“Don’t Make Me Think”
Those familiar with user experience design in the web application development context, might have heard the phrase “don’t make me think” coined by Steve Krug. This framework calls for usability and simplicity to be at the forefront of web design, even when developing the most complex of systems. Think about the last time you submitted a form online, perhaps while signing up for a new account or ordering a new pair of shoes. You most likely did not have to go read a manual to understand how to use the website - it was obvious even with minimal instructions.
The same concepts apply when building your business intelligence solutions. Most tools today allow us to create visualizations very quickly and iteratively, however there is still some design thinking that must come into play in order to build effective, actionable, and adoptable data visualizations.
Know Your Audience
At a high-level, effective data visualization, also known as card building in Domo, starts with understanding the audience for the dashboard(s) on which the visualization will live and the business questions they are looking to answer.
Customers often start dashboard design by identifying a function or department they want to onboard onto their new BI tool. The general goal might be something like “understanding how effective our marketing efforts are.” If we take this one step further, we’ll learn that one dashboard might not be an effective way to achieve this business goal. The executive audience for this goal is interested gauging the overall effectiveness of marketing efforts in the larger company strategy but the marketing managers would be better served by a more tactical dashboard in which they can make decisions on specific campaigns.
Understanding the audience and the story each unique audience is interested in will help guide the level of granularity needed in the visualization.
Choose Chart Types Wisely
As you start to understand your audience and the questions they are looking to answer, start crafting card designs that will answer those questions as a story. Your data visualizations will typically fall into 2 categories: actionable insight & context card. The actionable cards are the ones that tell you how much progress you’ve made towards a goal this week, how many opportunities you need to close to meet sales quota this month, which customers need some outreach this week, etc. The context cards supplement those cards, showing the impact of various influencers or dimensions on key metrics, such as a view over a larger period of time, a map of where the business operates, etc.
Use the right chart type to tell the right story. To name a few examples:
Line charts are good for showing trends over time and continuity. Don’t use a line chart if your x-axis represents something other than a date or time period.
Bar charts are good for showing comparisons between different categories. Use a stacked or grouped bar to show additional comparison between related metrics or another dimension.
Donut and Pie charts are good for showing composition or part-to-whole comparisons.
Line + Bar charts are good for showing trends over time plus the influencing metrics. For example, use the line to represent an occupancy rate overtime and the bars to show occupied & available units.
Map cards are good for showing geographic distributions.
A Few Design Tactics
If you’re a new card builder, it is tempting to build quickly. Do so, but don’t forget the low-hanging fruit that will make your visualizations and the overall dashboard much more consumable. Here are a few tactics to get you started:
1. The bar charts are a very popular type of chart, and in many cases justifiably so. Think about colors in the context of the dashboard, not just the current card. You might find that you initially have a string of blue bar charts across the same dashboard. While this might tell the necessary story, use color to help the user quickly identify each card. For example, maybe you make your cost-related card red, your profit-related card green, and the application count blue.
2. Design for adoption. If it’s not easy to understand, it will not be used. Again, “don’t make me think” to understand the point of the visualization. The visualization should be designed to help me ask questions to improve & make decisions on my business. If this card ended up in an online article or newspaper, would the average readership understand what it’s trying to say? Are there labels, hover text, or descriptions that could help make it more consumable? Are the numbers formatting correctly as currency, percentages, etc.?
Iterate but Keep It Simple
Once you’ve drafted a card, you can and should continue to iterate over it based on feedback from the business users. As feedback comes in, think before incorporating it into the card, so as to not over complicate the card. Would the new ask work better as a separate card or as a drill path? Will adding an additional metrics make the visualization confusing or will it help provide needed context?
Hopefully, this guide has helped you understand card building a little bit better, but if you need any assistance, please feel free to reach out to the Big Squid team.