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Predictive Analytics For Dummies by Tommy Jung, Mohamed Chaouchi, Anasse Bari

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Chapter 4

Complexities of Data

In This Chapter

arrow Meeting the challenges of big data

arrow Making your data semantically retrievable

arrow Distinguishing between business intelligence and data analytics

arrow Using visualizations to guide the cleaning of data

Your Google search log, tweets, Facebook status updates, and bank statements tell a story about your life. Your geographical locations logged by your cellphone carrier, your most frequent places visited, and your online purchases can define your habits, your preferences, and your personality.

This avalanche of data, being generated at every moment, is referred to as big data, and it’s the main driver of many predictive analytics models. Capturing all different types of data together in one place and applying analytics to it is a highly complex task. However, you might be surprised that only about 1 percent of that data is used for analysis that results in real, valuable results. This 1 percent of big data is actually smart data — the nucleus that makes sense out of big data. Only this 1 percent will make it into the elevator pitch that justifies ...

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