IDEAS home Printed from https://ideas.repec.org/a/igg/jban00/v3y2016i2p1-32.html
   My bibliography  Save this article

Defining, Understanding, and Addressing Big Data

Author

Listed:
  • Trevor J. Bihl

    (Department of Operational Sciences, Air Force Institute of Technology, Wright Patterson AFB, OH, USA)

  • William A. Young II

    (Department of Management, Ohio University, Athens, OH, USA)

  • Gary R. Weckman

    (Department of Industrial and Systems Engineering, Ohio University, Athens, OH, USA)

Abstract

“Big Data” is an emerging term used with business, engineering, and other domains. Although Big Data is a popular term used today, it is not a new concept. However, the means in which data can be collected is more readily available than ever, which makes Big Data more relevant than ever because it can be used to improve decisions and insights within the domains it is used. The term Big Data can be loosely defined as data that is too large for traditional analysis methods and techniques. In this article, varieties of prominent but loose definitions for Big Data are shared. In addition, a comprehensive overview of issues related to Big Data is summarized. For example, this paper examines the forms, locations, methods of analyzing and exploiting Big Data, and current research on Big Data. Big Data also concerns a myriad of tangential issues, from privacy to analysis methods that will also be overviewed. Best practices will further be considered. Additionally, the epistemology of Big Data and its history will be examined, as well as technical and societal problems existing with Big Data.

Suggested Citation

  • Trevor J. Bihl & William A. Young II & Gary R. Weckman, 2016. "Defining, Understanding, and Addressing Big Data," International Journal of Business Analytics (IJBAN), IGI Global, vol. 3(2), pages 1-32, April.
    • Handle: RePEc:igg:jban00:v:3:y:2016:i:2:p:1-32
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJBAN.2016040101
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wilkin, Carla & Ferreira, Aldónio & Rotaru, Kristian & Gaerlan, Luigi Red, 2020. "Big data prioritization in SCM decision-making: Its role and performance implications," International Journal of Accounting Information Systems, Elsevier, vol. 38(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jban00:v:3:y:2016:i:2:p:1-32. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.