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Making sense of data through analysis

It can be fairly easy to get data, but it's always easier to misunderstand it. Before you can draw conclusions about a dataset, you have to clean it and start asking questions. Then, as you refine your data, you can highlight significant findings worthy of further reporting.

Data are not magic beans. They don’t automatically write your story or cause you to see the light. Rather, they are a raw material, like interviews. You must ask the data questions, or query the data, to understand what it has to say. The process of querying data is also key to cleaning and refining your data — to make sure information can be compared and outliers aren’t mistakes.

In many ways, working with data is like interviewing a live source. You ask questions of the data and get it to reveal the answers. But just as a source can only give answers about which he or she has information, a data set can only answer questions for which it has the right records and the proper variables. This means that you should consider carefully what questions you need to answer even before you acquire your data. Basically, you work backwards. First, list the data-evidenced statements you want to make in your story. Then decide which variables and records you would have to acquire and analyze in order to make those statements.