The 5 Tips to Tell Better Stories with Data
By: James Le
Tags: Storytelling, Data Stories, Creativity, Data Visualization, Visualization

Data visualization — and communicating with data in general — sits at the intersection of science and art. There is certainly some science to it: best practices and guidelines to follow. But there is also an artistic component. This is one of the reasons this area is so much fun. It is inherently diverse. Different people will approach things in varying ways and come up with distinct solutions to the same data visualization challenge. Recently, I finished reading Cole Nussbaumer Knaflic’s Storytelling with Data — a superbly written, masterful display of rare art in the business world. This book is a straightforward, accessible guide that will help anyone who communicates with data connect more effectively with their audience. Below are the top 5 tips she shared in the last chapter that I found to be a great recap of the book.

Tip #1 — Learn your tools well

Try not to let your tools be a limiting factor when it comes to communicating effectively with data. Pick one and get to know it as best with the basics may be helpful. You don’t need fancy tools in order to visualize data well. There are a plethora of tools out there. The following is a very quick rundown of some of the popular ones currently used for creating data visualizations:

Google spreadsheets are free, online, and sharable, allowing multiple people to edit.

are free, online, and sharable, allowing multiple people to edit. Tableau is a popular out-of-the-box data visualization solution that can be great for exploratory analysis because it allows you to quickly create multiple views and nice-looking graphs form your data. It can be leveraged for the explanatory via the Story Points feature. It is expensive, though a free Tableau Public option is available if uploading your data to a public server isn’t an issue.

is a popular out-of-the-box data visualization solution that can be great for exploratory analysis because it allows you to quickly create multiple views and nice-looking graphs form your data. It can be leveraged for the explanatory via the Story Points feature. It is expensive, though a free Tableau Public option is available if uploading your data to a public server isn’t an issue. Programming languages — like R, D3, and Python — have a steeper learning curve but allow for greater flexibility, since you can control the specific elements of the graphs you create and make those specifications repeatable through code.

and — have a steeper learning curve but allow for greater flexibility, since you can control the specific elements of the graphs you create and make those specifications repeatable through code. Some people use Adobe Illustrator, either one or together with graphs created in an application like Excel or via a programming language, for easier manipulation of graph elements and a professional look and feel.

Tip #2 — Iterate and seek feedback

It takes iterating to get from early ideas to a final solution. When the best course for visualizing a certain data is unclear, start with a blank piece of paper. This enables you to brainstorm without the constraints of your tools or what you know how to do in your tools. Sketch out potential views to see them side-by-side and determine what will work best for getting your message across to your audience.

At any point, if the best path is unclear, seek feedback. The fresh set of eyes that a friend or colleague can bring to the data visualization endeavor is invaluable. Show someone else your visual and have them talk you through their thought process: what they pay attention to, what observations they make, what questions they have, and any ideas they may have for better getting your point across. These insights will let you know if the visual you’ve created is on the mark or, in the case when it isn’t, give you an idea of where to make changes and focus continued iteration.

When it comes to iterating, there is one thing you need perhaps more than anything else in order to be successful: time.

Tip #3 — Devote time to storytelling with data

It takes time to build a robust understanding of the context, time to understand what motivates the audience, time to craft the 3-minute story and form the Big Idea. It takes time to look at the data in different ways and determine how to best show it. It takes time to declutter and draw attention and iterate and seek feedback and iterate some more to create an effective visual. It takes time to pull it all together into a story and form a cohesive and captivating narrative.

It takes even more time to do well the most important step: communicating your work, as that is the only part of the entire process that your audience actually sees. Expect it to take longer than you think to allow sufficient time to iterate and get it right.

Tip #4 — Seek inspiration through good examples

Imitation really is the best form of flattery. If you see a data visualization or example of storytelling with data that you like, consider how you might adapt the approach of your own use. Pause to reflect on what makes it effective. Make a copy of it and create a visual library that you can add to over time and refer to for inspiration. Emulate the good examples and approaches that you see.

Said more provocatively — imitation is a good thing. We learn by emulating experts. That’s why you see people with their sketchpads and easels at art museums — they are interpreting great works. There are a number of great blogs and resources on the topic of data visualization and communicating with data that contain many good examples. I personally would suggest you start with the Data Visualization Society.

Tip #5 — Have fun and find your style

When most people think about data, one of the furthest things from their mind is creativity. But within data visualization, there is absolutely space for creativity to play a role. Data can be made to be breathtakingly beautiful. Don’t be afraid to try new approaches and play a little. You’ll continue to learn what works and what doesn’t over time.

To the extent that it makes sense given the task at hand, don’t be afraid to let your own style develop and creativity comes through when you communicate with data. Company brand can also play a role in developing a data visualization style; consider your company’s brand and whether there are opportunities to fold that into how you visualize and communicate with data. Just make sure that your approach and stylistic elements are making the information easier — not more difficult — for your audience to consume.

The book is written for anyone who needs to communicate something to someone using data. This includes: analysts sharing the results of their work, students visualizing thesis data, managers needing to communicate in a data-driven way, philanthropists proving their impact, and leaders informing their board. I would highly recommend this book for anyone who wants to improve their ability to communicate effectively with data. This is an intimidating space for many, but it does not need to be.