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August 31, 20204 min read

Dark Data 101: Everything You Need to Know

According to a study made by IBM in 2018, over 80% of all data is dark and unstructured and this will rise to 93% by 2020.

However, don’t let the name fool you: there’s nothing dark about dark data. In fact, it may be the light at the end of a tunnel for many businesses. Much like Big Data, Dark Data is a buzzword and you may hear a lot about it today.

To help you understand its importance, we’re going to provide you all the important details related to dark data, starting with a detailed explanation of the term.

dark data

What Is Dark Data?

Contrary to what the name suggests, there is nothing scary about dark data. Gartner’s IT Glossary defines the term as

“information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes.”

To put it simply, organizations collect a vast amount of unstructured data, which includes everything from raw survey data to previous employee profiles and customer information, and most of this data is never utilized.

Today, most companies have a significant amount of dark data stored in their repositories but only a few acknowledge this fact. One of them is Veritas ﹘ a multi-cloud data management company that admits that 52% of all stored data is dark. Many such cases would come to the fore if an audit was carried out on the data of all companies.

Find information in dark data

Where Is It Generated?

Unstructured, untapped data that is yet to be processed or analyzed, dark data is kept in data repositories. It is found within data archives and log files stored within data storage locations.

A tricky situation for any company is when every interaction, transaction and engagement gets captured. This is when companies need to prioritize — which data to utilize and which data to push aside for safe keeping. Often, this results in vast amounts of unstructured or semi-structured data being stored in log files or data archives in case it is required in the future.

data repository

Companies often generate much more data than they are equipped to interpret. For example, networking devices usually generate huge amounts of data and even if you dedicate time to collect that data, it remains dark unless you analyze it.

Often, data stays in the dark because of a company’s inability to process it efficiently. For instance, you won’t be able to turn your data into actionable information if it is stored in a format not supported by your analytics tools.

How to use Dark Data?

For many companies, dark data represents a sizable portion of all data stored. This makes it crucial to understand the use cases of dark data.

There is a lot to derive from dark data and some ways this data could be used are mentioned below:

Customer Support Logs

If you’re like most businesses, then you probably maintain records of customer-support interactions.

It is important not to keep data in these records such as when a customer contacted your business, which channel he/she used, how long the interaction lasted and so on in the dark for too long or using it only when a customer issues arises.

Instead, leverage the data to understand when your customers are likely to contact you, what their preferred methods of contact are.

Networking Machine Data

Large amounts of machine data related to network operations is generated by servers, firewalls, networking monitoring and other parts of your environment. By using this information to analyze network security and monitor the activity patterns of the data, you can avoid dark data in the network and ensure that the network structure is never under or over-utilized.

Is It Usable for Data Analysis?

Companies can analyze dark data to develop greater context and unveil trends, patterns and relationships that miss them during normal business intelligence and analytics activities. Analyzing valuable dark data could give your business insights you don’t currently have.


In the future, artificial intelligence (AI) tools will provide organizations with even more data than they currently have, making it crucial to have efficient data collection and analysis strategies in place. This includes dark data analysis, which may be the key to greater efficient, improved customer relationships, and higher profits.

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