What is dark data?

Dark data refers to data that has been collected and stored, but not organized in a usable format for further data analysis. As a result, you cannot use it for decision-making.

It is called dark because it is unexplored.

According to Gartner, dark data is “the information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes.”

What are some examples of dark data?

Customer call records are an example of dark data. These types of records are regularly recorded and stored, but rarely organized or analyzed.

Other examples of dark data include social network feeds, financial statements, raw survey data, emails, log files, geo-location data and notes, among others.

Why is dark data important?

Dark data is complex and difficult to categorize. Often, it requires large amounts of resources to be processed and analyzed. However, it offers significant value to organizations for extracting critical insights that can drive tremendous business value.

For example, customer call records can indicate customer sentiment. Web server logs can reveal visitor behavior. Organizations can use these insights to improve their products or services.

Think we're missing something? 🧐 Help us update this article by sending us your suggestions here. 🙏

See also

Articles you might be interested in

  1. What is the importance of dark data in the big data world?
  2. Leveraging dark data