Big Data Analytics
Big Data analytics is complex, in order to succeed organisations need to invest in the people behind the technology. Strengths and weaknesses are considered and practical case studies of implementation are shared to help organisations build up their Big Data capabilities.
Technique Overview
Big Data Analytics Definition
Big Data analytics refers to the use of powerful tools and techniques to leverage data insights, trends and patterns from huge – often unstructured and disparate – data sets and make them easily and quickly accessible to business leaders, managers and other key stakeholders. These insights are used to inform and develop business strategies and plans (Bertolucci, 2013a; Zakir et al., 2015).
Big Data Analytics Description *
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Business Evidence
Strengths, weaknesses and examples of Big Data Analytics *
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Business Application
Implementation, success factors and measures of Big Data Analytics *
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Professional Tools
Big Data Analytics videos and downloads *
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Further Reading
Big Data Analytics web and print resources *
Big Data Analytics references (4 of up to 20) *
- Berman, J. (2013) Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information, Morgan Kaufmann, Waltham, MA.
- Bertolucci, J. (2013a) Big Data: A Practical Definition, InformationWeek, Aug 26. [online] Available at: (http://www.informationweek.com/big-data/big-data-analytics/big-data-a-practical-definition/d/d-id/1111290?) [Accessed December 2015].
- Bertolucci, J. (2013b) Big Data Analytics: Descriptive vs. Predictive vs. Prescriptive, InformationWeek, Dec 31. [online] Available at: (http://www.informationweek.com/big-data/big-data-analytics/big-data-analytics-descriptive-vs-predictive-vs-prescriptive/d/d-id/1113279) [Accessed November 2015].
- Buttle, F. & Maklan, S. (2015) Customer Relationship Management: Concepts and Technologies, Routledge, New York.
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