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Big Data and Advance Analytics: Architecture, Techniques, Applications, and Challenges

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  • Surabhi Verma

    (National Institute of Industrial Engineering, Mumbai, India)

Abstract

The insights that firms gain from big data analytics (BDA) in real time is used to direct, automate and optimize the decision making to successfully achieve their organizational goals. Data management (DM) and advance analytics (AA) tools and techniques are some of the key contributors to making BDA possible. This paper aims to investigate the characteristics of BD, processes of data management, AA techniques, applications across sectors and issues that are related to their effective implementation and management within broader context of BDA. A range of recently published literature on the characteristics of BD, DM processes, AA techniques are reviewed to explore their current state, applications, issues and challenges learned from their practice. The finding discusses different characteristics of BD, a framework for BDA using data management processes and AA techniques. It also discusses the opportunities/applications and challenges managers dealing with these technologies face for gaining competitive advantages in businesses. The study findings are intended to assist academicians and managers in effectively quantifying the data available in an organization into BD by understanding its properties, understanding the emerging technologies, applications and issues behind BDA implementation.

Suggested Citation

  • Surabhi Verma, 2017. "Big Data and Advance Analytics: Architecture, Techniques, Applications, and Challenges," International Journal of Business Analytics (IJBAN), IGI Global, vol. 4(4), pages 21-47, October.
    • Handle: RePEc:igg:jban00:v:4:y:2017:i:4:p:21-47
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    Cited by:

    1. Zhuang Weiqing & Wang Morgan C. & Nakamoto Ichiro & Jiang Ming, 2021. "Big Data Analytics in E-commerce for the U.S. and China Through Literature Reviewing," Journal of Systems Science and Information, De Gruyter, vol. 9(1), pages 16-44, February.
    2. Roberto Del Gobbo, 2023. "I Big Data non "parlano da soli". Il ruolo dei modelli nella diffusione degli analytics per il management accounting," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2023(1), pages 5-20.
    3. Sinjini Mitra & Zvi Goldstein & Bhushan L. Kapoor, 2021. "Predictors of Choosing Business Analytics Concentration and Consequent Academic Performance," INFORMS Transactions on Education, INFORMS, vol. 21(3), pages 130-144, May.

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