By: Michael Benison

“The data speaks for itself” is a popular refrain that is usually used to demand your agreement or approval for any data point you may be skeptical about or attempt to reject completely.  You may have been presented with some numbers, pictures, videos, tables, charts and others at some point in attempt to help you make an informed decision or come to a certain persuadable conclusion but may not be convinced particularly in an era where misinformation seems to be on the ascendancy.  In essence, you may be right to reject the notion that data speaks for itself. For example, if you ever shopped online and you are like most people, you probably checked the ratings of the service or product but also looked beyond the number of stars and read the reviews.  

What you did was look for the stories around the ratings, so the ratings probably did not convince you as much as the stories told by previous and similar consumers. Sometimes you rely on word of mouth, recommendations by your friends and family, and other narratives about the product or service more than the start ratings.  So, essentially your decision is most likely influenced by the stories told than the data points (i.e. the ratings) themselves. What this means is that the 5-star or 4-star rating did not speak for itself, the stories around the data did speak for the star ratings. Data without a story or any interpretation is not meaningful. Accordingly, investments in data management practices without an effective data storytelling strategy is like taking a shower without water, it’s all soap. A data storytelling strategy is a good starting framework to lather and rinse your data to make it “cleaner and clearer” for effective decision-making.

Here are five effective ways to tell a good data story:  

          1. Identify, plan and design your story for its audience:  Everyone loves a good story but not every story is relevant to everyone. The same can be said of data, its value or usefulness is as good as its relevance to its audience or consumer. Therefore, a deliberate effort to delineate the type of audiences or consumers, what their needs are and what they all have in common is a critical first step to consider before beginning telling your data story. In most cases, you already had some audience in mind prior to investing and beginning your data management process, but what remains is where each intended audience fits into your data story. This is where identifying and isolating your audience based on several components including personas, profile, position, interests and needs is a good place to begin planning your data story.  Classifying the consumers of your data story should be a straightforward process. Start by asking yourself some questions such as; what do my audience know about the data, who is this data meant for, is the data meant for decision makers, the general public or certain interested parties? 
          2. Develop and tell your story to answer and resolve anticipated user questions and dilemmas:  You may have heard or read that a good story has certain elements such as character, setting, plot, conflict and resolution, a data story is not immune from these judging criteria. The character here is the data point around which your story revolves. All data evokes a lot of questions from many directions. For example, if you reported that unemployment decreased by  5%, several questions may follow from your audience. What was the unemployment rate before the decrease, what base year is under reference, how was the data collected, when was that data collected, who collected that data, who was sampled, what was the sample size, which industries contributed most to the decrease, what led to the collection of the data, what does the decrease generally mean and many more such questions is inevitable. It is therefore important to anticipate a vast range of questions that your data might induce and capture the answers in your data story. You should craft your story to elucidate the points of your data that have potential for confusion or misinterpretation by your audience while keeping the integrity of your data intact. An effective way of clarifying any anticipated obfuscated parts of your data points would be to provide a clear context and the environment around the collection and circumstances that produced the data points. The events and all related the data which I will call “data-plot” should be effectively discussed. The rate of unemployment decreased disproportionately such that it impacted the overall unemployment rate significantly. It would be informative to include in your story for good an explanatory value to illuminate the understanding of your audience. Like many elements of human engagement, data is not free from being controverted or if you like producing conflicts or misunderstanding. The questions that the data are likely to produce are sources of conflicts themselves. That’s why it’s important to state clearly all the possible contexts and assumptions made in the analysis process to help address any potential source of confusion or misinterpretation of your data point. After all, there is a reason you are telling the story, so make the purpose, the context and situation clear to the audience and clearly outline the parts of the data they can connect to.
          3. Use effective visualization techniques to support the understanding and interpretation of your audience:  Research has shown that where people look is tightly intertwined with what they decide. As a consequence, what people see or look influences their decisions.  With an ever decreasing attention span due to the vast volume and sheer velocity of increasing data, most people regardless of background or data experience are attuned to learning more from data with less attention and efforts required. Data visualization is essentially the effective antidote to give more with less amount of time, space and effort filling in the gap between audiences’ need for more details and minimal attention and effort. The goal of using visualizations in your story is not just to keep your audience interested or engaged but you actually want to guide their eyes and attention to focus on the parts of your data that matters to your story. Usually, your audience may have questions that they would like answers within the shortest amount of time possible before turning to your data story. Staying with our unemployment questions discussed earlier, your audience would want to find answers to those questions in a snapshot, so directing their attention to those data points on your visualization could positively tell your story with ease. In the design of your use visualization techniques such as visual cues, visual contrasts annotations, and color schemes that directs attention to the relevant data points and encourage the eyes to compare and contrast data.  For more on how to effectively use visualizations to influence the decision of your audience read my post, 6 ways your data visualizations can influence decisions.
          4. Match the most important data points of the story with the need of the audience. Your audience determines how you tell your story and their interests may vary from issues to issues. For example, the decrease in unemployment would be interesting to government leaders and they may be more interested in the details, but ordinary residents may not be interested in the details as much. It is important to first settle on the ultimate message that you want to convey based on your audience’s need. One of the most powerful data story techniques is by selecting the data points that may resonate most with your audience. For example, data stories meant for executives should match their need for their time limitations, hence summaries or conclusions could suffice for them, but managers would want more in-depth, actionable insights and deeper understanding about the “whys” and “whats” of the status of the data points.
          5. Keep your story simple and straight to the points that matter to the audience. A good data story is not complicated – it’s quite simple and straightforward. Not every data story you tell has to be riveting, what matters is the answers the story provides to the intended audience. Hence, all details that may detract from your core message must be left out to keep your message clear. Unarguably, we are inundated with so much data such that the principle that “less is more” couldn’t be more true today. Resist the urge to put too many details in your data story than needed. For instance, including in the decreasing unemployment data story details about people not seeking jobs, discouraged workers, weekly employment rates or theories of the decrease could detract from the core message of the data story. 

        In summary, your data may not tell the whole story or speak for themselves as the case may be, you will have to make them speak through your thoughtful stories to their appropriate audiences. The value of your data from the lens of your audience is as good as the stories around them. Just as you rely on the anecdotes together with the star ratings to inform your choice of a product or service, let your audience rely on your data story to make decisions. Thus your data story must understand the perspective of its audience, anticipate and address potential points of confusion, match the needs of its consumers and keep it simple, clear and visually organized to be impactful.