A huge amount of data builds up and accumulated in a short span of time but it takes time to get the gist of data properly. At present, it has become a major challenge for organizations as data is continuously growing at a fast rate.
Big Data has the following three characteristics – Volume, Velocity and Variety according to most experts. It needs large storage space, is created quickly and exists in different formats.When it comes to interpretation, data really needs to be organized especially if it is unstructured. It takes a lot of time to organize data in relevant categories.
The following categories need attention while organizing data:
Verifying data’s usefulness:
It takes a lot of effort to analyse to check its origin, its freshness and workflow relevance for decisions regarding business goals.
Data Sets too Varied/Irrelevant:
Data sets are in different formats and not necessarily related to each other. It comes from different sources such as social media feeds, customers’ feedback in a database and unfiltered data from social media.
Management of Unstructured Data:
Since the origins of data sources are different, it becomes confusing when trying to fuse them together.
Data discovery should be flexible which includes fresh queries, for which an interpretation technology can be opted. It is a good practice to identify relevant data sets and keep it useful in reference to the immediate business goals. Visual analytics work very well as they use eye tracking for picking up results faster. To save time and get the query results fast, fusion should be done with relevance and suitability so that data is suitable to make future business decisions.
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