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192 Part II • Descriptive Analytics

Dashboard Design
Dashboards are not a new concept. Their roots can be traced at least to the EIS of the
1980s. Today, dashboards are ubiquitous. For example, a few years back, Forrester
Research estimated that over 40 percent of the largest 2,000 companies in the world
use the technology (Ante and McGregor, 2006). Since then, one can safely assume
that this number has gone up quite significantly. In fact, nowadays it would be rather
unusual to see a large company using a BI system that does not employ some sort of
performance dashboards. The Dashboard Spy Web site (dashboardspy.com/about)
provides further evidence of their ubiquity. The site contains descriptions and screen-
shots of thousands of BI dashboards, scorecards, and BI interfaces used by businesses
of all sizes and industries, nonprofits, and government agencies.
According to Eckerson (2006), a well-known expert on BI in general and dash-
boards in particular, the most distinctive feature of a dashboard is its three layers of
information:

1. Monitoring. Graphical, abstracted data to monitor key performance metrics.
2. Analysis. Summarized dimensional data to analyze the root cause of problems.
3. Management. Detailed operational data that identify what actions to take to
resolve a problem.

Because of these layers, dashboards pack a lot of information into a single
screen. According to Few (2005), “The fundamental challenge of dashboard design
is to display all the required information on a single screen, clearly and without
distraction, in a manner that can be assimilated quickly.” To speed assimilation of
the numbers, the numbers need to be placed in context. This can be done by com-
paring the numbers of interest to other baseline or target numbers, by indicating
whether the numbers are good or bad, by denoting whether a trend is better or worse,
and by using specialized display widgets or components to set the comparative and
evaluative context.
Some of the common comparisons that are typically made in business
intelligence systems include comparisons against past values, forecasted values,
targeted values, benchmark or average values, multiple instances of the same measure,
and the values of other measures (e.g., revenues versus costs). In Figure 4.8, the
various KPIs are set in context by comparing them with targeted values, the revenue
figure is set in context by comparing it with marketing costs, and the figures for
the various stages of the sales pipeline are set in context by comparing one stage
with another.
Even with comparative measures, it is important to specifically point out
whether a particular number is good or bad and whether it is trending in the right
direction. Without these sorts of evaluative designations, it can be time-consuming
to determine the status of a particular number or result. Typically, either specialized
visual objects (e.g., traffic lights) or visual attributes (e.g., color coding) are used
to set the evaluative context. Again, for the dashboard in Figure 4.8, color coding
(or varying gray tones) is used with the gauges to designate whether the KPI is good
or bad, and green up arrows are used with the various stages of the sales pipeline to
indicate whether the results for those stages are trending up or down and whether
up or down is good or bad. Although not used in this particular example, additional
colors—red and orange, for instance—could be used to represent other states on
the various gauges. An interesting and informative dashboard-driven reporting
solution built specifically for a very large telecommunication company is featured in
Application Case 4.6.

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