The following is a repost of a blog post that was originally made here, on Medium.
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I had a miserable time showing my work in math class. Usually, I used ācommon sense,ā context, process of elimination, or guessed. I preferred English. If there was no real way to quantify an argument, how could you be wrong?
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So I latched onto information architecture (IA), a soft science, rather than, say, architecture, which lives on mathematical precision that literally makes or breaks a project. But the more I work with data, the more we measure, and the more we test, Iāve suspected that maybe information can be quantified, too.
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Is content data? Not really. A few years ago, I would have eye-rolled at calculating content in order to find the ārightā answer. But with IA-heavy projects such as redesigns for thousand-page websites, the scale of information begs for process. I decided to scope out an approach where we pretend content is data. Information can be corralled into topics, and I started counting how often each topic showed up in auditing and research. If certain topics leap out of the data, thatās where your content strategy should start.
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The basic answer would be content strategists. The real answer is anyone wrangling incredible amounts of web-based information. For the purposes of this article, itās UX designers who already have a solid grip on user research, decent exposure to IA, and intermediate spreadsheet skills.
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As an exercise, what follows is a sample set of rationale and steps for a standard .edu website redesign.
Actually, weāre counting āconceptsāāāāhypothetical content which we can infer from research and auditing. Each concept is a common theme, topic, object, or subject that succinctly represents user-facing information.
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Letās say surveys indicate college students want to ādecide what to major in.ā Relevant concepts on a site youāre planning might include ācoursesā and āfaculty.ā To count the global relative importance of faculty-related content versus course content, youāll want to pin up all the research you have, then count how often ācoursesā and āfacultyā are suggested. From this example, you might resolve to feature faculty profiles higher on the āAcademicsā landing page, or justify pulling detailed course descriptions into the CMS.
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The goal is to inform content strategy through data, rather than pre-existing assumptions, opinions, and beliefs about content.
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If you arenāt quantifying content in some way, youāll notice patterns you wanted to find. Your biases will lead you straight to takeaways that validate what you already comped. When you start from a vision youāre emotionally attached to, too much is at stake to prove yourself wrong.
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Hopefully, this approach makes the data-to-content translation process less daunting and more manageable. It definitely worked for me. At worst, weāll stop spending weeks collecting great, quantifiable, objective numbers, only to undermine the sterile purity of the data by letting our visions cherry-pick stats into stories. At best, weāll weave content decisions from numbers into words, like a tiny chunk of a Rosetta Stone between math and English.