Design Thinking for Better Data Storytelling

A brief introduction to Data Storytelling, Design Thinking, and how together they can help you tell better data stories.

What is Data Storytelling?

Data Storytelling is the process of using your data analysis findings to create a compelling story, with the goal of helping your audience make sense of the data and act upon it. This is often done with help of a series of linked data visualizations.

Data stories are almost everywhere, and not just in the numerous BI dashboards that you come across at work. They are in your fitness tracker app, in the Google search result for ‘weather’, and even in the battery settings of your phone.

These data stories empower you to easily understand datasets, which might be difficult to interpret otherwise, and then make informed decisions based on it.

Data Storytelling, if done right, can be incredibly powerful!

Not all data stories make sense!

Sadly though, not all data stories are very well designed and hence do not make much sense to the audience.

Let’s look at one such example.

This is the Co-Win dashboard, which displays statistics of the COVID vaccination program in India. There are many design flaws in this dashboard.

• The metrics such as ‘Total Registrations’ and ‘Total Vaccinations’ don’t tell you much apart from simply stating the numbers.
• The ‘Vaccination Trends’ might be useful to a few, but even this visualization has a design flaw. The default view displays the hourly trend from today! It’s difficult to imagine why anyone would find that useful. Yes, you can switch to a view with the last 30 days’ trend, but that’s another click.
• Somewhere on the dashboard, there is a visualization for ‘Session Trends’, but there is no information on what it signifies.
• Nowhere on the entire dashboard can you see the percentage of population vaccinated to date.

As a user, when I see this dashboard, I don’t see a story. I don’t get an overview of how well the vaccination program is going on in India. All I see is raw data converted to visualizations to make it look more appealing. Visualization for the sake of visualization.

Although this dashboard is available to the public, it doesn’t look like it was designed for them. In fact, the only people who might find this dashboard useful could be the government officials managing the vaccination program.

More often than not, such blunders happen when the creators do not keep the users at the center of their solution design process. Even though they put in a lot of effort, they end up creating a product or solution that is of little use to the user.

This is where Design Thinking can help!

But first, what is Design Thinking?

Let me begin with what Design Thinking is not. It is not something that is just for designers or creative professionals. It is for everyone!

Design Thinking is a human-centered approach to creative problem-solving. It is a process that helps you adopt a designer’s mindset and approach the problem from a user’s perspective.

There are five stages in the Design Thinking process.

• Empathize: The first stage is all about gathering information about your users. Ask a lot of questions. Who are the users? What is important to them? What questions they might want to answer with data? Get into their shoes and think from their perspective.
• Define: In this stage, take all the information you gathered in the ‘Empathize’ stage and use it to define what the users’ problems are and what they need.
• Ideate: This one’s my personal favorite. This is the stage where you brainstorm ideas and come up with solutions based on what you’ve learned in the process so far. It is completely okay (and even encouraged) to have more than one possible solution at this stage.
• Prototype: In the fourth stage, you take your ideas and convert them into prototypes that can be tested with the users. These prototypes don’t have to be fully functional. Just enough functionality that can be quickly built and tested.
• Test: In the final stage you take your prototype to a few selected users, get them to use it, and take their feedback. From my experience, it is also very useful to just observe (with their consent, of course) how a user interacts with the prototype.

The great thing about the Design Thinking process is that it is not necessarily linear. In practice, it can be flexible and iterative.

• If in the Ideate stage you realize there are questions about the users that you don’t have answers to, you can go back to the Empathize stage and collect additional information.
• If you learn more about the users in the test stage, you can go back to the Define stage and redefine the problem statement.
• If the insights from the Test stage help you come up with ideas on how to build a better solution or product, you can go back to the Ideate stage.

This process allows you to create innovative products or solutions that are not only technically feasible and economically viable, but also desirable for the user.

Why use Design Thinking for Data Storytelling?

Now, why do I think that Design Thinking can help you tell better data stories?

Here’s why.

• Engaging the audience is one of the key aspects of Data Storytelling. It is easier to create engaging data stories if you truly empathize with your audience and understand what questions they want to be answered.
• Data Storytelling is a compelling way to answer questions asked by the users. However, the kind of questions that users ask, can at times be limited by their lack of expertise with data. As an expert, if you have a great understanding of the users’ ways of working and their priorities, then you are uniquely positioned to identify and answer some of the ‘unasked’ questions.
• Data visualizations can at times be difficult to interpret for many users. Rapid prototyping and testing with the users can help create visualizations that are easy to understand. If more users understand the insights presented to them, the insight-to-impact conversion rate would be higher. And isn’t that the goal of Data Storytelling?

Hope you all loved this article. If you have any doubts or queries, write them up in the comments below. Also, do applaud this article. Thanks for your time. 🙏

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