What is dark data and why is it an economic challenge for businesses?

Understanding dark data: a hidden resource

According to Gartner, a multinational corporation that provides strategic consulting, market research and analysis in the field of information technology, dark data represents all those information resources that companies collect, process and store during normal business activities, but which are not exploited for further purposes, such as economic analysis, business reports or direct monetization. This phenomenon is a hidden cost for many companies, as unused data represents both a financial burden and a missed opportunity. Today, most companies store huge volumes of dark data: a survey of 1,300 business and IT decision makers conducted by Splunk revealed that for 60 percent of respondents, half of the data they collect is considered “dark,” and for a third this percentage reaches 75 percent or more.

Why data goes “dark” and its impact on business costs

The accumulation of dark data is often incentivized by the low cost of storage, which makes it easy for companies to archive any information they collect in the hope that it may prove valuable in the future. However, dark data leads to direct and indirect costs. Direct costs include storage costs, which, even at low cost, represent a significant infrastructure expense considering the ever-increasing size of corporate archives. Indirect costs include legal liability risks, as many privacy regulations require companies to secure even unused data, and opportunity costs: much dark data could contain valuable information to improve marketing strategies, investment decisions, or competitive analysis.

Dark data and economic inefficiency

Excessive management of data, including unused data, can significantly slow down business processes, reducing operational efficiency. Employees often have to spend a lot of time finding relevant information, and this time spent results in increased labor costs and reduced overall productivity. In addition, dark data can increase business risks, contributing to ineffective cybersecurity management and possible compliance violations, resulting in economic and reputational damage.

Despite the risks and costs, proper management of dark data can transform it from a liability to an economic asset.  Eliminating silos between departments allows data to be shared so that it can be leveraged to its fullest extent, while a clear data governance policy helps decide which data to keep and which to delete. Companies can also make use of artificial intelligence (AI) and machine learning (ML) to analyze dark data, uncovering any hidden economic insights and enabling more comprehensive data-driven decisions.

Dark data poses a significant challenge to business economics. If managed properly, it can become a strategic resource that can improve a company's competitiveness, but if left unused, it risks being an economic drag that slows growth. For modern companies, a proactive approach to dark data management is essential to maximize the economic value of the data at hand and minimize costs.