IN THIS ARTICLE
- What Is Data Storytelling?
- Why Is Data Storytelling Important?
- Essential Parts of a Data Story
- Data Storytelling: Tips, Tricks, and Things To Remember
- Data Storytelling Tools
- Data Storytelling Examples
- Data Storytelling Resources
- Making a Career in Data Storytelling
- Data Storytelling FAQs
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In the past few years, data has become an increasingly valuable resource for making business decisions. But cold, hard data doesn’t always offer valuable insights right away. Data needs to be corralled into a cohesive narrative, and that’s where data storytelling comes into the picture.
Data storytelling is an underrated part of the larger data insights process. While most data professionals understand the importance of various data analysis techniques, not enough attention is paid to creating a compelling narrative from their findings. In this article, we’ll find out why effective data storytelling is so important and how you can learn to do it well.
What Is Data Storytelling?
Data storytelling is the process of narrativizing data analysis so that its insights can be understood by a wide audience. While not everyone can quickly comprehend data, most can follow a visual story that summarizes findings from the analytics process.
Why Is Data Storytelling Important?
Let’s take a look at an example to see why data storytelling is important. Here are some earth surface temperature data from a Kaggle dataset.
This is what your average dataset looks like—several rows and columns with various data points. Analyzing this dataset can produce valuable insights, but it won’t change what the output looks like. To the average viewer, it will remain a collection of rows and columns that don’t make a lot of sense.
And that’s why data-driven storytelling is so important. It transforms complex data into universally comprehensible stories with a solid narrative.
Essential Parts of a Data Story
Here are the components that contribute to an effective data story:
You can’t have a data story if you don’t have any data. So the bedrock of the data storytelling process is clean data that can be analyzed to produce actionable business insights.
The places from where you source your data will depend on the nature of the project that you’re working on. For example, if you’re trying to use data analytics to make better fantasy football picks, then you should use the Football Database. There are various sites that offer data sets that you can analyze for fun.
Now that you have your dataset, it’s time to do some data analysis. That means that you connect your data to a specific business problem, clean the data so that there are no erroneous or missing entries, and carry out your analysis.
At the end of this process, you will find that you’ve produced insights that weren’t available when you started with a raw dataset. If you work in the professional world, this will contribute to the business decision-making process. But in order to convince your organization about your findings, you need something more than just those insights.
One doesn’t usually associate a narrative with the data process, but the two are more intertwined than you might first think. Here are a few elements that contribute to a good story, data or otherwise:
A setting is the foundation of any strong narrative, and data storytelling is no different. In this specific case, that means that you explain the context of your project, its business significance, and any specific problems that it is looking to address.
Imagine that you’ve been given data from various marketing campaigns to determine why there is a lag in the sales of a specific product during a certain time of the year. The setting is the problem statement as it has been outlined, the product, and the nature of the marketing campaigns (marketing channels, nature of creatives, ad spends, etc.).
You can’t have a story without characters. The same goes for the story that you’re trying to tell with data. The characters in this particular case are the subjects who contribute to your data and the various stakeholders who care about this particular data project.
Let’s return to the example of the marketing campaign. Our characters are going to be the target demographic of those campaigns. The list of characters might also include members of the marketing team who contribute to the creation of those campaigns.
Stories are driven by conflict. Every project that you will work on is driven by some kind of conflict, usually one that involves a pressing business problem. To further the example we’ve been using, the conflict could be defined as ineffective marketing campaigns. In another case, it might be the need to explore new markets or enhance employee productivity.
Describing the conflict helps your audience gain a clear understanding of the goals of the project. It tells them exactly what you’re trying to achieve and what kind of impediments might be in your way.
The resolution is your answer to the conflict that you’ve established in the previous step. This resolution must, of course, be backed by the data analysis that you’ve already conducted.
Remember that your resolution needs to tie in with all of the other elements of your narrative. It must work in the setting that you’ve established, be relevant to your characters, and be appropriate to the conflict that you’re dealing with.
Returning to our example, we see that certain marketing campaigns aren’t working as well as a company wants them to. Your data might reveal that the problem is that the specific marketing channels that have been chosen don’t cater to the target audience. In that case, your resolution will be to select different marketing channels based on their ability to reach that demographic.
Visuals are an essential aspect of data storytelling, as they convey large amounts of information quickly and keep readers engaged. Here are a few things you should keep in mind when coming up with your visual elements:
The kind of data visualizations you choose in each case will depend on the kind of data you’re working with. For example, column charts are a good choice when you want to compare numerical values, whereas a histogram works better when you want to track a specific data trend over time.
Colors in Moderation
There is a tendency to make visualizations colorful in the hopes of this leading to a more attractive appearance. But using too many colors can confuse readers and distract them from the broader story. So make sure that you use a few colors and choose neutral tones as much as possible.
The whole idea of using visuals is to make it easy for viewers to quickly glean the information they need. You can contribute to that goal by using familiar layouts and formatting throughout your story. For example, if you have multiple graphs in your story, use the same formatting and colors across them so that they’re easy to parse quickly.
Data Storytelling: Tips, Tricks, and Things To Remember
Just like the stories you watch on TV or read in books, a data narrative needs to have a structure. Where do you want your audience to begin? Where does the story end? How will you get them there? Here are a few veteran moves you can use to put all those things together in your data story:
Include the Right Details
Don’t overload your audience with information right off the bat. By keeping things succinct, you can guide users toward meaningful conclusions. For example, this table provides too many details about each sales opportunity, but the bar chart shows the metric the department truly cares about—open opportunities—in a more visual manner.
