Data Storytelling Mistakes and How to Avoid Them

In data science storytelling you want to avoid the most common pitfalls like:

Letting the data speak for itself.
Letting someone else interpret the data insights.
Using technical or business jargon.
Ignoring the human element.
Overusing data and visualizations.

Don't Let the Data Speak for Itself

Imagine scheduling a doctor's visit to review the results of recent lab tests. Your doctor hands you a copy of the results and leads you through the document, uncovering data insights for better decision-making. Perhaps your fasting blood sugar level is 100 mg/dL; your total cholesterol is 270 mg/dL, LDL is 220, and HDL is 50; and your triglycerides are at 160 mg/dL. Your doctor says, "Well, the data speaks for itself."

Or imagine turning on the local news and having the meteorologist present a bunch of charts that show changes in temperature, humidity, and barometric pressure over the last 48 hours, along with maps of low- and high-pressure systems across the country. She wraps up by saying, "Well, the data speaks for itself."

As you can see, raw data, even when accompanied by data visualizations such as tables, charts, and maps, can be meaningless without expert interpretation of that data. When you consult an expert, you want the expert's opinion and practical advice — expert insight drawn from the data. In the same way, as a member of the data science team, you must interpret the data for your audience or at least lead the audience through the process of understanding the data and drawing reasonable conclusions of their own.

Don't Relinquish Your Responsibility to Interpret the Data

If your data science team is working in the context of a traditional corporate culture with a strong hierarchy, your team may be discouraged from telling stories or interpreting the data. In organizations like these, presenting the data and visualizations and letting management interpret the data are the politically safe options. Your team simply plays the role of impartial presenter, uncovering data insights to aid stakeholder decision-making.

The problem with this approach is that the data science team is responsible for the outcome, even if management misinterprets the data.

Although the data science team should certainly be open to different interpretations of the data, team members should interpret the data on their own and clearly communicate their findings. The team should do this by telling a story that connects the dots and extracts meaning from the data. Don't give anyone else carte blanche over interpreting your team's data and visualizations.

Use Familiar Language, Not Jargon and Acronyms

Data science is a high-tech pursuit that involves a great deal of specialized language and acronyms. This specialized language is like shorthand — it enables people in the field to communicate efficiently and effectively. Every field has its own specialized language (jargon). If you've ever read a study published in a medical journal, you probably needed a translator to define some of the terminology. However, when a doctor meets with a patient, the doctor uses more common terminology to explain the patient's diagnosis and treatment protocol.

In the same way, when you tell a story, consider your audience and tailor your data-driven narrative to speak to them in a language they understand. Don't use the same language you use with your colleagues on the data science team.

Don't Ignore the Human Element

New data science teams often struggle with the idea of creating a story from data. Some data just looks like lifeless columns of numbers, but through data analysis, every data point can tell a compelling narrative. Data visualizations are more attractive but can be equally cryptic. How do you tell a story with a chart?

It's a real challenge for data science teams to reverse engineer tables and charts to tell the story behind the data. Frankly, it’s one of the biggest challenges. One way to overcome this challenge is to humanize your reports. For example, instead of calling a report "Upcoming consumer trends," call it something like, "What people are buying." This simple solution makes it easier to think about your data in terms of real-world events and activities.

Use Data and Visualizations Sparingly

Business intelligence (BI) tools produce a dizzying array of data visualizations, making it incredibly tempting to create and use every visualization imaginable to illustrate your presentation. Avoid the temptation. Slides are great for displaying data that supports your claims, but if you or your audience becomes too focused on the data, you will all be distracted from what's most valuable — the interpretation of that data.

Count your slides. As a rule of thumb, if you have 30 slides for a 60-minute presentation, you have too many, and you're not telling a story. Keep in mind that the charts are the first things your audience will forget. To achieve maximum impact, focus on the things your audience will remember. Your audience is more likely to remember a clear, interesting story.

Like any skill, data storytelling takes time to improve. Start thinking about the key elements of a story — plot, setting, characters, conflict, and resolution. Then strive to weave those elements into a story around the data that reveals its meaning and significance and will connect with the target audience.

Over time, your stories will become more robust and interesting. You might even draw stronger conclusions and bolder interpretations. Try to remember to have fun with your stories and your audience. It will improve your stories and make you a more interesting storyteller.

Frequently Asked Questions

What are some common mistakes in data storytelling?

Common mistakes in data storytelling include using biased data. People often fail to share insights clearly. They don't give context. Data storytellers often show data without a story, which doesn't grab the audience's attention.

How can an analyst ensure effective storytelling?

As an analyst, you can tell a good story with data. Explain why the data matters. Use a story that makes the data easy to understand. Show the data with simple pictures or graphs. Make sure these graphs show the most important highlights.

Why is it important to avoid using biased data?

You should avoid using biased data because it can lead to incorrect conclusions and mislead the audience. People need unbiased and accurate data to make smart and informed decisions.

What are some ways to avoid common mistakes in data storytelling?

Some ways to avoid common mistakes include:

Double-checking data for accuracy
Ensuring clarity in the narrative
Avoiding complex information
Regularly updating the data to keep it relevant.

These practices help data analysts present effective storytelling based on the data.

How does explanatory data visualization contribute to effective storytelling?

Explanatory data visualization helps tell a story with data. It makes complex information easier to understand. Dashboards and charts can show important trends and patterns, which helps the audience get to the point easily.

Why is context important when presenting data?

You need context when you show data. Context gives background and meaning to the numbers. It helps people understand why the data matters. Without context, data can mislead people as it won't share the right message.

How important is it for a data analyst to tell a story rather than just present data?

It is very important for a data analyst to tell a story rather than just present data because storytelling goes beyond mere presentation.

A story can communicate insights more effectively, making it easier for the audience to understand the meaning of data and take action based on it.

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