000 02121 a2200217 4500
008 140323b2012 xxu||||| |||| 00| 0 eng d
020 _a9781583473801
082 _a005.74015
_bS2B4
100 _aSathi, Arvind
_9224999
245 _aBig data analytics: disruptive technologies for changing the game
_cSathi, Arvind
260 _c2012
_bMc Press
_aBoise
300 _a73 p.
365 _aINR
_b973.00
520 _aMost 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)
650 _aComputer and information systems
_9224095
650 _aBig data - Strategies
_9224096
650 _aInformation technologies - Data processing
_9224097
650 _aElectronic data processing
_91439
650 _aData mining
_956428
942 _cBK
999 _c176287
_d176287