Telling Compelling Stories With Data

Raw data is powerful, but it needs a good narrative!

The word data is thrown around a lot, and nowadays there’s no part of a business that doesn’t use it.. The rate that companies gather data today is bigger than anybody could have imagined a couple of decades ago. But having a big collection of data alone is not enough. It is what, how, and why you use your data that really can make a difference.

With the ever growing list of Business Intelligence (BI) tools, data is becoming increasingly accessible and easy to gather, and dashboards and spreadsheets are quicker to build. So isn’t it funny that companies are still struggling to harness the power of their data.

What they lack is a key storytelling component. Dashboards can only ever tell you what is happening. They can rarely tell you how, or why, or what this means for a business. As awesome as its power is, data alone will never be enough.

In a time of information overload - with screens on our desks, walls, and even our bodies - storytelling to cut through the noise is more important than ever. That’s where data visualization comes in.

Here, we’re going over:

  • What is data visualization? Why tell a story with data?
  • How to create a compelling data Story
  • Using narrative as a tool
  • …with some real life examples to tie it all together 

What is Data Visualization?

Before we dive into the storytelling part, it is good that we have a base understanding about some key principles in data visualization.

 Our eyes are naturally drawn to colors and patterns. It helps us identify and focus on key points. Simply put, images speak louder than words. Data visualization helps tell a story by curating information into an understandable form, highlighting hidden information and guiding the audience through a path that leads to a conclusion, or to discover something that was not so obvious from raw data.

The key elements that are played together to help data be more clear are: Size, Color, Order and Scale.

Size can help emphasize information and add context to the user. Size is easier to be adjusted to the values and has a more direct connection to the information itself.

Colors are another element that can help the viewer understand the context and make associations between the elements. It is especially good to categorize your different labels or to emphasize the intensity.

Order should help reduce cognitive overload. When the data is pretty close, ordering, for example from largest to smallest, will help you automatically see the most important points.

Scale is important to not give misleading information. We tend to associate the magnitude of the data by its scale, so keep that in mind when building your visuals.

One thing to keep in mind is that your visual will be useless if only you can understand it. You need to have a good picture in mind of who you are designing the visual for, and keep your audience in mind when creating it. If your viewer is not so familiar with the data, or it's their first time seeing it, be more instructive and give directions to guide the user experience.

Besides this product mindset, there are 4 commandments that every visual must follow. They are:

4 commandments of data visualization

Up down, left right speaks to the order that information should be presented in your graph. All of Western culture reads and writes in a very specific way, that is, starting from the top left corner and going from that direction downwards. Keeping this in mind when building a dashboard can be useful to guide you selecting which visuals are going to be present first, and then prepare your story to in the end your audience reach the right conclusion.

data visualization guidance

Color correctness is so important…and so easy to mess up. Our brain is so powerful and it is built in a way that we are looking for patterns and associations all the time. Using this in your favor can make your visual easier to read and coherent. Failing in color correctness can lead to misleading analysis. A good example can be making all data related to monetary features green. Or dividing your labels into different colors and keeping this consistency throughout your different visuals.

data visualization colors

Filtering is specific to interactive visuals (e.g dashboards). This can be a powerful tool to present more data without necessarily having it displayed all at once. However, be very careful where you put your filter, how well you signal it, and where the data is being filtered.

data dashboard

Granularity is the amount of detail in your visual. Is good to start with low granularity and end with high granularity. Something that is very usual to happen is when you reach the end of your presentation and some colleague asks you for the Excel spreadsheet of the data. Some people tend to need to see a table to believe what you’re saying, so it is a good idea to have this high granularity element at the end of your visual.

There are 2 main forms of delivering a data story. It can be explorative or narrative. Explorative forms are delivered usually in Powerpoints, PDFs or Dashboards. Narrative forms can be delivered in presentations, talks, meetings and in Dashboards as well. Today we will focus more on the narrative type of data storytelling, but keep in mind that exploring your data is necessary and you might need to do it first before building your story.

Telling A Story With Data

A good data story is composed of fiable data, well-designed visuals, and compelling narrative. The data aspect is straightforward, we must have accurate data to reach trustable insights. The visual elements help us visualize (duh!) the data better, finding trends and insights that are not easily seen in the rows and columns of a common spreadsheet. The narrative part comes into play to give voice to the data. Looking at raw data to get information or to prove a point is just the hardest thing to do. We use data visualization because we want to help the viewer understand the message that we want to pass on.

Each data point can give a message, and the combination of them creates a story that voices the insight that you are looking for. Data storytelling is an attractive way to communicate the story that you found inside your data. It also improves the credibility of your data, as you look to find connections.  You can have a brilliant idea or message that you want to transmit, but if you don’t know how to do it in an effective manner, your audience will counter argument you or create other narratives to explain to you the things that you have found.

“People need a narrative, and if there isn’t one on offer, they make one up.”  Jean Hanff Korelitz

You don’t need to be the best storyteller that there is. It is a question of finding the right elements, understanding the simple “Pixar” formula and remembering the most important concept: “Show, don’t tell."

Storytelling is the art of delivering, developing and adapting creative stories utilizing specific elements: characters, context, conflict and message. What we will do now is try to translate this into the data world:

The characters are your data points, your features or your KPI’s. They are the ones that go into the journey.

