The business world runs on data. Organizations collect and store a large quantity of data on a daily basis, some of which is extracted and consumed in various ongoing processes and strategies. Given its significance, it comes as no surprise that data plays a pivotal role in the overall growth and progress of modern businesses. A constantly growing database is more than just a mere asset. However, the problem lies when the collected but unused data is left unmanaged. One can even say that the raw data, which is stored in heaps, never see the light of the day, as it remains untouched.

Let us find out more.

What is Dark Data?

The precise meaning of dark data can be difficult to pin down. The term was originally coined by Gartner, and they define the term as, “the information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes.”

In layman’s terms, dark data can be called the data collected and left in archives and storage, which is yet to be analyzed for any use in business decision making. A large amount of dark data collected is now stored in locations where the analyzing process is difficult and expensive. The research firm IDC states that 90 percent of big data is actually dark data.

Also read: Big Data: Is It More Or Less Important Than Small Data?

Data is useful, as we continue to collect it for a specific purpose. But the majority of it that we hold on to is additional data that we think we may require in the future, but it is almost obsolete for any other cause. Dark data is a fairly broad categorization which can include a variety of information like customer data, raw survey data, old financial statements, old documents, and more.

Problems With Dark Data

Now that you know what dark data is, it is easy to imagine the amount of storage it needs. As it continues to grow, the obvious are space issues. More storage means more hardware, maintenance, and security costs. Dark data may be outdated but, it can also contain sensitive information which hackers might use. With new news of data breaches every week, leaving old documents vulnerable can mean bringing out old skeletons that might be hiding in the closet.

The unstructured data also contains a lot of valuable information, and letting it build up over time means many lost opportunities. With hoarding up of this data, it gets immensely complicated and time taking to find anything of worth in that giant heap.

How to Manage Dark Data?

While we can never be completely free of dark data, it can certainly be managed well. Every bit of information can be of use, but not in its raw form. A constant managing system must be in place to regularly organize this data and keep the related risks and costs to a minimum.

Database audit. This is a necessary job that should be done periodically. Audits for the whole database on a regular basis will categorize the old data and reduce the hoarding of new dark data. Also, the process should also include doing away with unneeded data. This means a more manageable database where one can find and use valuable information whenever they want.

Backing up your data. Perhaps this is the most essential activity that must be done. But the issue arises with how you take that backup. Some companies take full backup each time, which creates unnecessary data heaps. Use modern backup solutions – taking a single snapshot of the original and making differential backups over the same. This will significantly decrease storage costs.

Also read: The Role of Security in Artificial Intelligence

Data encryption. Security of all your data will always remain a top priority. Keeping that in mind, all your assets and sensitive information must be encrypted. It is crucial to saving your data from falling prey into the hands of people who would misuse that data to publicly demean your brand name. Any data, whether on an in-house server or cloud, must have strong encryption.

Getting into Structure

In theory, we know that the management of unstructured data requires the following basic tasks.

– Recognizing a problem or a query that needs an answer from the dark data.
– Reviewing the stored dark data.
– Identifying and categorizing what each document is about.
– Extracting information into a format fit for analysis.

These steps seem pretty simple to follow, which is true on a small scale. But for heaps of unstructured data, reviewing each document for relevancy and getting it into a proper format is a monumental task. Also, there can be many files which are about the same thing but in different formats. This can be very confusing if done manually.

Assistance from Artificial Intelligence

With the help of AI, a lot of data structuring can be done, without worrying about the scale of your business. Machines can be programmed to understand and pull out relevant information from piles of documents. Natural Language Processing (NLP) and other branches of AI act as an enabler to aid the structuring process and fast.

Thus, to save ourselves from the overgrowing unstructured database, we must have a management system set in place. Or else, most of the storage units will be full of dark data and not analyzed data.

What are your views on dark data? What other types of dark data have you come across in your business? Share them in the comments below!

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