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Cleaning up your Dark Data is necessary.

Companies of every shape or size recently have moved to Big Data as they realized how beneficial it is for their organizations. To efficiently use the big data, companies need to collect and process it first, from multiple sources. Some data remains unused in the case of unstructured data mostly, which poses a problem later leading to clogging up data centres. Such unused data has been termed as – Dark Data which needs to be cleaned up or else it increases security risk and costs for the companies. Dark Data firstly needs to be identified. It is the information collected but not put to any specific use. Such data remains unused for years and takes up a lot of space which could have been used otherwise to store useful data. Dark Data remains unprotected for a while and poses risk of security breaches and mishaps. Dark Data is costly and dangerous but still companies keep them to make sure they don't lose any kind of records. Cleaning of Dark Data is time consuming but is important as well. One of the most effective ways of avoiding such data is by filtering the data which prevents data from piling up. Other ways to clean such data is by moving the Dark Data to different repositories using cloud computing. Dark Data can be deadly for organizations and hence it should be avoided before it poses any problem for the business.


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