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How Big Data Can Offer An Optimized Realistic Overview In Marketer Research

Author

Listed:
  • Gheorghe Orzan
  • Andreea-Larisa Boboc
  • Ioana Burghelea
  • Luana Diana Stupu

Abstract

Nowadays, we can access big volume data a lot easier than it was possible at the beginning of the decade through a system with a high computing power. However, in order to obtain a higher realistic value of the research, using big data is need adequate tools to caption and organize a huge variety of data types from different sources. These instruments should be able to easily analyze huge volume of data in a contextual system. The purpose being to find new perspectives and the subtle relations between unstructured existing data. This type of analyze it would be almost impossible to achieve a while ago because of the tremendous work, time, resources needed and costs. More and more companies are interested to include and integrate non-traditional data with high potential value with the existing traditional data of the company to achieve business intelligence analysis or data mining. Because of the quick decision maker that this solution is bringing, our current paper aims to demonstrate if marketer research is improved by using the solutions provided by a big data platform in analyzing information and if it can provide a quick and realistic decisions based on the information that it has access to. In order to do this, were analyzed a series of literature specialty papers and the latest technologies that big IT companies are using, which helped in demonstrate that indeed big data systems are bringing a new way to approach information and to puzzle up series of trends in order to provide a quick and a better decision.

Suggested Citation

  • Gheorghe Orzan & Andreea-Larisa Boboc & Ioana Burghelea & Luana Diana Stupu, 2016. "How Big Data Can Offer An Optimized Realistic Overview In Marketer Research," Annales Universitatis Apulensis Series Oeconomica, Faculty of Sciences, "1 Decembrie 1918" University, Alba Iulia, vol. 2(18), pages 1-13.
  • Handle: RePEc:alu:journl:v:2:y:2016:i:18:p:13
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    Keywords

    Big Data; storage; marketer research; decision maker system;

    JEL classification:

    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics

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