alt="tip"/> Conventional wisdom says that the measures on your dashboard shouldn’t be governed by the availability of data. Instead, you should let dashboard KPIs and measures govern the data sources in your organization. Although I agree with the spirit of that statement, I’ve been involved in too many dashboard projects that have fallen apart because of lack of data. Real-world experience has taught me the difference between the ideal and the ordeal.
If your organizational strategy requires that you collect and measure data that is nonexistent or not available, press Pause on the dashboard project and turn your attention to creating a data collection mechanism that will get the data you need.
In the context of reporting, a dimension is a data category used to organize business data. Examples of dimensions are Region, Market, Branch, Manager, or Employee. When you define a dimension in the user requirements stage of development, you’re determining how the measures should be grouped or distributed. For example, if your dashboard should report data by employee, you need to ensure that your data collection and aggregation processes include employee detail. As you can imagine, adding a new dimension after the dashboard is built can get complicated, especially when your processes require many aggregations across multiple data sources. The bottom line is that locking down the dimensions for a dashboard early in the process definitely saves you headaches.
Along those same lines, you want to get a clear sense of the types of filters that are required. In the context of dashboards, filters are mechanisms that allow you to narrow the scope of the data to a single dimension. For example, you can filter on Year, Employee, or Region. Again, if you don’t account for a particular filter while building your dashboarding process, you’ll likely be forced into an unpleasant redesign of both your data collection processes and your dashboard.
If you’re confused by the difference between dimensions and filters, think about a simple Excel table. A dimension is like a column of data (such as a column containing employee names) in an Excel table. A filter, then, is the mechanism that allows you to narrow your table to show only the data for a particular employee. For example, if you apply Excel’s AutoFilter to the Employee column, you are building a filter mechanism into your table.
Many dashboards provide drill-down features that allow users to “drill” into the details of a specific measure. You want to get a clear understanding of the types of drill-downs your users have in mind.
To most users, drill-down feature means the ability to get a raw data table supporting the measures shown on the dashboard. Although getting raw data isn’t always practical or possible, discussing these requests will, at minimum, allow you to talk to your users about additional reporting, links to other data sources, and other solutions that may help them get the data they need.
A refresh schedule refers to the schedule by which a dashboard is updated to show the latest information available. Because you’re the one responsible for building and maintaining the dashboard, you should have a say in the refresh schedules – your manager may not know what it takes to refresh the dashboard in question.
While you’re determining the refresh schedule, keep in mind the refresh rates of the different data sources whose measures you need to get. You can’t refresh your dashboard any faster than your data sources. Also, negotiate enough development time to build macros that aid in automation of redundant and time-consuming refresh tasks.
A Quick Look at Dashboard Design Principles
When collecting user requirements for your dashboarding project, there’s a heavy focus on the data aspects of the dashboard: the types of data needed, the dimensions of data required, the data sources to be used, and so on. This is a good thing – without solid data processes, your dashboards won’t be effective or maintainable. That being said, here’s another aspect to your dashboarding project that calls for the same fervor in preparation: the design aspect.
Excel users live in a world of numbers and tables, not visualization and design. Your typical Excel analysts have no background in visual design and are often left to rely on their own visual instincts to design their dashboards. As a result, most Excel-based dashboards have little thought given to effective visual design, often resulting in overly cluttered and ineffective user interfaces.
The good news is that dashboarding has been around for such a long time that there’s a vast knowledge base of prescribed visualization and dashboard design principles. Many of these principles seem like common sense; even so, these are concepts that Excel users don’t often find themselves thinking about. Because this chapter is about getting into the dashboard state of mind, I break that trend and review a few dashboard design principles that improve the look and feel of your Excel dashboards.
Dashboard design expert Stephen Few has the mantra, “Simplify, simplify, simplify.” The basic idea is that dashboards cluttered with too many measures or too much eye candy can dilute the significant information you’re trying to present. How many times has someone told you that your reports look “busy”? In essence, this complaint means that too much is going on in the page or screen, making it hard to see the actual data.
Here are a few actions you can take to ensure simpler and more effective dashboard designs.
Admit it. You include as much information in a report as possible, primarily to avoid being asked for additional information. We all do it. But in the dashboard state of mind, you have to fight the urge to force every piece of data available onto your dashboards.
Overwhelming users with too much data can cause them to lose sight of the primary goal of the dashboard and focus on inconsequential data. The measures used on a dashboard should support the initial purpose of that dashboard. Avoid the urge to fill white space for the sake of symmetry and appearances. Don’t include nice-to-know data just because the data is available. If the data doesn’t support the core purpose of the dashboard, leave it out.
The key to communicating effectively with your dashboards is to present your data as simply as possible. There’s no need to wrap it in eye candy to make it more interesting. It’s okay to have a dashboard with little to no color or formatting. You’ll find that the lack of fancy formatting only serves to call attention to the actual data. Focus on the data and not the shiny happy graphics. Here are a few guidelines:
✔ Avoid using colors or background fills to partition your dashboards. Colors, in general, should be used sparingly, reserved for providing information about key data points. For example, assigning the colors red, yellow, and green to measures traditionally indicates performance level. Adding these colors to other sections of your dashboard only serves to distract your audience.
✔ De-emphasize borders, backgrounds, and other elements that define dashboard areas. Try to use the natural white space between components to partition the dashboard. If borders are necessary, format them to hues lighter than the ones you’ve used for your data. Light grays are typically ideal for borders. The idea is to indicate sections
