Science teaches us that the human brain can get a sense of a visual scene in less than 1/10th of a second. Ninety percent of information transmitted to the brain is visual. And most impressive – visuals are processed 60,000X faster in the brain than text.
All of this helps us better understand the value and power of data visualizations when interpreting company performance and insights. Data visualizations and infographics are your best choice for helping business users quickly and accurately understand their performance drivers. They immediately pinpoint the user’s attention to what requires action or further analysis.
But do they really? Well they do if you create your visualizations correctly and respect some basic guidelines.
In my first post on the Art of Visualization, I addressed some best practices. Now it is time to explore a little deeper. In this blog, I will highlight some of my personal lessons learned from customer discussions. The focus is on visualizations and infographics. Visualizations are created to get visual insight in a certain metric, key performance indication (KPI) or information aspect. Infographics then use these visualizations and combine them into an overview that provides the overall insight.
Time series and animation
Times series are best done using a line chart. Here are some key learnings:
- directly connect values in adjacent intervals; if data is missing, indicate it is missing
- synchronize the X-axis when you have various metrics and use combination charts
- improve the readability of the visualization, using subtle grey grid lines
- don’t overlap bars and ensure some space between contiguous bars when using bar charts or a combined bar/line chart
- use soft colors and be careful with tick mark values
- when comparing metrics in a time series line chart, work with variances against a base line
Below examples show the two ways of visualization – the first one with baseline I recommend and the second one I don’t.
If you want to highlight when specific data exceptions occurred over time, you might want to use animated graphs. I use these sometimes when creating bubble charts to indicate exactly when a correlation in data is off expectations.
Bullet charts are highly useful when you have a key measure, a comparative measure (goal/target), and a qualitative measure. A typical example is the insight in actuals versus forecast and budget. Bullet charts serve the visualization of KPIs very well.
The heat map is very useful if you are looking for white-spot analyses. White-spot analyses are used to immediately recognize areas not covered very well in a certain domains, for example, sales numbers per product and per country. The heat map immediately indicates what products in what countries are underperforming and are white spotted. Close to heat maps are the tree maps that you can best use in cases where large numbers of values exceed the number that can effectively be shown in a bar chart. Even better, tree maps offer “heat indicators” if you use color coding of the data points smartly.
The Internet is full of open source D3 charts; here is one example. Tools like SAP Lumira are capable of using them. The D3 charts can look stunning, but be aware, quite often they serve one very specific purpose or objective. Though this can be perfect for that purpose, users tend to interact with data and change visualization types on the fly. If you do find the ultimate D3 for your objective, ensure to lock it to end users so they can’t change the chart/graph type. If you use SAP Lumira, check out this link where the best D3 visualizations are selected already.
Avoid the “Christmas tree”
Especially in our domain of data visualizations, the paradigm of “less is more” rules. Yes, I know today’s self-service tools allow to fill your infographic with bells and whistles, but please keep in mind the consumer of your infographic. They will quickly get confused and lose focus on what you really want to tell.
Filters and cloud tags
For my infographics with filters where users tend to make large multiple selections, I always add a linked tag – cloud – to the filter so people can easily see what they have selected. I use this technique in the freely available tool I built for choosing the right BI component, where users may select multiple functional requirements to a BI tool of choice.
My post “Let me interact” addressed how your meetings will never be the same, since people constantly interact with their visualizations and infographics. They want to change filtering, drill-down of granularity on the fly. They want to rank, highlight, or zoom in on data during the meeting’s discussions. They want to exclude or include certain data sources that might come from outside.
In some cases, where your infographics need to become closer to dashboards and cover multiple different info areas, you probably use multiple tabs or pages in your final results. In these cases, for navigation purposes, I always advise to create a landing page as your page/tab one. You use this page to create URL links to the specific areas, and you can use the landing page to share specific info (assumptions, scope, etc.). You can also use it if you want the user to set filters that apply the full dashboard; this saves space on the individual pages/tabs and your users need to set the filter only once.
If you have multiple visualizations on an infographic, ensure they are interrelated at least on the level of one page or tab. Now when people drill or zoom on one visualization, it applies to all other visualizations which guarantees consistency in insights.
Branding and color coding
Unfortunately I am a guy that is part of the community that doesn’t have “the feeling” for choosing the correct colors. I often find it difficult to look for appropriate colors not only in the graph, but also for backgrounds, symbols, or accompanying pictures. The trick is in using Adobe Color. With this tool (also available on mobile devices) you can take a picture of a preferred scenery with numerous focus points, and it automatically finds good accompanying colors even providing the RGB codes. Very useful!
Read more on data visualization and analytics on my blog.
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