Dark data: concept, value and challenges

Abstract

Dark data is a term used to refer to data that has not been analyzed or processed. This data is most likely to remain unused and eventually lose its value. After the advanced development in the field of big data, dark data has attracted attention as being a rich resource of data that can come in handy one way or another. Therefore, dark data is the focus of this study, in which the concept of Dark data has been explained by answering several pertaining questions such as: what is Dark data? What is the importance of Dark data? What are its properties? How can Dark data be processed? And what are the challenges that face organizations in analyzing Dark data? A descriptive analytical approach has been adopted in the study to investigate the scientific problems regarding this topic. The study concluded the following: the existence of Dark data is not noticed due to its being unindexed and unorganized. Storing dark data is not the only challenge, but the real challenge is determining its value. The study came up with several recommendations including:    a. setting up a preventive control on the flow of the data into any organization and processing the data professionally. b. Stressing the importance of upgrading storing policies for the data not accumulate in a manner that renders it valueless. c. Organizations should regularly filter their data, extract its value, and get rid of old ROT data.