The Importance of Storytelling with Data – Alchemer

Survey Tips
February 6, 2018

It’s a big trend: storytelling with data. But what is it? How do you do it? Is it really the best way to communicate data insights to your organization? This post will answer those questions and leave you with tips to start using in your storytelling with data efforts.

When storytelling with data think of how your favorite book is written.

Books, movies, even fairy tales have similar traits: characters, story flow, and a lesson to be learned. By using the same approach when communicating data findings, otherwise known as storytelling with data, the challenge of sharing data becomes less so. Not only will your organization know what the data is trying to say, but the method will not be as difficult to you as you may think.  

Take HR for example, for those who have access to real-time employee engagement feedback data can find common threads to form a close-knit story.

By wrapping those data findings, so to speak, inside of a story, color and context  the color and context the method provides conveys the core message of the data in a way that tells your audience what to do in a straightforward way.  

“To tell a story, you have to be able to aggregate and assimilate a lot of different types of data,” says Jeffrey Belanger, Lead HR Business Partner and Head of Organizational Performance at Pandora. “To come up with key themes, you want to architect the narrative the way you would when you are writing an article or book. Put an outline together and think of the process into a cohesive flow. Then have all of the correlating data and examples to fill in the details.”

Professionals struggle to communicate data findings to their organization. To lessen the struggle, know what decisions you’re looking to make with data first.

In a recent study, we found that 52 percent of 500 professionals surveyed say that data plays a large role in their long-term business strategy, but 48 percent say they are only sometimes able to communicate data findings to key stakeholders.  

The challenge is in large part due to not knowing how to properly visualize data. Before digging into the basics of this, a simple tip is to ask yourself what kind of decision is going to be made from the data first.

The look and feel of the visual will be determined by what decision is going to be made from it.

“Yet even today, many dashboards and analytic projects fail to make a business impact because they overlook this simple requirement,” says Donald Farmer, Principal at TreeHive Strategy. In order to make data actionable, consider focusing on understanding decisions.

Here is a basic overview of the different kinds of decisions that can be used in your business, according to Farmer:

Operational Decisions

This decision type is narrowly scoped, have a clear structure and are very repeatable. The same inputs will typically lead to the same outputs. Each operational decision may be small, but there are many in the course of your business. Examples of operational decisions include: How do I respond to this error condition? Which supplier should I use for this order? Do we make this promotion? Do we offer this customer a load?

Tactical Decisions

This decision type is broadly scoped and the results of operational decisions can be influenced by tactical decisions. Examples of tactical decisions include: What is our target time for dealing with error conditions? Who are our preferred suppliers? What is our goal for issuing loans this quarter?

Strategic Decisions

This decision type is falls into the realm of most senior leaders of your business. The input to strategic decisions can be very complex, ranging from detailed understanding of the market to the to “gut feeling” of a leadership team. Examples include: Is it time to move to a new system? Do we build components ourselves or do we purchase from suppliers? Should we offer business loans or instead invest in domestic mortgages?

Like traditional stories, the best storytelling with data should  have a beginning, middle, and end.

Beginning: Set the stage for your audience and give context by providing background information as it relates to the story. Make it personal and bring in anecdotes from your own life to trigger immediate connection and relatability.

Middle: Hook the audience with insights, share what you’ve discovered through your analysis, and explain in layman terms what it means for the audience and why they should care.

End: Great endings provide audiences with a new understanding through the transformation of the story’s characters. Data story endings should not only capture that traditional a-ha moment for your audience but inspire them to take action.

Once you have this basic storytelling structure down, begin to map data insights to a story arc, also known as the story’s shape or framework.Doing this will best structure the data around the storytelling method most people are familiar with and learn from.

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