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Big Data offers enterprises the potential for predictive metrics and insightful statistics, but these data sets are often so large that they defy traditional data warehousing and analysis methods. However, if properly stored and analyzed, businesses can track customer habits, fraud, advertising effectiveness, and other statistics on a scale previously unattainable. The challenge for enterprises is not so much how or where to store the data, but how to meaningfully analyze it for competitive advantage. Big Data storage and Big Data analytics, while naturally related, are not identical. Technologies associated with Big Data analytics tackle the problem of drawing meaningful information with three key characteristics. First, they concede that traditional data warehouses are too slow and too small-scale. Second, they seek to combine and leverage data from widely divergent data sources in both structured and unstructured forms. Third, they acknowledge that the analysis must be both time- and cost-effective, even while deriving from a legion of diverse data sources including mobile devices, the Internet, social networking, and Radio-frequency identification (RFID). The relative newness and desirability of Big Data analytics combine to make it a diverse and emergent field. As such, one can identify four significant developmental segments: MapReduce, scalable database, real-time stream processing, and Big Data appliance. Not all Big Data is unstructured, and the open-source NoSQL uses a distributed and horizontally-scalable database to specifically target streaming media and high-traffic websites. As the name suggests, real-time stream processing uses real-time analytics to provide up-to-the-minute information about an enterprise's customers. Enterprises seeking to edge out their rivals are looking to Big Data. Storage is only the first part of the battle, and those than can efficiently analyze the new wealth of information better than their competitors will almost certainly profit from it. These ambitious enterprises would do well to regularly reassess their Big Data analytics methods.
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