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Designing Executive Dashboards: Part 1
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This white paper outlines the issues faced by BI operational staff in maintaining high quality of
BI information, and discusses technologies that have the potential to dramatically raise the
reliability and quality of BI information, improve how BI teams use their time and resources
to deliver rapid ROI and free resources to focus on answering business questions based on
reliable and meaningful data.
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Introduction
Corporate dashboards are becoming the “must have” business intelligence technology for executives and business users across corporate America. Dashboard solutions have been around for over a decade, but have recently seen a resurgence in popularity due to the advance of enabling business intelligence and integration technologies.
Designing an effective business dashboard is more challenging than it might appear due to the fact you are compressing large amounts of business information into a small visual area. Every dashboard component must effectively balance its share of screen real estate with the importance of the information it is imparting to the viewer.
This article will discuss how to create an effective operational dashboard and some of the associated design best practices.
Dashboard Design Goals
Dashboards can take many formats, from glorified reports to highly strategic business scorecards. This article refers to operational or tactical dashboards employed by business users in performing their daily work; these dashboards may directly support higher-level strategic objectives or be tied to a very specific business function. The goal of an operational dashboard is to provide business users with relevant and actionable information that empowers them to make effective decisions in a more efficient manner than they could without a dashboard. In this context, “relevant” means information that is directly tied to the user’s role and level within the organization. For instance, it would be inappropriate to provide the CFO with detailed metrics about Web site traffic but appropriate to present usage costs as they relate to bandwidth consumption. “Actionable” information refers to data that will alert the user as to when and what type of action needs to be taken in order to meet operational or strategic targets. Effective dashboards require an extremely efficient design that takes into account the role a user plays within the organization and the specific tasks and responsibilities that user performs on a daily/weekly basis.
Defining Key Performance Indicators
The first step in designing a dashboard is to understand what key performance indicators (KPI) users are responsible for and which KPIs they wish to manage through their dashboard solution. A KPI can be defined as a measure (real or abstract) that indicates relative performance in relationship to a target goal. For instance, we might have a KPI that measures a specific number, such as daily Internet sales with a target goal of $10,000. In another instance we might have a more abstract KPI that measures “financial health” as a composite of several other KPIs, such as outstanding receivables, available credit and earnings before tax and depreciation. Within this scenario the higher-level “financial” KPI would be a composite of three disparate measures and their relative performance to specific targets. Defining the correct KPIs specific to the intended user is one of the most important design steps, as it sets the foundation and context for the information that will be subsequently visualized within the dashboard.
Defining Supporting analytics
In addition to defining your KPIs, it is helpful to identify the information a user will want to see in order to diagnose the condition of a given KPI. We refer to this non-KPI information as “supporting analytics” as it provides context and diagnostic information for end users in helping to understand why a KPI is in a given state. Often times, these supporting analytics take the form of more traditional data visualization representations such as charts, graphs, tables and, with more advanced data visualization packages, animated what-if or predictive analysis scenarios.
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