Five Key Steps to Telling Stories with Data

Admit it, as soon as you saw this image of an inkblot, you tried to figure out what it was. I’ve heard “vertebrae, two mice, a carriage with two footmen, and a pelvis.”

Each person sees something different. That’s because our brain flips through a library of files of our knowledge and experiences as it tries to make sense of what it is seeing.

It considers, “Do we know what this is? Is it related to something we know? Is it brand new?”

Each person can discern a different answer because our knowledge and experiences are unique. This can lead to assumptions, confusion, and miscommunication.

Whenever you put up a chart of data, the brain takes the same approach to detect and understand. If the presenter doesn’t take you through the story of the data, each person can come away with different interpretations.

A Simple Example:

In 2019, I guest lectured for the Data Mine at Purdue University where students could get a certificate in data analytics regardless of their major. They were required to attend and write a summary of four “outside event” events. These were hour-long sessions with guest speakers like me.

With one month left in the semester, 25 people had completed all four papers. Almost 80% hadn’t completed any papers or had only submitted one.

Why do you think the submissions were so low?

When I share this chart with audiences during keynotes and ask their perspective, I’ve heard:

• “Students didn’t know about the requirement.”
• “They started them but haven’t finished them.”
• “Procrastinating!”

Each person responds based on their experiences and assumptions of college students and deadlines. This chart seems straightforward and clear. But when I dug further with the Data Mine coordinators, we realized there was more to the story:

• The university had record enrollments and students were balancing heavy class schedules and workloads.
• Students were selective about the summaries they chose to write.
• Some students didn’t read their syllabus and weren’t aware of the requirement.
• The timing of the outside events competed with other classes and work and students couldn’t attend.

While procrastination plays a role, it’s easy to make inaccurate assumptions when you don’t have the story behind the data. With context, coordinators were able to recognize the need for different communications to ensure future students understand the requirement. They also were able to schedule events at various times to create flexibility with class and work schedules.

Data Doesn't Speak for Itself

This simple chart is an example of what happens every time someone shares data. We assume it’s easy to follow and will make sense to everyone. But each person can have a different understanding, resulting in endless debates.

By telling a story, you bring everyone to the same common point to start a discussion. Even if people disagree, you are discussing the same data. Neglect the story and it’s like sharing the inkblot above. One person may be talking about mice and another could be talking about vertebrae. You aren’t discussing the same things and don’t even realize it.

Storytelling with data is a rich topic, one you could spend a whole semester studying. I find there are five common mistakes that can easily be fixed.

1. Don’t let the tail wag the dog.

Most people collect as much data as a ream of paper and try to figure out how to present it. This is backward and confusing. Decide why you are collecting the data before you start collecting it. It's like the scientific hypothesis. You form it before the experiment, not after you have gathered results.

What questions are you looking to answer? What problem statement are you looking to solve? Are you monitoring information? Will the data inform a decision or help define a new market or strategy? Know why you are gathering data and how you will use it before you collect it.?

2. Start with the questions, not the data.

With data in hand, go back to the questions you want to answer. Too often, people start digging into the data and lose why they collected it in the first place. Start with your questions and notice any outliers or patterns. These help you identify and inform the story.

3. Tell the story of the data.

If you don’t guide your audience through the story of the data, you risk everyone having a different interpretation, just like with ink blots. We make assumptions based on our knowledge and experience, which differs from person to person.

Share why you are collecting it, what you expected to see, what you learned, and what was unexpected. You may even choose to tell a parallel story that has nothing to do with the data but reinforces the same takeaway. If you have data about poor customer experience, telling a parallel story about poor customer experience in a different context can reinforce what people pay attention to and lower defensiveness.?

4. The person closest to the data bears responsibility for guiding others through it.

No one is as close to the data as you are. This gives you responsibility for creating meaning for others. Guide people through the story of the data and share a recommendation. This could include sharing questions to prompt discussion. Or it might be recommending taking a decision or continuing to monitor. Help your audience make data-informed and not data-driven decisions.?

5. Visuals come last.

Data visualization is incredibly important. To some people, this is where you begin telling stories with data. It’s not, it comes at the end. Most people make the mistake of generating a bunch of cool-looking visuals about the data, but they don’t have a cohesive storyline and it introduces confusion. Once you define the story, you can determine what images best support that.?

We have a few spots left for storytelling workshops this fall. If your team or organization is looking to?become more memorable and effective communicators, let's start a conversation.

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