The Data Fix: Demarq’s complete guide to storytelling with data

As a data expert, you’re responsible for uncovering insights that are incredibly valuable to the wider business and your external customers. Stakeholders, however, often quantify this value through the way you present the findings.

The more digestible and engaging your data ‘story’, the more your audience will value it. When data is coupled with narrative, it explains to your audience what’s happening and why a particular insight is important.

Getting data storytelling right can be tricky when you’re more comfortable interrogating and wrangling data than presenting it, but it shouldn’t be an afterthought. Our complete guide to storytelling with data breaks the process down into digestible chunks, so you know exactly what data storytelling is, and why it’s so important.

Now’s the time to make your data story more compelling.
 

1. What is data storytelling?

 
Today, almost all businesses collect multiple sources of data, and many use this data to draw high-level business insights. It’s why the role of the data analyst is now in high demand.

The way these insights are presented, however, is absolutely key to a data analyst’s business recognition, as well as getting buy-in from the wider business. That’s why data storytelling is a core skill data analysts need.

Data storytelling is a communication method for presenting your information to stakeholders. It should be tailored to your specific audience, and be accompanied by a compelling narrative. It is arguably the most important aspect of your data analyses.

“Numbers have an important story to tell. They rely on you to give them a clear and convincing voice.”
Stephen Few, author and data visualisation expert

What are the three key elements to data storytelling?

 

  1. Data science
  2. Fundamentally, your data need to be scientific and accurate. It also needs to do more than deliver figures; it should contain trends, insights and analyses.

  3. Visualisation
  4. Data in isolation is visually unappealing. Transforming it into graphs, charts and graphics allows us to see data like never before. Data visualisation alone, however, has limitations. It requires context to explain why and how conclusions are drawn.

  5. Narrative
  6. The most essential part of data storytelling is the commentary that accompanies it—the story itself. It is a key vehicle to convey insights, with visualisations and data being important proof points.

When you combine the right imagery and narrative with insightful data, you have a data story that can influence strategic decisions and drive business change.

Read more about the value of data visualisation in our blog: data presentation—is it time to go back to basics?
 

2. Why is storytelling with data essential in modern business?

 
Today, data is money—the more strategic information an organisation has available, the more informed its business decisions, and the better the outcomes.

For some data analysts, crafting a story around this data may sound unnecessary. Data speaks for itself, doesn’t it?

You or your data analysts might think insights should stand on their own, as long as they’re presented clearly. You may believe that insights alone should influence the right decisions and drive your audience to a conclusion.

“The goal is to turn data into information, and information into insight.”
Carly Fiorina, Former CEO of Hewlett-Packard

This point of view, however, is based on the assumption that business decisions are based solely on logic. Often, however, strategic business decisions are equally influenced by emotion. Stories and context have the power to help business leaders understand information and, as a consequence, can shape high-level decisions.
 

How does data storytelling help analysts?

 
As data sources become bigger and more complex, and we access a wider library of information across mobile, social and cloud platforms, being able to tell a compelling data ‘story’ becomes more important than ever.

Have you ever struggled with the perception of your role as a data analyst to the rest of the business? This might not be a result of technical misunderstanding, it could be a communication roadblock.

Taking time to communicate your data findings in a tangible way for your audience—helping them understand the value of what you do—will make all the difference to this negative perception.

Find out the importance of communication by data analysts in our blog: Building trust and credibility — how do your analysts communicate?

 

3. The art of the data narrative

 
Your data holds vast amounts of value, but this value is hidden until you translate it into business recommendations, suggested actions or strategic outcomes. Storyboarding allows you to effectively plan these outcomes.
 

What is data storyboarding?

 
Storyboarding is one of the most important plans you can create early on when deciding the direction of your data insights. It is like a flowchart; it maps out the direction and flow that your data insights will follow, from start to conclusion.

“The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades.”
Dr. Hal R. Varian, Google’s Chief Economist, 2009

When you’re writing your data storyboard, it’s important to consider what you want your insights to say—what is your overall goal?
 

What’s your data presentation objective?

 

Think of a traditional story; what is your setting, your plot, who are your characters, your ‘big reveal’ and your themes?

  1. Setting — where (which platform / department / website?) and how often is it happening?
  2. Plot — what are the most important pieces of information and stats?
  3. Characters — who is this impacting? Internal, external?
  4. Twist/reveal — what business problem can be changed / improved?
  5. Takeaway — what are the opportunities going forward?

How can you engage your audience?

 

The best way to ensure your audience is engaged throughout your data presentation is to consider their various points of view. Consider:

If you can tailor your data narrative to your audience’s specific objectives, you stand a higher chance of fully engaging them.

Find out how to plan an effective data story in our blog: What is storyboarding? Planning a data narrative.
 

4. How to make data more visual

 
Data, in its more raw form, is a collection of number and stats. But how do you decide which aspects of the data matter most, which are worthy of visualisation, and why?

