[4] In an industrial context, dark data can include information gathered by sensors and telematics.
[1] Some organizations believe that dark data could be useful to them in the future, once they have acquired better analytic and business intelligence technology to process the information.
However, storing and securing the data usually entails greater expenses (or even risk) than the potential return profit.
[1] In academic discourse, the term dark data was essentially coined by Bryan P. Heidorn.
An obvious examples is whether a capitalized word is a name or not, and if so, whether it represents a person, place, organization, or even a work of art.
A lot of unused data is very valuable, and would be used if it could be; but is blocked because it is in formats that are difficult to process, categorise, identify, and analyse.
According to Computer Weekly, 60% of organisations believe that their own business intelligence reporting capability is "inadequate" and 65% say that they have "somewhat disorganised content management approaches".
All this data that is being collected can be used in the future to bring maximum productivity and an ability for organisations to meet consumers' demand.
Furthermore, many organisations do not realise the value of dark data right now, for example in healthcare and education organisations deal with large amounts of data that could create a significant "potential to service students and patients in the manner in which the consumer and financial services pursue their target population".