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Predictive Analytics For Dummies, 2nd Edition by Dr. Tommy Jung, Dr. Mohamed Chaouchi, Dr. Anasse Bari

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Chapter 16

Targeting Big Data

IN THIS CHAPTER

Identifying technological trends in predictive analytics

Exploring predictive analytics as a service

Applying open-source tools

Selecting a large-scale big data framework

At a broad level, big data is the mass of data generated at every moment. It includes — for openers — data emerging in real time from

  • Online social networks such as Facebook
  • Micro-blogs such as Twitter
  • Online customer-transaction data
  • Climate data gathered from sensors
  • GPS locations for every device equipped with GPS
  • User queries on search engines such as Google

And that list barely scratches the surface; think of big data as a growing worldwide layer of data. What to do with it all? Answer: Start by using predictive analytics as a means to extract valuable information from the mass of big data, find patterns in the data, learn new insights, and predict future outcomes.

Here's a familiar example: You might have noticed ads appearing on websites while you're browsing that “just happen” to mention products that you already intended to buy. No magic at work here; the sites that show such ads are utilizing predictive analytics to data-mine (dig up insights from) big data: your buying patterns leave a trail of valuable data online; the well-targeted ads show that someone is using that data.

This chapter shows you how predictive analytics can crack the big-data nut and extract the nutmeat. First we guide you through a number of trends in the predictive analytics market ...

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