Data storytelling, however, is not simply data visualisation, although the visual element is very important. Firstly, not all visuals are story-telling, some simply set the scene—these visualisation often assume a certain level of knowledge. When visuals are applied to data, they should enlighten your audience.

Adding visual impact to your data will increase your audience’s willing to read and understand your insights.

“Humans are pattern-seeking, story-telling animals. We are quite adept at telling stories about patterns, whether they exist or not.”
Publisher, Michael Shermer

Today, there are countless tools available for data analysts. They all offer data visualisation and data storytelling capabilities to make the connections between data points more visible to the wider organisation.

Ideally, you want to make your life as presenter easy; requiring additional explanations on your data as you go through will only add complexity and stress to your presentations. Today, data visualisation has a tendency to become style over substance.
 

Visualise data without overcomplicating the information

 
Sometimes, creating a more intricate graphic is less time consuming than producing a simple one. Not to mention, it feels more intuitive to include as much information as possible. You should, however, only include information if it adds something new to the story you are telling.

Some tips for simplifying your data:

  1. Strip your data presentation down to simple, digestible graphics
  2. Use minimal colours and distractions
  3. Ensure you highlight data proportionately and accurately
  4. Draw your audience’s attention to the most important results

Overall, data storytelling needn’t be complex nor time consuming, but it is important to boost the profile of your role as data analyst.

Read more about the value of data visualisation in our blog: data presentation—is it time to go back to basics?
 

5. Consider your ‘eureka’ moment

 

Before you present your data, or even begin your storyboard, consider what your ultimate conclusion will be. What are the biggest takeaways from your insights for your specific audience?

As a data analyst, it’s essential to be continually mindful of the high-level goals or business problems and why you’re tasked with solving it. Address these problems and goals in your final conclusion.

“Most of us need to listen to the music to understand how beautiful it is. But often that’s how we present statistics: we just show the notes, we don’t play the music.”
Hans Rosling

What data questions will your audience have?

 
Once you’re ready to present your insights, you’ll be capable of determining how much information to share. Don’t fall into the trap of revealing all your information without any hierarchy, and include one or two top level ‘big reveals’. To do this, decide what the most important questions your audience will ask is.

Then, you must ensure your data sufficiently addresses your business’ ‘question’. If there’s any key information missing, you may want to rethink your question, revise your methodology, or collect more data.
 

What is the final answer?

 
The ‘eureka’ moment of your presentation is the point at which your audience realises the value of the insights you’re presenting. The best way to maximise the impact of this moment is to personalise this revelation to your audience’s trigger points.

So, ask yourself, what piece information or conclusion is most likely to excite your audience?

This final conclusion should consist of concise but eloquent recommendations that will lead your stakeholders to make proper decisions or interpretations from your data insights.
 

6. Practical tips for your data storytelling

 
When planning your data presentation, you should think about the following elements:

Your target group
Different forms of presentation may be needed for different audiences—for example, subject specialists or the wider business

The role of graphics
It is essential to keep your charts simple. Graphics should deliver a message with minimal mental investment from your audience. In the majority of cases, bar graphs and line charts are best; they are most easily recognisable and have strong preattentive attributes. Avoid data tables unless they have less than 6 numbers, or they form a more visual heat map.

Annotating text
Although your graphs and charts communicate the visual message, the associated text lines are also important. Ensure any key elements are clearly called out, and that your titles have appropriate weight to ensure the user is drawn to any conclusion or insight the slide is giving.

Colour choices
The choice of colour in a chart or graphic may depend on the data and on the type of visualisation your making. If you’ve kept your chart design simple, consider using different shades of the same colour rather than different colours to enhance your audience’s understanding. You should also be aware of any existing conventions associated with any given colour, as well as possible positive or negative connotations.

Aspect ratios
When you’re creating a data visual, always make sure your ratios are presented accurately. A graph that shows objects in disproportionate sizes can be misleading.

How will you present it?
Will your presentation be a detailed analysis or a quick slideshow? Consider whether graphs, textual analysis or a data table would be a better solution.

“Facts don’t persuade, feelings do. And stories are the best way to get at those feelings.”
Philosopher, Tom Asacker

Accessibility
Colour scheme is important but don’t rely on it alone. If you remove it, is the data still understandable? Do colour combinations have sufficient contrast? Do your colours work for the colourblind? Bear this in mind when choosing your graphics.

Consistency
Ensure that separate elements within your data visualisations are presented consistently. Use common elements where possible— for example, always use the same colour to represent a repetitive data element.

Complexity
Is your presentation easy to understand? Is it too much for the audience to grasp at a given session? Consider testing your presentations on other colleagues or members of your target group to see if they get the intended messages.

Read more about the value of data visualisation in our blog: data presentation—is it time to go back to basics?