Chapter 3
Exploring Your Data Types and Associated Techniques
IN THIS CHAPTER
Understanding your data, its types, and its categories
Capturing the driving forces
Connecting predictive analytics to statistics, data mining, and machine learning
In this information age, data is being accumulated at such a rapid pace that it can be overwhelming. That data is usually stored in a database, or scattered across documents, e-mails, and text or audiovisual files.
Knowing your data types — whether attitudinal or behavioral, structured or unstructured, static or streamed — will position you to have a deeper and broader understanding of your data. Learning how to categorize your data can bring you the rest of the way to that deeper understanding — which in turn can facilitate your predictive analytics efforts.
The handiest way to define those efforts is in terms of the tools they use: Predictive analytics is an approach to business data that uses the techniques, tools, and algorithms of three disciplines — data mining, statistics, and machine learning — to develop a predictive model. This chapter covers these disciplines, but first it explores data types. When carefully built, that model can help decision-makers spot trends and patterns that represent enhanced business opportunities. Understanding the connection between predictive analytics and the three disciplines that provide its primary tools will strengthen your analysis.
There are two major ways to implement predictive analytics:
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