Irrespective of the type or nature of the business, organizations of all sizes end up hoarding huge amounts of data.
The volume of this data is so enormous that it streams in from almost everywhere, including emails, business operations, employee/customer activity, social media, and other channels.
What is Dark Data?
Dark data is essentially the data that organizations collect and store but never put to any discernible use or leave unanalyzed. It is, in fact, one of the top issues behind Big Data and data analysis that organizations are continuously trying to solve.
What is the kind of data that is usually left unanalyzed?
There are various sources through which organizations collect this dark data, including IoT devices, website visitors, sales leads, or other similar points of data generation. But instead of being used to drive business decisions, this data simply remains on a server, taking a lot of space and other network resources.
Among some of the other categories of data that can qualify for the dark data include –
- Previous employee data
Raw survey inputs
Chat and call center transcripts
Why is Dark Data A Problem?
Data collection, with the aim of using it to better the business strategy, gain more insights into things such as customer behavior, productivity as well as customize the products to suit specific customer needs, has always been a topmost priority for a large number of organizations.
However, a report suggests that 55% of all the data that organizations collect, remains unused, or goes to waste. This is largely because most of the enterprises fall into the trap of just collecting data thinking that it will suffice to improve their business strategy without paying much thought to the lack of resources, time, or knowledge required to process, analyze and use such massive amounts of data.
Although it is hard for organizations to track and secure every piece of data they have, dark data – the data created by an organization unwittingly – poses a number of challenges such as figuring out ways on how to gain access to this data, use it, keep it secure and how to prevent online attackers from using it.
What Impact Does The Dark Data Have On Cybersecurity?
The most unpleasant consequence of this process is the lack of control over the data, which leads to risks. The confusion and ambiguity related to not knowing what each of your data sets contains and who can access that data can create huge cybersecurity threats leading to the possibility of wrong individuals accessing sensitive information and putting the business at risk of a data breach.
Cyber thieves, online hackers, and network infiltrators are lurking out there for any opportunity to access such data. The more data available freely, the more attractive it will be to wrongdoers.
Further, a lack of insight into dark, unstructured or unused data could lead to legal, financial, or compliance-related liability (by violating data privacy regulations and compliance mandates such as the HIPAA, PCI DSS, and GDPR) thus impacting the organizations’ bottom line. Poorly maintained data may also lead to various kinds of permission challenges.
Ways To Manage Your Dark Data
There are a number of reasons why companies are not utilizing dark data. It could be technological limitations, lack of investment, isolated data processing policies, or lack of priorities assigned.
However, it is well-established that dark data has a lot of potentials. The need for organizations is to assert control over the dark data with a robust plan, appropriate solutions like data governance with the aim of bringing positive results to compliance, economics, and overall productivity.
The very first step to reclaiming complete control of the dark data is to gain better visibility w.r.t. to storage management and advanced analytics of data to be able to extract the most useful information out of it.
That, in turn, makes it possible to easily classify, utilize, delete, or retain the dark data. Once the data visibility is achieved in terms of classification, analysis, retention, and deletion, it is time to gain control of the extracted information to –
- Improve product quality with customer feedback
Product management, engineering, and design teams can leverage customer feedback to improve the quality of the products. This category of dark data can help enterprises by offering a comprehensive view of the product and how it is perceived/viewed in the market.
- Demand prediction and issue solving
One of the excellent uses of the dark data lying in the form of customer or sales data is to accurately forecast demand and respond appropriately by optimizing the supply of the goods.
While there is no way to eliminate dark data entirely yet, there are various ways to reduce the amount of dark data held by your organization and monitor it to reduce or minimize the security or compliance risk. Among these include-
- Prune the data before dumping it
Take stock of all the data from time to time
Encrypt your data to endure security
Keep assessing how you backup your data
Prioritize and limit access to the data
Set guidelines for data retention and self-disposal
One of the effective tools to win the constant battle against dark data is using modern content management solutions as they allow you to set expiratory dates on all your documents and data points. This helps to ensure that you don’t end up amassing huge volumes of data that can put you at unnecessary cybersecurity risks but only have what you need.
In an era where cyberattacks are more & more ruthless and security threats are incredibly sophisticated, protecting and governing data-of any type or form-becomes critically important for organizations in mitigating security risks.
Until recently, dark data was a huge stream of data that was unanalyzed/undiscovered due to the acute lack of technological tools. But with changing times, the dark data has finally come to light, and along with it brought a transformational change in the way businesses operate. To be able to open a whole new avenue for the future, businesses need to constantly engage in discovery and illumination of their dark data sets.