In the analog age, collecting data about behavior—who does what, and when—was expensive, and therefore relatively rare. Now, in the digital age, the behaviors of billions of people are recorded, stored, and analyzable. For example, every time you click on a website, make a call on your mobile phone, or pay for something with your credit card, a digital record of your behavior is created and stored by a business. Because these types of data are a byproduct of people’s everyday actions, they are often called digital traces. In addition to these traces held by businesses, governments also have incredibly rich data about both people and businesses. Together these business and government records are often called big data.
The ever-rising flood of big data means that we have moved from a world where behavioral data was scarce to a world where behavioral data is plentiful. A first step to learning from big data is realizing that it is part of a broader category of data that has been used for social research for many years: observational data. Roughly, observational data is any data that results from observing a social system without intervening in some way. A crude way to think about it is that observational data is everything that does not involve talking with people (e.g., surveys, the topic of chapter 3) or changing people’s environments (e.g., experiments, the topic of chapter 4). Thus, in addition to business and government records, observational data also includes things like the text of newspaper articles and satellite photos.
This chapter has three parts. First, in section 2.2, I describe big data sources in more detail and clarify a fundamental difference between them and the data that have typically been used for social research in the past. Then, in section 2.3, I describe ten common characteristics of big data sources. Understanding these characteristics enables you to quickly recognize the strengths and weaknesses of existing sources and will help you harness the new sources that will be available in the future. Finally, in section 2.4, I describe three main research strategies that you can use to learn from observational data: counting things, forecasting things, and approximating an experiment.
