How to Tell Compelling Stories With Data

October 2, 2018

[billion photos]
[billion photos]

Faced with too much data and too many tools, analysts and marketers might easily find themselves challenged about how to communicate data effectively. Data can hold an incredible amount of value, but that value might not be realized unless insights are uncovered from it and translated into stories that drive business outcomes.

As Rudyard Kipling wrote, “If history were taught in the form of stories, it would never be forgotten.”

But how can PR pros use storytelling to creatively package data in meaningful ways, so it’s memorable and drives business results? Here are some tips to help you make sense of your information, so you can go beyond merely dumping data and tell compelling stories:

Structure your story

To ensure you include all the relevant information and your audience can clearly follow and navigate your story, it helps to follow a structure. Orient the reader or listener by providing the context of your story, including why you have analyzed this particular data and the problem you have solved by doing so. In your story, communicate what you did to solve the problem and how you used data to help inform the insights you’re sharing.

As part of your structure, also provide the story’s resolution — including your main findings and recommendations for how the data applies to the problem you’re solving. If possible, then offer action steps and a timeline based on your findings.

Get visual

Compelling visuals have become increasingly popular in the marketing and communications industries in the past several years. Why? Mainly, it’s because humans can process images faster than words or numbers. In fact, the brain processes images 60,000 times faster than it does text. Our brains are more accustomed to processing images — 90 percent of the information sent to the brain is visual, according to research, and data visualizations such as graphs, charts and maps help your audience quickly understand trends and patterns in your stories. (To learn more about visualizing data, read Dona M. Wong’s 2013 bookThe Wall Street Journal Guide to Information Graphics: The Dos and Don’ts of Presenting Data, Facts, and Figures.”)

Visualizations are important in data storytelling, but they don’t tell stories by themselves. You usually need additional insights to show audiences how data can solve business problems or motivate actions. Data visualizations deliver the strongest impact when they’re part of larger narratives that help audiences understand key insights from data and why they matter.

Keep it simple

In a world of never-ending data sets, it’s easy to muddle stories by pouring too much data into them. Great stories are often short, simple and to the point. When analyzing data for a story, ask yourself whether you can quickly explain its meaning to a colleague you run into at the company cafeteria. If you can’t, your story is probably too complicated and may confuse audiences who aren’t as data-savvy as you are.

To keep your story simple, look again at the business question you’re trying to solve with your data analysis. Make sure that every point you communicate links back clearly to how your data will help solve that problem.

Including visual cues in your story also helps highlight points you want to emphasize. For example, you might increase the font size of your most important insights or use colors to make parts of what you’re communicating stand out.

Avoid bias

When telling stories with data, be careful to remove biases. Biases can start with the data itself, so make sure the data you’re using for your story is derived from algorithms that are free of bias.

The problem of algorithmic bias is becoming a hot topic in the industry, with media outlets such as VentureBeat and TechCrunch covering how it could become a major societal issue, particularly as artificial intelligence and machine learning are used for functions such as job recruiting and loan approvals. According to management-consulting firm McKinsey & Company, tests can detect when human prejudices, such as gender biases, have inadvertently been built into machine-learning algorithms.

Once you’ve confirmed the neutrality of the data you’re using, make sure you also form and communicate your story in an unbiased way. Storytellers always share information from their own perspectives, but make sure the data supports your point of view. To help verify that your data is unbiased, subject it to critical thinking by brainstorming with a team or someone who knows the topic well.

With the right story, your audience will remember the data you’ve spent hours analyzing. And more important, they’ll use it to make informed business decisions.
 

Julie Walker

Julie Walker is a senior project manager in the Chicago office of Ketchum Global Research & Analytics, supporting a number of clients’ social media and digital-analytics needs. She specializes in developing and managing the implementation of communication and measurement projects to drive insightful and actionable recommendations for clients. Reach her at Julie.walker@ketchum.com
 

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