The context is your business setting. This is your start point and should be present at the beginning, as in “Once upon a time…”

The conflict is the reason that you are doing the story. No story is good without a conflict, people must be presented with a challenge to make the story exciting. In data this is the problem that you are trying to solve, or give insight on.

The message is your insight in itself, or the key takeaway you are trying to make your audience understand. At the end of the day, your audience may forget you, or the title of the presentation, but they must remember the message.

Define the objective of your story since the beginning. Do you want to uncover a threat to your company, reveal an interesting insight or just tell a funny story?

Are you enjoying this article? Keep learning about Data Analytics!

Take the first step into tech and find out more about our Data Analytics bootcamp

Creating a Narrative

As said before, a complete narrative contains characters, obstacles, conflict and a well defined journey of transformation for the protagonist. Think about your favorite movie and certainly you will find it. Now translating this into a data story is tricky because things may not seem so obvious at first, but you can think of not the data being the story, but the story being the structure that your data will fit in. Following the “Pixar” model you will find the sections in your journey.

Presentation: “Once upon a time…”. Insert your audience into the context. Their minds are not yet fully prepared to receive all the information, so start slowly introducing the different features that you are going to work with. For example, if you are doing a Sales presentation and your company has different segments -  Consumer, Corporate and Home Office - start by showing them to put this in their mind as you advance in your story.

Conflict: After that, it is not good to keep the viewer waiting for some action, or people might start to sleep. Present your conflict, or the point of interest that motivates you to follow the direction that you are going. Because of this conflict you will need to show a bunch of different other features that along the way will help you solve or give an insight about this. For example, if your Sales team is struggling recently in profiting, this is the struggle that you want to show.

Journey: Here is the part that you will need to work around the different elements that you have at your disposal. Focus on compelling visuals and comparisons that will show the different facets of your conflict and give basis for the next steps.


The change: We are presented with the takeaway, the pinpoint data point that demonstrates the reason for the conflict or the tool that can help solve it. Use that moment to deliver your message and convince people that the journey that you took is justified. You can show for example that the state of Tennessee has had a significant increase in costs and selling there isn't the best option right now.

A second challenge: It is common that we are faced with another challenge that we will have to deal with. This happens because nothing in real life is so simple that with only a change on a lever will solve your problems. Ok, we can stop promoting sales in Tennessee, but maybe we will lose future customers there. Be ready for these counter arguments and use past elements to deal with these other conflicts.

The conclusion: Returning to your usual setting, and at some times, presenting recommendations for the future, as in a movie that ends with a cliffhanger.  This is very interchangeable and so it's difficult to put it in a formula. Changes from story to story and must be taught as the moment that you put it all together and end on a high note.

How Real Companies Use Data Storytelling

Spotify “Wrapped” is a great example of a compelling data story that has the unique element that every company looks for: is personal. It was in 2006 the first time they presented this feature and it's been a sensation since the beginning. Being a data enthusiast myself I can tell that it was one of the things that motivated me in subscribing to the paid version. It shows you how powerful big data can be, and at the same time how simple you can be with it.

Spotify data storytelling with Wrapped

In the past it came in your email and now it is present as “stories” inside of your app, which makes it easier to post on other social media apps. They put together interesting statistics for each user such as the number of minutes they’ve listened to music, their favorite artists, podcasts and many other data points. This is very engaging because the direct reaction that most people have is sharing this with their friends, promoting the brand in an organic way.

Another cool example is Microsoft's “Anatomy of a breach”. This data story guides readers though a data heist to show how prevalent breaches are. Viewers are encouraged to explore the data to draw their own conclusions, but the provider has full control of the flow that the user is taking so in the end you are taking the journey that they’ve built for you.

LinkedIn's “Data and Insights” page is trying to promote data into the job seeking world. By presenting stats like “Most in-demand job” and “Most confident markets” Linkedin builds a story with their own data, gathered in their website and, as it has nowadays the status of a mainstream tool for job seeking, you can expect that the data is somewhat reliable.

Turn Data Into Beautiful Things

As Plato said 2400 years ago: “Those who tell stories rule society”.

Want to meet others like you who love data, and want to use it to make beautiful things? You need to join the Ironhack family!

Our Data Analytics bootcamps empower you with all the knowledge you need to launch your Data career. Learn part time or full time, live online or on campus. We've also got financing options to make investing in your career as easy as possible.

Join Ironhack

Ready to join?

+10,000 career changers and entrepreneurs launched their careers in the tech industry with Ironhack's bootcamps. Take a step forward and join the tech revolution!

Courses

What would you like to learn?

Location

Where would you like to study?

Related blog posts about Data Analytics

How Much You Can Earn in London As a Data Analyst

Read more...

How to Improve Your Python Skills

Read more...

Data Analytics Is Changing The World - Here’s Why You Should Care

Read more...

The 5 Most In-Demand Machine Learning Languages in 2022

Read more...
Ironhack Data Analytics

How to get into data science

Read more...
image with code for data and tech

Differences between data analytics and data mining

Read more...
Stay up to date on our latest news and events. Sign up now!
Please type your name
Type your last name
The email is not valid. Please try again

By applying I accept the Privacy Policy and the Terms of Use