This post is by Alyssa Zeisler from MediaShift
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When it comes to using data in the newsroom, spending a few minutes to structure your thinking before diving into a data set can make all the difference in getting actionable results afterwards. At the Financial Times, our data team fields questions from individual reporters, news desks, the audience engagement team, and managers from all parts of the business. To manage that intake and ensure data is effectively used, the team uses a Google form to help those making the request think through what they are asking for. It includes two simple, yet absolutely critical, prompts:
- What is the question you are trying to answer?
- What action(s)/decision(s) will be driven by the answer?
It’s our job to help others in the newsroomWithout a doubt, this is an excellent starting point for any good data query (which is any question that can be answered with analytics). But using
effectively is still quite new in newsrooms. Many reporters and editors are not familiar with structuring their thinking this way. For instance, many reporters and editors ask about the metrics they are familiar with (page views, shares, etc) instead of being open to new types of measurement and analysis. Additionally, using a Google form (instead of just emailing or asking directly) is outside of the norm for many. As a result, the audience engagement team often acts as the intermediary between data and journalists. It is our job to work with others in the newsroom and help them to think through what they want to know. In these instances, I’ve found that there are two parallel questions we can use in the newsroom that help ensure the resulting data is useful: why and how. Last week, for instance, a journalist asked me for a breakdown of his readership against that of our full audience base. Asking him “why” led to: “Is there an unmet demand for my content?” And asking “why” again led to what he really wanted: “I would like to build a case for additional promotion of my articles.” He wanted to understand whether there was an unmet demand for his content that additional promotion would satisfy, a query that data can help us answer. But he had made an incorrect assumption about the best way to ascertain that information. None of the three ways of framing this newsroom request — a readership breakdown, unmet demand or case for more promotion — were inherently wrong, but the third iteration was more focused and would have led to a very different method of data analysis than what he had initially asked for. If asked about promotion opportunities, the resulting analysis would be linked directly to whether and how his articles should be promoted. The first and second questions did not, as they weren’t connected to what he really wanted to find out or the possible resulting actions. Knowing what question you are asking and why you are asking isn’t the same as being willing to incorporate it into your thinking and behavior. Similarly, understanding what actions could be driven by this data is not always the same as being ready to take action.