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dc.contributor.authorSelf, Richard
dc.contributor.authorVoorhis, Dave
dc.date.accessioned2018-03-16T13:56:20Z
dc.date.available2018-03-16T13:56:20Z
dc.date.issued2015-02-27
dc.identifier.citationSelf, R. and Voorhis, D. (2015) 'Tools and technologies for the implementation of Big Data.' in Babak, A. et al (eds.) Application of Big Data for National Security: A Practitioner's Guide to Emerging Technologies, London: Elsevier, pp. 140-154en
dc.identifier.isbn9780128019672
dc.identifier.doi10.1016/B978-0-12-801967-2.00010-0
dc.identifier.urihttp://hdl.handle.net/10545/622362
dc.description.abstractThis chapter uses the five V’s of Big Data (volume, velocity, variety, veracity, and value) to form the basis for consideration of the current status and issues relating to the introduction of Big Data analysis into organizations. The first three are critical to understanding the implications and consequences of available choices for the techniques, tools, and order to provide an understanding of choices that need to be made based on understanding the nature of the data sources and the content. All five V’s are invoked to evaluate some of the most critical issues involved in the choices made during the early stages of implementing a Big Data analytics project. Big Data analytics is a comparatively new field; as such, it is important to recognize that elements are currently well along the Gartner hype cycle into productive use. The concept of the planning fallacy is used with information technology project success reference class data created by the Standish Group to improve the success rates of Big Data projects. International Organization for Standardization 27002 provides a basis considering critical issues raised by data protection regimes in relation to the sources and locations of data and processing of Big Data.
dc.description.sponsorshipN/Aen
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urlhttps://www.sciencedirect.com/science/article/pii/B9780128019672000100en
dc.relation.urlhttps://www.sciencedirect.com/science/book/9780128019672en
dc.subjectData protectionen
dc.subjectGovernanceen
dc.subjectBig Data analyticsen
dc.titleTools and technologies for the implementation of Big Data.en
dc.typeBook chapteren
dc.contributor.departmentUniversity of Derbyen
html.description.abstractThis chapter uses the five V’s of Big Data (volume, velocity, variety, veracity, and value) to form the basis for consideration of the current status and issues relating to the introduction of Big Data analysis into organizations. The first three are critical to understanding the implications and consequences of available choices for the techniques, tools, and order to provide an understanding of choices that need to be made based on understanding the nature of the data sources and the content. All five V’s are invoked to evaluate some of the most critical issues involved in the choices made during the early stages of implementing a Big Data analytics project. Big Data analytics is a comparatively new field; as such, it is important to recognize that elements are currently well along the Gartner hype cycle into productive use. The concept of the planning fallacy is used with information technology project success reference class data created by the Standish Group to improve the success rates of Big Data projects. International Organization for Standardization 27002 provides a basis considering critical issues raised by data protection regimes in relation to the sources and locations of data and processing of Big Data.


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