British statistician Karl Pearson (1857-1936) is attributed with saying, “That which is measured improves. That which is measured and reported improves exponentially.” If you manage a business or a part of an organization, you most likely have a dashboard that helps you monitor performance. Whether or not you’re entirely happy with your current dashboards, most business owners and executives will admit they would feel blind and vulnerable without them. As organizations become increasingly data intensive, it’s essential to have quick, easy access to real-time information in order to make timely corrections and enhancements.
When you look at your dashboards, do they tell stories? By tell, I don’t just mean provide an assortment of information or KPI-based reporting. Do they clearly explain specific insights so you can understand and act on them? If your dashboards don’t do this, the next question is should they? Or maybe even, can they?
It’s common for most analytics vendors and industry experts to suggest dashboards tell stories (full disclosure: I work for Domo). Indeed, data storytelling is a popular trend today as we are awash with data and often struggle to communicate insights effectively. However, while dashboards can be used to tell data stories, the vast majority of them don’t. Why? The intent of most dashboards isn’t to communicate a singular story based on a particular insight. Instead, they visualize a series of key metrics and dimensions so users can discover potential insights—key results, trends, patterns, anomalies, outliers, correlations, and so on. Rather than telling a specific story, most dashboards are tasked with framing essential information for end users. They are primarily exploratory—not explanatory.
The difference between storyframing and storytelling
Imagine if you had to routinely comb through ALL of your data each day or week. You would have to wade through so much irrelevant information and random noise, it would be nearly impossible to extract any valuable signals. However, when an executive dashboard is properly framed and aligned to your strategic priorities, it can provide an invaluable window into business performance. By focusing on the most relevant and essential information, a dashboard frames the types of potential insights and stories that can emerge from the data.
For example, a dashboard that focuses on revenue performance will frame different insights than one that focuses on customer satisfaction or employee retention. Storyframing isn’t a less noble or important task for a dashboard than data storytelling. In fact, both of these analytical tasks contribute to driving action and value from your data—just at different stages in the analysis process.
As you move from data to action in the analysis journey, storyframing occurs at an earlier stage in the process than storytelling. A storyframing dashboard turns raw data into a relevant, useful set of information that helps people to monitor and understand how things are performing. It’s similar to placing CCTV cameras in strategic areas of your business to monitor what’s happening. Most of the footage taken by security cameras is routine and unnoteworthy. Likewise, most of the data flowing through a storyframing dashboard won’t necessarily translate into any meaningful insights. However, when something unusual or unexpected occurs in the data, the dashboard’s role is to ensure potential insights are easy to spot and interpret. It might even allow people to drill further into the data for clarification and deeper insights. As long as a storyframing dashboard remains aligned with your strategic priorities, it can deliver up multiple insights over time.
Once you’ve identified a key insight that you want to share with another party, you then shift to storytelling. In most cases, you can’t pass along raw insights and expect them to be properly understood. Most insights must be explained before other people can fully grasp and act on them. Rather than relying on live data that may shift over time, you assemble static snapshots of key data points and build them into a curated data story. Extraneous or irrelevant data will be removed to focus and strengthen the narrative. Visual highlighting and annotations are critical aspects in drawing out the story from the data. In addition, the flow or sequence of sharing insights is also crucial as it forms the narrative structure of the story. After a data story has communicated its key findings to its intended audience, it will have run its course and can be archived.
The following table summarizes the main differences between storyframing and storytelling:
Storyframing and storytelling often go hand-in-hand in driving value from your data. For example, a storyframing dashboard might help you pinpoint a stage in your online application process where applicants are abandoning at a higher rate than expected. After further examination, you identify the source of the problem—a browser compatibility issue. If you’re able to fix the issue on your own, storytelling isn’t necessary since no communication or coordination with others is required. In this scenario, storyframing can drive action without needing any storytelling. However, if the required action is dependent on other people (their approval, involvement, support, etc.), you may need to turn to storytelling. A data story can explain what’s happening and the urgency of the situation. It can also help inform the audience of what must be done to correct the problem. However, depending on the audience and scenario, a dashboard may not be the right delivery vehicle for a data story. Instead, a presentation, report or video may be more appropriate.
The vast majority of dashboards aren’t going to tell data stories because they were designed for a different purpose: storyframing—not storytelling. Storyframing helps you identify and extract valuable insights from your data, and storytelling helps you to explain key insights to others so they can understand and act on them. No dashboard can serve both masters. It will either frame the information so potential insights can emerge or tell a compelling story to explain a specific insight. While a dashboard can frame multiple key metrics and dimensions, it won’t be able to tell multiple stories at the same time (at least not effectively).
While most companies already have a good handle on storyframing, many need to augment their storytelling capabilities. Data stories will play an integral role in the last mile of the analysis journey where insights are turned into action and value. However, don’t believe technology vendors that tout natural language generation (NLG) as a “storytelling” capability when it’s just describing—not explaining—visualized data. Don’t assume alerting on data anomalies is storytelling when it’s only highlighting potential problems or opportunities—not clarifying what caused them or what they mean. You must ensure your people are empowered with both the right tools and skills to tell stories with your data—and only then will you witness a higher return on your data investments.
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