The most common exercise of analytics is to business data in order to describe, predict and ameliorate business performance. Specifically, arenas within analytics include enterprise decision management, retail analytics, marketing and web analytics, predictive science, credit risk analysis, and tarradiddle analytics. Since analytics can require extensive computation ( count Big Data), the algorithms and computer software make design ofd 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 cognition from data - data analysis.
On the other, analytics uses this insight to recommend action at law or to guide decision making - communication. Thus, analytics is not so much concerned with individual analyses or analysis steps, solely 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 wine 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 studies, client segmentation, conjoint analysis and other techniques allow marketers to use large...If you ask to get a full essay, order it on our website: Orderessay
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