{"id":892,"date":"2019-03-20T09:40:23","date_gmt":"2019-03-20T16:40:23","guid":{"rendered":"http:\/\/www.lightsondata.com\/?p=892"},"modified":"2020-06-25T09:30:53","modified_gmt":"2020-06-25T16:30:53","slug":"data-story-anatomy","status":"publish","type":"post","link":"https:\/\/www.lightsondata.com\/data-story-anatomy\/","title":{"rendered":"The anatomy of a data story"},"content":{"rendered":"

Stories are how people make sense of the world, so it follows that they\u2019d also be our way of making sense of data. As much as technology can <\/span>facilitate <\/span><\/i>data storytelling, it cannot (yet) perform the pivotal step of placing data into the human context. That\u2019s our job, and we\u2019re really good at it. We\u2019re such natural storytellers, in fact, that most of us already use a story format for presenting information.<\/span><\/p>\n

Take the standard <\/span>scientific research paper format<\/span><\/a>. After the title and authors (our main cast of characters), we get a summary of the story in the article abstract. It\u2019s like one of Shakespeare\u2019s <\/span>spoiler-laden prologues<\/span><\/a>, a quick preview of what\u2019s to come. After that comes the Introduction, complete with the scientific problem and research question that incites the story\u2019s action, the experiment itself. The Materials and Methods sections chronicle the heroes\u2019 journey as they conduct the experiment, and the Results and Discussion sections are when we find out how they fared. Beginning, middle, end. Rising action, falling action, resolution. Story!<\/span><\/p>\n

But there\u2019s a big difference between following a <\/span>narrative arc<\/span><\/a> and telling a story that will deliver an impactful message and linger with audience members. In his <\/span>TED Talk on data storytelling<\/span><\/a>, blogger Ben Wellington of I Quant NY identifies four features of lasting stories, data-driven or not. Great data stories:<\/span><\/p>\n

    \n
  1. Connect with people<\/span><\/li>\n
  2. Try to convey one idea<\/span><\/li>\n
  3. Keep it simple<\/span><\/li>\n
  4. Explore a topic you know well<\/span><\/li>\n<\/ol>\n

    So let\u2019s walk through the process of composing a data story starting with the data itself.<\/span><\/p>\n

    This Story is About<\/span><\/h2>\n

    The function of data in a data story is to tell <\/span>what happened<\/strong>, and figuring out what happened typically starts with a question. Typical business-oriented questions usually revolve around sales or other KPIs, but they could be about anything\u2014the results of a customer satisfaction survey, the efficacy of a policy change, the user behavior on a website. <\/span><\/p>\n

    Once the overall question is established (e.g., How are our sales doing this quarter compared to last quarter?), you can begin to partition the data into smaller, more manageable pieces. Maybe you can track sales by product or by sales representative or by company branch. Maybe you can look for correlations between revenue and other variables.<\/span><\/p>\n

    If you don\u2019t have a question to answer or an artificial intelligence to point you to an interesting trend, you\u2019ll likely have to do some data discovery and exploration to find a story worth telling. This is the process Ben Wellington employs when doing research for his blog posts. He pulls public information from <\/span>NYC Open Data<\/span><\/a> and begins looking for patterns using the following template:<\/span><\/p>\n

    [Aggregation Type] of [Metric] by [Dimension] in NYC<\/span><\/em><\/p>\n

    The aggregation type is typically the sum, average, or count of the metric, and the dimension in his case is usually a type of demographic. In his TED Talk, for example, he shows a map of displaying the total number of parking tickets given to residents of different states in NYC over a period of time, so the completed template would read:<\/span><\/p>\n

    [Sum] of [Parking Tickets] by [State of Residence] in NYC<\/span><\/em><\/p>\n

    His explorations showed that that residents of New Jersey mostly get ticketed in lower Manhattan while residents of California mostly get ticketed in Brooklyn, suggesting something about the cultural proclivities of each group. <\/span><\/p>\n

    Once you\u2019ve found an interesting pattern, the next step in familiarizing yourself with the data is to ask <\/span>why<\/strong>. Why do Californians get ticketed in Brooklyn more than in Manhattan? Perhaps there are other metrics to help explain the trend.<\/span><\/p>\n

    Ultimately, your goal in gathering and analyzing the data is to evaluate it. Are the trends favorable or unfavorable? What action do you hope to incite with your data story? The evaluation is <\/span>why<\/strong> you\u2019re going to the trouble of crafting a data story rather than a straightforward report. Ben Wellington noticed that the <\/span>NYPD was systematically ticketing legally parked cars<\/span><\/a> and wanted authorities to put a stop to it. Without a purpose, your story will fail to connect with the audience, so work with your data until you have a clear narrative direction.<\/span><\/p>\n

    Visualizing Your Data<\/span><\/h2>\n

    Your visuals should be clear and an memorable, but memorable doesn\u2019t have to mean gimmicky. <\/span><\/p>\n

    Cole Nussbaumer Knaflic, formerly the People Analytics Manager at Google, today hosts workshops called Storytelling with Data and maintains a blog by the same name. In her workshops, attendees sometimes request to explore novel ways of visualizing data. So they take simple data sets and illustrate them using dot plots and slope graphs and other less-conventional charts. In the end, though, they come to the conclusion that \u201c<\/span>simple often beats sexy<\/span><\/a>.\u201d <\/span><\/p>\n

    Knaflic explains that \u201cit\u2019s not the graph that makes the data interesting. Rather, it’s the story you build around it\u2014the way you make it something your audience cares about, something that resonates with them\u2014that\u2019s what makes data interesting<\/em>.\u201d<\/span><\/p>\n

    \"the<\/p>\n

    So, when designing your visuals, take clarity and conciseness over sizzle\u2014but also consider what it is you want to emphasize. In <\/span>The Time You Have (In Jellybeans)<\/span><\/a>, performance artist Hosea Jan Frank emphasizes the scarcity of free time by contrasting quantities of jellybeans. Any time you can give your audience a more familiar point of reference, it can help drive an idea home. Consider the following tactics to either visually or conceptually make your figures more relatable:<\/span><\/p>\n