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How Big Data Can Offer An Optimized Realistic Overviewin 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 thebeginning of the decade through a system with a high computing power. However, in order toobtain a higher realistic value of the research, using big data is need adequate tools to captionand organize a huge variety of data types from different sources. These instruments should beable to easily analyze huge volume of data in a contextual system. The purpose being to find newperspectives and the subtle relations between unstructured existing data. This type of analyze itwould 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 integratenon-traditional data with high potential value with the existing traditional data of the companyto achieve business intelligence analysis or data mining. Because of the quick decision makerthat this solution is bringing, our current paper aims to demonstrate if marketer research isimproved by using the solutions provided by a big data platform in analyzing information and ifit can provide a quick and realistic decisions based on the information that it has access to. Inorder to do this, were analyzed a series of literature specialty papers and the latest technologiesthat big IT companies are using, which helped in demonstrate that indeed big data systems arebringing a new way to approach information and to puzzle up series of trends in order to providea 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 Overviewin 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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