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Why Is Big Data So Interesting to Businesses? What Challenges Does Big Data Present?

Autor:   •  May 2, 2016  •  Coursework  •  328 Words (2 Pages)  •  943 Views

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Why is big data so interesting to businesses? What challenges does big data present?

Business has always wanted to obtain an in-depth understanding from information collected to formulate fact based decisions that are better, smarter and in real time. The demand of this insight of knowledge that has propelled the growth of big data tools and platforms.While the ability to seize and bank colossal amounts of data has expanded at an exponential rate, the technical capacity to aggregate and analyze these disparate volumes of information is only just now catching up.

Big data is proposing companies a looking glass into the information gathered. Big data will completely turn around the way businesses operate and stay competitive in the industry. Companies that provide capital for and successfully attain value from their data will have a significant advantage over their competitors. This performance gap will continually increase as more pertinent data is generated alongside technologies that enable quicker, simpler data analysis further evolves.

With the promises that big data offers, the reality of it in current situation are several characteristics that make them technically challenging. Below are a few examples of these characteristics that businesses today have to overcome :

  1. Volume
  • Currently, the architectures and infrastructures of an organization can’t cope with the         terabytes and petabytes of data pouring in daily.        

  1. Velocity
  • Companies not only have to acquire and analyze the relevant data necessary, they react to the flood of information in an allocated time by the application.
  1. Misinterpretation
  • Organizations need to fully comprehend the data in hand in order to hold it into some sort of context. Without doing so, the decision made will be based on false data.
  1. Quality and Relevance
  • Even if you can gather and interpret data quickly and put it in the proper context for the end user, determining the quality of data sets and relevance in a timely manner.



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