Pros and Cons of Big Data
Big data, in essence, consists of large and complex sets of information that are so large and complex that traditional data processing software cannot handle them.
This information comes from a variety of sources and can take many forms. It may be structured, in which case the information can be easily presented in the form of a table (rows and columns), similar to a traditional relational database.
However, the majority of big data is unstructured or semi-structured information that cannot be easily represented or managed using traditional methods.
This less structured end of the big data spectrum contains information such as web data, social media content, audio, video, and images.
Big data has traditionally been defined by three core characteristics, namely:
Volume: Big data sets typically contain large amounts of low-density, unstructured data that can range from tens of terabytes to hundreds of petabytes or more, depending on the organization.
Variety: This information is derived from a variety of sources and can take many forms and formats.
Velocity: Big data arrives at a rapid pace, and many applications require it to be processed and analyzed at the same rate — often in real-time.
With the increased use of big data in business, health, and infrastructure applications, two additional characteristics have emerged:
Veracity: A measure of the information’s validity and dependability.
Value: An assessment of the value of data possession and analysis to an organization.
The primary goal of big data storage, processing, and analysis is to examine massive data sets in order to identify trends, patterns, and insights that can be used for a variety of purposes.
The pros and cons of big data stem primarily from the ease or difficulty with which it is possible to do so, as well as the value or deficits that these processes ultimately produce.
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