Interviewing Your Data, Part 2

data_driven_journalism_process

In my November 1 blog, I provided an introduction to some tips when you are interviewing data for a story. I said it was important to know what questions the data can and cannot answer and to recognize the published data was probably collected for a different purpose than your own as mentioned by Technology Tank and others. Today I want to discuss what I see as the four steps in the data journalism process: 1. Get the Data, 2. Filter the Data, 3. Visualize the Data and 4. Present the Story.

According to the Tow Center for Digital Journalism, today’s big data revolution coupled with an increasing emphasis on technology have brought more and more data into journalism. The first step in this process is to collect, verify and clean the data. Recognizing a trustworthy data source is key to getting good, quality data. A NY Times journalist sums it up nicely “Data journalism can be created quickly or slowly, over weeks, months or years. Either way, journalists still have to confirm their sources, whether they’re people or data sets, and present them in context.’

Filtering or cleansing the data is the next step in the data-driven journalism process. Learning about the subject matter where your data was created is important even if you’re not an expert in that subject. There are some that argue that cleaning the data is not within the scope of a data journalist. I would say that this is probably the case but errors in the data or missing data need to be noted within the article to prevent confusion or false conclusions. Being a data skeptic is an important role for the journalist as the public and something I’ve mentioned in my blog before.

Translating the data from numbers to a visualization and then turning that into a story is both an art and a science. Some free great visualization tools that are designed for a more non-technical audience and that I like are Tableau, Info.gram, Piktochart and Wordle. After you’ve visualized the data using these or other tools, it’s time to tell the story. A great Lynda tutorial sums up how to tell a story with data by creating a narrative for the data, identifying what you want to say with the data, and determining what your audience needs to know. Communicating the data story effectively completes the journey of data-driven journalism.

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