Navigate the Data Storytelling Guide
- What is Data Storytelling
- Storytelling and Narrative
- Data Storytelling Examples
- The Data Storytelling Process
What is Data Storytelling
Data storytelling is the process of finding the value and insights in a data set, visualize it effectively, communicate it to your audience and extract the outcome and applicable action based on it. Often data storytelling is interpreted as the practice of building effective data visualizations. And by effective it is implied they are easy to read, understand and quick to analyse and draw insight from. The practice of data storytelling however goes beyond effective data visuals and focuses on building a story from start to finish based the data you are working with. While effective visuals are sill a crucial part in data storytelling, it is important that they visualize the most important, action oriented insights of your data.
Storytelling and Narrative
Storytelling refers to the practice of sharing stories that we think are interesting and often we put our own improvisation and theatrical behavior. I imagine the art of telling jokes when I think about real storytelling. Telling jokes requires skills to keep your listeners entertained but also to tell your funny story in a way that will result loughter.
The narrative is what builds our stories. Narratives are the series of related events and experiences that put our story together.
To be good at storytelling one needs to understand how to build the narrative and how to embilsh this narrative with visual or acting clues to engage and keep the audience interested.
All stories have a simple diagram:
Stories can take many diffirent twists and turns and there can be many different story narratives. Here is an example of a data story structure
As you decide on a visual for your data, think about the story narrative arch and what story structure works best in your case.
Data Storytelling Examples
Effective Data Visualizations
Data visualization is what makes large data sets and raw numbers easy to digest, understand and draw insights from. Data visualization refers to the use of graphs, charts and tables that visually represent our data. It is important data visuals are designed to effectly communicate our insights or we risk to loose the attention of our audience and not take the necessary actions. There are several data design concepts to consider to tell your data story.
Choosing the right chart
This may sounds easy but a lot of people still make mistakes of choosing an inapporpriate chart. For example a line chart is best used for trends over time. If a line is used to compare categories it wil extremely confusing to the audience. This step of choosing the right chart for your data is the most crucial. Look at your data set, what data is it? Then look at the chart options and decide what would be best to convey your data message.
In some cases you might consider choosing a different type of chart not because other charts don’t work but because a certain chart has components that better tell your data story.
For a while I would use a combo chart to show report on website visitors, conversions and conversion rate. And a combo chart works fine. However the same data presented in a 100% stacked area chart looks much cleaner, easier to read and righ not point to focus your audience attention (something I cover later in this resource).
There are several chart components that can contribute to clutter and unecessary distraction for your audience. These are a few things to consider:
- Remove gridlines
- Use either data labels or vertical axis
- Consider splitting your data in multiple charts
- Avoid 3D charts
- Don’t overuse bright colors
Focus audience attention
Every data set and the charts that come out of it have an insight that we want to focus on. It could be the top performing category or a peak in the trend over time. There are several design techniques we can use to focus our audience attention on the outcome and insight of our data.
- Use bold or italics text
- Use a highlight color
- Use bullet points
- Increase mark and data label size
One of the most powerful component in any design, be it data visualization, company’s logo or book cover, is color. Colors bring emotions and feelings and frankly are the first thing our audience notices. Colors in data visualizations may have numerous meanings – cultural, political, branded or with a specific color meaning. For example in the chart below I used red to signal danger since my data shows a concerning trend that needs urgent attention. If this chart is designed in blue or green or yellow for example it will have a complety different effect on the audience.
- Binary colors to devide categories:
- Sequential colors to show progression:
- Categorical colors to differentiate between multiple categories:
- Highllight color to focus audience attention:
Titles and annotations
Chart titles are the first element of the data visualization that the audience will notice. Titles should set the tone for your data story and contain takeaway insights. Chart titles should summerize the insights from the data.
Instead of labeling charts as Top 10 Cities in the USA by Population you can use New York is the top USA city with the largest number of population.
You can also add labels and annotations to emphasize additional information on your charts. Annotations are very useful on line or area charts showing increases and decreases over certain periods of time. Check out the area chart above on African elephant population.
The Data Storytelling Process
Data stories are pretty much everywhere around us. There is data in everything we interact on a daily basis. The weather changes, our mood, the most popular meal at our favourite restaurant, the most popular product at Sephora, so on and so forth. It is always important to remember that data is useful because it provides insights and these insights can help make assumptions, predictions, figure out what comes next and make well informed decisions. That is how data becomes key to any success, especially in business and news.
There are a few approaches that can help find a story in a tangled net of data. They work in almost any situation, whether you are a business analyst, a marketin analyst or a journalist.
- Focus on your audience. Forget about your fears of doing a presentation for a moment and think of your story as if you are telling it over lunch. Why is this story important? What does it tell us about doing things differently? What would you focus on and what details would you leave out? Knowing your audience is great but
- What is the most significant point in your data? Remember that stories become newsworthy with their impact, anomalies, novelty and trends. Focus on these significant events in your data, point them out in your visualizations and focus your audience attention on them
- What is next? What are the action items and key takeaways? Remember that the sole purpose of data stories is to answer the question “What is next”? You don’t necessarily have to put all the insighs into the story but structure it in a way and choose visualizations wisely to imply on what the key insights are.
Deciding what data to show or summarize has to be guided by the audience and by the problem that you are trying to solve. Data is here to educate us about our communities, the world and the business we are tasked to drive to success. Good data stories can help us predict what is next and how we can prepare for it.
Check out my short data story videos