
Whether we’re talking about financial or reputational damage, it’s absolutely clear that bad data governance is bad business.
Think about the Internet of Things (IoT) for a moment …
IoT devices are gaining more stock in daily life – from the mundane of smart refrigerators and thermostats to the formidable of medical devices. Despite the degree of severity here, personal data is personal data, and the steps taken to mitigate security risks must be evidenced to be compliant.
Data governance is fundamental to risk mitigation and management. That’s because data governance is largely concerned with understanding two key things: where your data is kept and what it’s used for. Considering the scope of IoT data, this is no easy feat.
Estimates indicate that by 2020, 50 billion connected devices will be in circulation. Misunderstanding where and what this data is could leave the records of millions exposed.
On top of the already pressing need for effective data governance for risk management, we’re constantly approaching uncharted territories in data applications.
The driverless car industry is one such example on the not-too-distant horizon.
Businesses from BMW to Google are scrambling to win the driverless car race, but fears that driverless cars could be hacked are well founded. Earlier this year, a Deloitte Insights report considered the likely risks of introducing autonomous vehicles onto public roads.
It reads, “The very innovations that aim to enhance the way we move from place to place entail first-order cybersecurity challenges.” It also indicates that organizations need to make radical changes in how they view cybersecurity to ensure connected vehicles are secure, vigilant and resilient:
The first thing organizations should take away is that this advice applies to the handling of all sensitive data; it’s by no means exclusive to autonomous vehicles. And second, security, vigilance and resilience all are enabled by data governance.
As discussed, data governance is about knowing where your data is and what it’s used for. This understanding indicates where security resources should be spent to help mitigate data breaches.
Data governance also makes threat data, IT data and business data more readily discoverable, understandable and applicable, meaning any decisions you make regarding security investments are well informed.
In terms of resilience and the ability to rapidly respond, businesses must be agile and collaborative, points of contention in traditional data governance. However, Data Governance 2.0 as defined by Forrester addresses agility in terms of “just enough controls for managing risk, which enables broader and more insightful use of data required by the evolving needs of an expanding business ecosystem.”
As GDPR looms ever near, an understanding of data governance best practices will be indispensable. To get the best of them, click here.