Typical Dashboard Data
Dashboards are useful for all kinds of work. Whether you're a meteorologist monitoring the weather, an intelligence analyst monitoring potential terrorist chatter, a CEO monitoring the health and opportunities of a multi-billion dollar corporation, or a financial analyst monitoring the stock market, a well-designed dashboard could serve you well.
2.2.1. The Common Thread in Dashboard Diversity
Despite these diverse applications, in almost all cases dashboards primarily display quantitative measures of what's currently going on. This type of data is common across almost all dashboards because they are used to monitor the critical information needed to do a job or meet one or more particular objectives, and most (but not all, as we'll see later) of the information that does this best is quantitative.
The following table lists several measures of "what's currently going on" that are typical in business.
|
Category |
Measures |
|---|---|
|
Sales |
Bookings Billings Sales pipeline (anticipated sales) Number of orders Order amounts Selling prices |
|
Marketing |
Market share Campaign success Customer demographics |
|
Finance |
Revenues Expenses Profits |
|
Technical Support |
Number of support calls Resolved cases Customer satisfaction Call durations |
|
Fulfillment |
Number of days to ship Backlog Inventory levels |
|
Manufacturing |
Number of units manufactured Manufacturing times Number of defects |
|
Human Resources |
Employee satisfaction Employee turnover Count of open positions Count of late performance reviews |
|
Information Technology |
Network downtime System usage Fixed application bugs |
|
Web Services |
Number of visitors Number of page hits Visit durations |
These measures are often expressed in summary form, most often as totals, slightly less often as averages (such as average selling price), occasionally as measures of distribution (such as a standard deviation), and rarer still as measures of correlation (such as a linear correlation coefficient). Summary expressions of quantitative data are particularly useful in dashboards, where it is necessary to monitor an array of business phenomena at a glance. Obviously, the limited real estate of a single screen requires concise communication.
2.2.1.1. Variations in timing
Measures of what's currently going on can be expressed in a variety of timeframes. A few typical examples include:
- This year to date
- This week to date
- This quarter to date
- Yesterday
- This month to date
- Today so far
The appropriate timeframe is determined by the nature of the objectives that the dashboard supports.
2.2.1.2. Enrichment through comparison
These measures can be displayed by themselves, but it is usually helpful to compare them to one or more related measures to provide context and thereby enrich their meaning. Here are perhaps the most typical comparative measures, and an example of each.
|
Comparative measure |
Example |
|---|---|
|
The same measure at the same point in time in the past |
The same day last year |
|
The same measure at some other point in time in the past |
The end of last year |
|
The current target for the measure |
A budgeted amount for the current period |
|
Relationship to a future target |
Percentage of this year's budget so far |
|
A prior prediction of the measure |
Forecast of where we expected to be today |
|
Relationship to a future prediction of the measure |
Percentage of this quarter's forecast |
|
Some measure of the norm for this measure |
Average, normal range, or a bench mark, such as the number of days it normally takes to ship an order |
|
An extrapolation of the current to measure in the form of a probable future, either at a specific point in the future or as a time series |
Projection out into the future, such as the coming year end |
|
Someone else's versions of the same measure |
A competitor's measure, such as revenues |
|
A separate but related measure |
Order count compared to order revenue |
These comparisons are often expressed graphically to clearly communicate the differences between the values, which might not leap out as dramatically through the use of text alone. However, text alone is often adequate. For example, when only the comparison itself is required and the individual measures (a primary measure and a comparative measure) aren't necessary, a single number expressed as a percentage can be used (such as 119% of budget or7% of where we were this time last year).
Measures of what's currently going on may be displayed either as a single measure, as a single measure combined with one or more individual comparative measures, or as one of the following:
- Multiple instances of a measure, each representing a categorical subdivision of the measure (for example, sales subdivided into regions or a count of orders subdivided into numeric ranges in the form of a frequency distribution)
- Temporal instances of a measure (that is, a time series, such as monthly instances of the measure)
Time series in particular provide rich context for understanding what's really going on and how well it's going.
2.2.1.3. Enrichment through evaluation
Because with a dashboard a great deal of data must be evaluated quickly, it also is quite useful to explicitly declare whether something is good or bad. Such evaluative information is often encoded as special visual objects (for example, a traffic light) or as visual attributes (for example, by displaying the measure in bright red to indicate a serious condition). When designed properly, simple visual indicators can clearly alert users to the state of particular measures without altering the overall design of the dashboard. Evaluative indicators need not be limited to binary distinctions between good and bad, but if they exceed the limit of more than a few distinct states (for example, very bad, bad, acceptable, good, and very good), they run the risk of becoming too complex for efficient perception.
2.2.2. Non-Quantitative Dashboard Data
Many people think of dashboards and KPIs as nearly synonymous. It is certainly true that dashboards are a powerful medium for presenting KPIs, but not all quantitative information that might be useful on a dashboard belongs to the list of defined KPIs. In fact, not all information that is useful on dashboards is even quantitativethe critical information needed to do a job cannot always be expressed numerically. Although most information that typically finds its way onto a dashboard is quantitative, some types of non-quantitative data, such as simple lists, are fairly common as well. Here are a few examples:
- Top 10 customers
- Issues that need to be investigated
- Tasks that need to be completed
- People who need to be contacted
Another type of non-quantitative data occasionally found on dashboards relates to schedules, including tasks, due dates, the people responsible, and so on. This is common when the job that the dashboard supports involves the management of projects or processes.
A rarer type involves the display of entities and their relationships. Entities can be steps or stages in a process, people or organizations that interact with one another, or events that affect one another, to name a few common examples. This type of display usually encodes entities as circles or rectangles and relationships as lines, often with arrows at one or both ends to indicate direction or influence. It is often useful to integrate quantitative information that is associated with the entities and relationships, such as the amount of time that passed between events in a process (for example, by associating a number with the line that links the events or by having the length of the line itself encode the duration) or the sizes of business entities (perhaps expressed in revenues or number of employees).
Now that you know a bit about how and why dashboards are used, it's time to take a closer look at some design principles. In the next chapter, we'll delve into some of the mistakes that are commonly made in dashboard design.