Provide the Right Context
Everything you include in your data story should relate to your organization’s larger goals, and your dashboard or data visualization should highlight the correlations between each metric and these KPIs. The chart below shows actual website traffic alongside the target numbers to provide important context. This is an example of how you can contextualize your data in light of larger organizational goals.
Provide Actionable Information
Your audience should know why a specific chart or graph is important without any guesswork. Additionally, it should be easy for them to see how the data relates to their role in the organization.
Let’s look at an example based on the visualization below. Viewers can quickly figure out that the chart displays educational assessments across different subjects at Eisenhower Elementary. It’s also easy to get an idea of the comparative performance between subjects just by looking at this visualization. That makes it a lot easier to take actionable steps based on the data.
Data Storytelling Tools
There are various data visualization tools that are used by data storytelling professionals. Let’s take a look at the most popular ones out there.
If you’ve ever gone about researching data visualization tools, then you’ve no doubt heard of Tableau, a powerful tool that can integrate data from a myriad of sources. It’s also a relatively intuitive software to learn.
Data storytelling is a domain that’s not limited to just designers anymore, and that’s thanks to tools like Infogram. This is a piece of software with a simple drag-and-drop interface that you can use to build dashboards, infographics, and more. An added advantage is that you can use Infogram to produce interactive visualizations, which is a great way to create a more engaging experience for your audience.
Datawrapper is a visualization tool built specifically for news stories. You can easily copy and paste your data into the tool to create tables and maps that enrich news pieces. These visualizations can easily be embedded on news sites with an embed code and can also be exported as a PNG, PDF, or SVG.
Data Storytelling Examples
Here are a few examples that you can use as inspiration to create your own data stories.
We Feel Fine is a large-scale experiment in visualizing human emotions. How does it do that? Well, the tool trawls blogs on the Internet every 10 minutes and conducts a sentiment analysis on them to figure out the dominant emotions of each post. The results visualizations are a visually rich, data-driven exploration of human emotions in real-time.
Given its position as one of the most widely used music streaming services out there, Spotify has vast amounts of data at its disposal. The company used some of that data to produce the Fan Study data visualization. This is a website where musical artists can get a data-driven insight into what Spotify has been able to gather about listeners’ preferences and tastes. The site shows artists how they can expand their reach, make a splash with new releases, and improve merch sales.
Data Storytelling Resources
Here are a few resources that can help you learn more about data storytelling:
- Data Storytelling and Data Visualization – Udemy
- Data Storytelling for Business – StoryIQ
- Data Science Bootcamp – Springboard
- Storytelling and Persuading using Data and Digital Technologies – edX
- Storytelling with Data
- Effective Data Storytelling – Lights On Data
- Crafting Stories with Data – Google Career Certificates
Books and Websites
- DataStory – Explain Data and Inspire Action Through Story
- Effective Data Storytelling
- Information is Beautiful
- Junk Charts
Experts to Follow on Social Media
Get To Know Other Data Science Students
Making a Career in Data Storytelling
A career in data storytelling can be exciting if you’re passionate about making data-driven insights comprehensible to a large audience. Here’s how you can go about making a career in this field.
What Does a Data Storyteller Do?
A data storyteller’s job lies at the intersection of data, business, and design. These are all disciplines that you must have some familiarity with if you want to work in this area.
It’s essential that data storytellers understand the foundational aspects of the business context of their organization. This will help you better grasp the problems that the data team is trying to solve and why certain resolutions have been proposed.
You do, of course, need to have a strong understanding of how data is employed in business decision-making, as your work will entail interpreting results from the data analysis process and deciding which of those results needs to be highlighted for a specific audience.
Finally, you need to know what constitutes good design. This doesn’t mean that you need to have a degree in design or be able to illustrate digital mosaics from scratch. Rather, data storytellers have a strong grasp of the basics: clean layouts, intuitive formatting, and concise textual elements.
Job Roles and Titles To Explore
There are a few different titles you’ll come across when looking for data storytelling jobs. Here’s what each of them entails.
This is a job role that’s equal parts data and storytelling. You’ll spend your time producing key insights based on the requirements of the projects and visualizing them for a non-technical audience.
Data visualizers work specifically on transforming insights from the data analysis process into visuals. This job is less heavy on data-specific work and requires a strong understanding of tools like Tableau and Adobe Photoshop.
Some data analyst jobs require skills in data storytelling. While your job will primarily involve data analysis, you will also need to be able to work with designers to produce presentations for different stakeholders.
These skills are all prerequisites to landing a job in data storytelling:
- Data concepts: A fundamental understanding of the value of data in business decision-making and the analysis process.
- Business and strategy: Understanding the market context in which an organization functions and its business goals.
- Design: Knowledge of how humans process visual information and how to effectively communicate with design.
- Creativity: The ability to come up with innovative ways to communicate data insights to an audience.
Data Storytelling FAQs
We’ve got the answers to your most frequently asked questions.
How Do You Present Data Stories?
Data stories can be presented in various formats depending on the nature of the projects. In some cases, they’re embedded in websites or apps. In others, they might be presented in the form of a PDF, PNG, or other image formats.
What Skills Are Needed for Data Storytelling?
Data storytellers need to have skills in design and a basic understanding of data analysis. They should also grasp the business context within which they’re working.
Which Industries Have a High Demand for Data Storytelling?
Data storytelling has gained prominence across many industries and has become an especially important skill in journalism and media.
Are There Lucrative Jobs Based on Data Storytelling Expertise?
Yes. Top earners can earn over $150,000 per year.