The Elements of Persuasion: Big Data Techniques

Intuition becomes increasingly valuable in the new information society precisely because there is so much data.

—John Naisbitt

Chapter 2 helped us get comfortable with Big Data, at least at a conceptual level. At this point, we know a good deal about the general characteristics of Big Data—its DNA. But Big Data is a catchall, an umbrella classification that encompasses many fields and subfields. In this chapter, we drill down and explore those specific fields and subfields. What are the specific elements or fields that comprise Big Data? How can these fields help us make sense of Big Data? Answering these questions is the goal of this chapter.

In May 2011, management consulting firm McKinsey released a lengthy e-book titled Big Data: The Next Frontier for Innovation, Competition, and Productivity.1 In it, the authors list many techniques for analyzing Big Data. While not a definitive list, the following tools can be used to operationalize Big Data:

A/B testing, association rule learning, classification, cluster analysis, collaborative filtering, crowdsourcing, data fusion and integration, data mining, ensemble learning, genetic algorithms, machine learning, natural language processing (NLP), neural networks, pattern recognition, predictive modeling, regression, radio-frequency identification (RFID), sentiment analysis, signal processing, supervised and unsupervised learning, simulation, text analytics,42 time series analysis, ...

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