Dark data refers to all of an organization’s underused, unidentified, and unexplored data generated in the course of a consumer’s everyday online communication with different devices. Dark data is anything from machine-generated data to unstructured data retrieved from social media. Although dark data is a subclass of big data, it accounts for the majority of the entire volume of big data acquired by businesses each year. Businesses very rarely process or handle dark data for a variety of reasons, but this does not diminish its significance in terms of business impact. There are two perspectives on the significance of dark data. Unanalyzed data, according to one viewpoint, includes hidden key insights and represents a missed opportunity. From the other side of the argument, unanalyzed data that isn’t handled properly can cause a lot of problems, including legal and security problems. This work also discusses the simple mathematical model of analyzing dark data and ways to convert unstructured data into structured data with examples.