Interviewing Your Data, Part 1

data-journalism
(Photo by Tech Tank)

Today I want to take a closer look at the Data Journalism Handbook by the Open Knowledge Foundation. The Handbook provides a great introduction to those that have reported on or plan to report on data in an easy-to-understand format. I think it’s so essential for the story to be told about what’s going on in the data world in order for the ‘data ecosystem’ I’ve mentioned several times in this blog to flourish. All the hard work of data scientists and data innovators needs to be explained in a way that’s interesting and understandable. And admittedly more and more journalists are using data in their stories because we are a society that craves numbers and information.

You may be asking yourself – “What is a data interview?” The Data Journalism Handbook says that a data interview is very similar to interviewing a real person. In the case of data journalism, you’re asking questions of the data rather than a live source. One example is a journalist that wants to take a closer look at where crime happens in a particular city.  They would need to decide which data is needed to answer that question. Perhaps they would create a data  request that includes date and time each crime was reported, the crime type, and the address where the crime occurred.

It’s also important to know what questions the data can’t often answer such as the “why”. In the crime data example, the dataset the journalist obtains may not include information about the victims, the value of any stolen property or the names of officers that make the arrests. Also the journalist may not have a complete dataset if for instance the crime data is only publically available for the last three years. As I mentioned last week, data science answers the “what” but not the “why.” Those and other types of questions may be outside of what can be reported.

MediaShift describes what the Data Journalism Handbook also mentions in their 2014 article – that the purpose of why the data was initially collected will probably be different than your purpose for it as a data journalist. I have seen this first hand in my experience when I tried to analyze government data that was created to fulfill a legislative mandate rather than for other practical purposes. The data is often hard to get, clean and understand. Just as humans journalists interview have flaws, so do data. Even if the original purpose for publishing the data is different than yours, a good story in the data can be found.

In two weeks, I’ll talk more about some of the steps in the process of data-driven journalism. Look for a special Veteran’s Day holiday data blog next week.

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