Analytics Essay

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Analytics is the discovery and communication of meaningful patterns in data. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance. Analytics often favors data visualization to communicate insight. The most common application of analytics is to business data in order to describe, predict and improve business performance. Specifically, arenas within analytics include enterprise decision management, retail analytics, marketing and web analytics, predictive science, credit risk analysis, and fraud analytics. Since analytics can require extensive computation (See Big Data), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.[1] Contents 1 Analytics vs Analysis 2 Examples 2.1 Marketing optimization 2.2 Portfolio analysis 3 Challenges 4 See also 5 References 6 External links Analytics vs Analysis Analytics is a two-sided coin. On one side, it uses descriptive and predictive models to gain valuable knowledge from data - data analysis. On the other, analytics uses this insight to recommend action or to guide decision making - communication. Thus, analytics is not so much concerned with individual analyses or analysis steps, but with the entire methodology. There is a pronounced tendency to use the term analytics in business settings e.g. text analytics vs. the more generic text mining to emphasize this broader perspective. Examples Marketing optimization Marketing has evolved from a creative process into a highly data-driven process. Marketing organizations use analytics to determine the outcomes of campaigns or efforts and to guide decisions for investment and consumer targeting. Demographic

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