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Big data analytics: disruptive technologies for changing the game

By: Sathi, Arvind.
Publisher: Boise Mc Press 2012Description: 73 p.ISBN: 9781583473801.Subject(s): Computer and information systems | Big data - Strategies | Information technologies - Data processing | Electronic data processing | Data miningDDC classification: 005.74015 Summary: Most traditional approaches to data analytics aim to align the operational activities of the organization with the specific business strategies of C-level executives. The process accomplishes this by identifying key measurements of business performance, developing the internal (or external) metrics and governance principles, and then implementing a data analytics framework to deliver the information accurately. This enables management to monitor the key performance metrics and instruct managers where improvements are required. By comparison, big data analytics is at a completely different level of complexity. Instead of having too little data, big data provides an overwhelming number of data points from myriad input devices and services that are constantly refreshing in real time—often providing unstructured information, conflicting signals, and contextless meanings. Most of the time, C-level execs have no idea of how these data points (cell phone data, social media data, sensor data, GPS data, etc.) might benefit their business strategies. Or they may have heard about specific big data projects that offer transformative business benefits. In other words, they recognize big data's potential but don't yet have the tools to develop the insights or make meaningful decisions. (http://www.mcpressonline.com/business-intelligence/book-review-big-data-analytics-disruptive-technologies-for-changing-the-game.html)
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Most traditional approaches to data analytics aim to align the operational activities of the organization with the specific business strategies of C-level executives. The process accomplishes this by identifying key measurements of business performance, developing the internal (or external) metrics and governance principles, and then implementing a data analytics framework to deliver the information accurately. This enables management to monitor the key performance metrics and instruct managers where improvements are required.



By comparison, big data analytics is at a completely different level of complexity. Instead of having too little data, big data provides an overwhelming number of data points from myriad input devices and services that are constantly refreshing in real time—often providing unstructured information, conflicting signals, and contextless meanings. Most of the time, C-level execs have no idea of how these data points (cell phone data, social media data, sensor data, GPS data, etc.) might benefit their business strategies. Or they may have heard about specific big data projects that offer transformative business benefits. In other words, they recognize big data's potential but don't yet have the tools to develop the insights or make meaningful decisions. (http://www.mcpressonline.com/business-intelligence/book-review-big-data-analytics-disruptive-technologies-for-changing-the-game.html)

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