Introduction

Data storytelling is a hot topic in the data world these days. As an analyst, you should know about data storytelling due to its increasing demand and the impact it has on the audience. It’s not a technical or necessary skill but it can position you as an all-rounder in your field because it helps in building connections with people.

If you don’t know anything about data storytelling and wondering what exactly it is and why we even need it then this article is for you. I’ll walk you through what data storytelling is to how to tell a story with data.

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What is data Storytelling?

As the name suggests it is telling a story with the help of data.

Data storytelling is the idea of building a narrative, presenting a story and painting a picture with the help of the data that we, as an analyst deal with on regular basis. Instead of just presenting the numbers it is the art of making a story out of it.

It includes using various visualizations which are combined with analyses and some solid narratives. This helps the audience in understanding the whole picture better rather than just listening to some stats. Data storytelling has way more impact on the listeners than just the data.

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Purpose and benefits of telling a story with data

The simple purpose of data storytelling is to build a narrative. As an analyst when you’re presenting your analyses with storytelling you’ll get more attention from your audience and you’ll be able to get your point across better. Simple stats and analyses won’t impact the listener as much as the narrative and the story attached to it will.

Many businesses have started including data storytelling in analyst job descriptions, while others have chosen to fill data storyteller jobs to augment the skills of their current analytics teams. Being able to use the right visualization and present the right story can help you stand out as an all-rounder applicant.

Data storytelling also helps in the explanation of data to individuals with various learning preferences and enables you to tailor your communication style to the target audience.

For instance, a visual learner would need more data visualizations and other visual aids, whereas an auditory learner could respond better to a spoken lecture. A successful data story will often use various elements to keep a wide audience’s interest.

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4 steps to tell a story with data

Here is the 4 step process to find and tell a story with data. Follow them in order and you’ll end up with a great data story to present to your stakeholders.

Let’s move to the first step, assuming you have the data and you’ve done your analyses.

1.    Understand the audience

The most crucial step of any presentation is to know who you’re presenting to. The same goes for data storytelling. Understand your audience, know who they’re. Find out what matters to them the most, what their goals are, what they currently know, and what decisions they need to make.

Your analyses and data stories will help them make decisions for their business. Your job is to provide them with all the knowledge they need in terms of data to make the right decision. And you can only do this when you know their goals and priorities.

When creating and presenting your data stories, keep your audience in mind. Your intended audience must find the story you want to tell interesting and relevant for it to have the impact you desire. The understanding and reaction to your stories will vary depending on your audience’s age, demographics, occupation, and level of subject matter expertise.

For example, an audience of executives will most likely have a wider variety of professional experience and will be searching for data that has been distilled into a few key points. Adapt your tale and take different approaches based on the people you intend to share your analytics with.

2.    Find a story in the data

After knowing your audience, it’s time to find a perfect data story. Discovering a story worth telling in the data is the first step in crafting an effective data story. You might start by formulating a query or a hypothesis according to your data. Let’s see this in detail a little bit.

Start by seeing the data that you’ve, what is it about? Is it about marketing, customers, or sales? Your story is also going to be related to it. Once you know this analyze your data and analyses to find a pattern in it. It might be a problem, solution, success or downfall. Know these 2 things and then you can easily create a story related to the business or organization you’re working with.

You can do this for various data sets in the same field like web analytics or sales and then combine those patterns to create a bigger picture for multiple metrics.

Also, consider any data that behaves strangely or beyond the norm are considered outliers. Discover that and enquire as to why. Utilizing discrete values while the data is continuous; handling missing, outlier, and out-of-range values; arbitrary temporal ranges; and capping values, volumes, ranges, and intervals are all examples of this selectivity. Once they realize it, viewers will stop believing in the visualization.

3.    Refine your story

Once you have a complete idea of what your data story is going to be it’s time to refine it and put it in a proper format. This is necessary, why? Because even if your story is worth listening people can get bored if you haven’t presented it the right way.

So work on the format. You should linearly tell your tale. Start at the beginning and work your way toward the conclusion. Don’t begin with your findings; they should come later in the story. Keep your audience interested and save that for the conclusion’s climax.

4.    Visualize your story

A strong data story also requires graphics. Mainly when dealing with audiences that are not technically savvy, visuals are an important tool for capturing attention and increasing retention. If you’re unsure which visualization best depicts your data, you may use certain AI tools to search for suggestions.

These tools will employ AI to suggest the most appropriate visualizations for your query. Additionally, be careful not only to embellish the material but to attempt to explain it. Avoid falling into the “that looks good” trap since the facts may not be best explained in this way.

Dos and Don’ts of telling a data story

• Make sure the information you are utilizing is accurate and thorough.
• Always cite your sources, preferably with links, so that readers may click through for further information.
• Do spare some time to give your audience some serious thought.
• Don’t make it difficult for the audience to become worn out by your terminology or the pictures.
• If they aid the reader’s connection to the information, include photos of well-known items.
• Don’t forget to give colour, font, format, and location due consideration.
• Do consider data communication to be an ongoing dialogue.

Frequently asked questions

1.    How can I ensure that my data narrative is precise and relevant?

No matter the subject, creating to-the-point and concise data stories is crucial. You should frequently edit and revise the story as you would do with other writings and presentations until it’s crystal clear. You can also take your fellow analyst’s opinions on your story and narrative. Constructive criticism will help you refine the story and make it the best version of it.  

2.    What essential elements of the data story do I need to concentrate on?

A data story must have three key components: data, narratives, and visuals. You may understand the data completely by doing descriptive, diagnostic, and prescriptive analyses. Next, specify the appropriate storylines and actions you want your audience to take. Lastly, use eye-catching images and visualizations.

3.    How to maintain interest in your info among the audience?

Instead of handing your team a data spreadsheet and a list of figures, think about how you may activate different brain regions. You may make your arguments more memorable and actionable by using data storytelling to bring out an emotional reaction on a brain level.

In a nutshell

Your whole data set may be made or broken by the quality of your data narrative. It can assist in putting data findings into practice. For insights to be fully utilized by your audience, it’s important for them to fully understand it and data storytelling helps you do that.

Without good communication, insights may go missed or forgotten. Here is a quick review of how to find a story in data and make a perfect data story.

1. Understand the audience
2. Find a story in the data
3. Refine the story
4. Visualize your story

Following these steps may produce a remarkable story that will convince the audience of your point. All you need is some reliable information, a compelling character, and an effective tool for visualizing your components and conclusions.

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