Data Storytelling: Craft Meaningful Visual Stories That Drive Action

Data Storytelling: Craft Meaningful Visual Stories That Drive Action

It is no secret that there is an overload of data in today’s world, often impeding the decision-making process for many brands. And analytics without decisions is a waste of the resources used to acquire these programs as well as a missed opportunity for ROI, innovation, and transformation. Resources, such as Brent DykesWeb Analytics Action Hero, are emerging to provide guidance for a drive to action.

Decision making requires emotion, according to brain science, and data stories act as the bridge between logic and emotion. Using stories, we build an emotional bond with our audience, accessing the brain on both the logical and emotional sides. Humans hear statistics, but we feel stories.

To communicate high-value, data-driven insights, analysts need stories. But, data storytelling is difficult. Read on to learn how to build a compelling story with the right blend of narrative, data, and visualization.

The classic narrative structure has a beginning that sets the stage, a middle that builds to a climax, and an ending. In data storytelling, that structure is modified based on audience impact. In thinking about an audience’s goals first, start with the “ah ha” moment, the major finding or key insight that tells them why they should care. Then, give them some background on the current situation and demonstrate why they should believe you. Having set this hook, now you can show your work — just enough to create context and to make the connections that reveal the deeper insight. Finally, end with solutions and next steps.

As analysts, we struggle with the dilemma of when to generalize and when to be detailed. Resolving this struggle requires understanding one’s audience and tailoring the presentation to their needs. Our job is to be knee deep in the noise and data, finding the insights among the false leads. When we report on these insights, audience members of different specialties are not as familiar with the data, so we need to help them catch up to our head start.

The analyst’s job is to summarize and deliver what an audience needs in a way that explains and does not merely describe. Our job is to eliminate the noise and detail that they don’t need and then add focus.

There is a place for detailed written explanations but not in a presentation. You should be the one communicating — not the slide. In fact, you should go way beyond the slide; instead, interact with the individual pieces of a chart to tell the story in a way that explains the symbolized data.

Visuals don’t always have to be pie charts or bar graphs; they just have to be something that helps provide context, connects the pieces of the story, and moves that story forward. For example, a simple overlay of key statistics on the homepage may communicate meaning better than a customer flow diagram. Again, the tools we use to find the insights are not necessarily the same ones we use to explain those insights to others. Depending on what our audience needs and the point we’re trying to convey, we might display the data in a completely different way from how we arrived at the insight. We need to zoom in, give the data some focus — and most importantly, give it context.

When you bring together narrative, data, and visualizations, the synergy of these three clearly elucidates powerful insights that should spur decision and change. It shows that the Hopi people are right when they say, “Those who tell the stories rule the world.”

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