Dark Data: What it is & How Businesses Should Address it

What is Dark Data?

Per Gartner, dark data is the information that gets collected, processed and stored over the course of typical business activities, but isn’t leveraged. Dark data is a significant portion of the enormous and complex Big Data world.

Dark data is generated from the digital user interactions that occur everyday on innumerable systems and devices, including server log files, machine data, and unstructured social media data. An example of dark data is unstructured customer geolocation data and sentiment data that could potentially support business and marketing planning after it is analyzed for patterns in traffic. 

In some cases, organizations are unaware dark data exists, or they view it as too outdated, redundant, or insufficient to be valuable. In other cases, the dark data exists in a format that isn’t accessible with the organization’s tools.

Because a business collects and stores dark data over time, it might be personal, unstructured, regulated, or unguarded. Data that a company isn’t aware of means that it's an unprotected risk to security and compliance. For example, dark data is an attractive target for ransomware attacks and data breaches by fraudsters and cybercriminals who leverage this sensitive data for malicious intent. But only 33 percent of IT staff and executives are knowledgeable about dark data risks.

However, dark data can ultimately be used to explore new revenue source opportunities and minimize expenses. Organizations just need to be aware of it, understand what it holds, and how to find and leverage it.

How to Address Dark Data

Technologies like artificial intelligence (AI), and machine learning can help businesses locate, manage, safeguard, and gain overall visibility into their dark data. 

For example, AI-driven solutions that can be used to process dark data are IBM’s Datacap, Google’s Cloud Vision and AutoML, and Microsoft’s Azure Cognitive Services. This might involve deep knowledge of deep learning, natural language processing (NLP), MLOps, Python, Java, and Kubernetes.

AI and machine learning can also help organizations with data management to detect compliance and security risks stemming from their dark data assets and take steps to resolve any exposure. Data mapping can complement this approach by uncovering the locations and sources or stored data. Data minimization can also help as it can decrease the quantity of data that’s being stored and verify that any retained data is specifically suited for the reason it was collected.

Dark analytics, or the AI-powered analysis of an organization’s dark data, is another important emerging tool in managing dark data. Driven by skyrocketing data breaches, the mainstream growth of data-driven marketing, and the need to scale security across the organization, the dark analytics market is anticipated to reach over $1.776 billion by 2026. 

A recent Splunk report indicates that of the 1,300 IT and business decision makers, just 10 person to 15 percent use AI to address dark data. The main barrier cited was a lack of relevant stills and the sheer quantity of dark data. Skills in AI, machine learning, and analytics — already in high demand - will continue to be a huge boon to businesses in every industry as organizations take action on dark data.

Don’t Wait on the Dark Data Opportunity 

Organizations simply can’t let the opportunities or risks surrounding dark data slip through their figurative fingers. It’s time to create viable strategies to manage dark data challenges and realize its potential for key business objectives. 

Check out Simplilearn's  Professional Certificate Program in Data Science for more information about dark data and educational options in artificial intelligence, machine learning, and dark analytics.