


Customer Role: How User Experience Research Is Conducted 








Information Display 
The Information Display pattern group helps project managers, developers, and designers select visualization techniques such as tables and graphs, which enable users to understand the meaning and significance of the numbers. Numbers are central to a user's understanding of business, and they help users make informed decisions. 

Pattern Filter Tool 
Are you looking for the right graph and chart representation? 






















The Charts and Graphs pattern set helps product managers, developers, and designers understand the advantages and disadvantages associated with visualizing data using the basic graph types. People use graphs to see large amounts of data at once, gain insight, and use that insight to take action. The choice of which graph to use depends on the types of data you want to display and the types of insight users are expected to be able to gain. If you are considering using multiple graphs, see the Guidelines for Using Multiple Graphs. 





Use the following decision table to determine what pattern is appropriate based on user needs:

Charts & Graphs 
Analytic Grids 

Does the end user need to examine values to discern patterns or trends? 
Yes 
No 
No 
Does the end user need an easy way to look up a particular value? 
No 
Yes 
No 
Does the end user need only a single data point summary (for example, Up/Down)? 
No 
No 
Yes 






Key Concepts 









User Types 
Casual users are infrequent graph readers who can accurately interpret lowcomplexity graphs, such as bar graphs and pie charts. Experienced users are frequent graph readers who are comfortable reading highercomplexity graphs. Note that experienced users can also easily interpret graphs that are intended for casual users. 
Data Dimension 
A set of related data points of the same type and units, such as product names, years, costs, profits, and so on . Note that the term ”dimension” is used here to mean either quantitative or categorical data. 
Correlation 
Two quantitative data dimensions are correlated if they have a simple and recognizable relationship. For example, if profits rise with higher sales, the profit and sales dimensions have a positive correlation. If profits fall as expenses rise, the profit and expenses dimensions have a negative correlation. You use multiquantity graphs to visualize correlations. 
Trend 
A trend is a correlation that can be used for future planning. For example, if profit and sales have a positive correlation, the trend is positive, indicating that future sales will likely result in future profits. Trends are emphasized by line graphs, a type of multiquantity graphs; percent area graphs, a type of percentage graph; and stacked area graphs, a type of total graphs. 
KPI 
A Key Performance Indicator (KPI) is an aggregated singlequantity metric that is critical for the success of an organization, such as the performance of an operational, tactical, or strategic activity, or yeartodate expenses (see SingleQuantity graphs). 






Data Dimension Types 










Categorical 
Quantitative 
NonSequential 
Aggregate 
Temporal 
Data Values 
Textual names (with no inherent numerical order) 
Numeric quantities, possibly with duplicates 
Ranges of numeric quantities 
Timebased divisions 
Examples 
Products, geographic regions, customers 
Salaries, prices 
Salary ranges, price ranges 
Years, quarters, weeks, seconds 
Use in graphs 
Bars, bar segments, pie slices, colors 
Bar heights, pie areas, twodimensional positions, gray values, bubble sizes 
Bars, pie slices, colors 
Horizontal axis 
Relational terms 
"items" 
"data points," "variables" 
Data dimensions are either categorical or quantitative.
Categorical data is the name of groups of things. Groups may be nested to form hierarchies. When shown in a graph, categorical data is typically shown using a different bar, pie slice, or color for each category or group.
Quantitative data resulting from counts or measurements are considered nonsequential because the data may have duplicate values. Plot nonsequential data using the heights of bars in bar graphs, the area of slices in pie charts, the twodimensional positions of marks and bubbles in scatter graphs, and bubble graphs (respectively), and as gray values in gray scatter graphs and gray bubble graphs.
In contrast, aggregate and temporal data are sequential, do not have duplicate values, and are typically used to mark the scales of graphs. Aggregate data is formed when quantitative data is aggregated into uniformly sized bins or ranges of a sequential scale, such as when salary data is aggregated into salary ranges. Although aggregate data is quantitative, aggregated bins or ranges may also be considered a form of category or group. Aggregate data may, therefore, be shown using different bars or pie slices for each aggregate bin or range. Unlike categorical data, however, aggregate data has a welldefined quantitative order and should be shown with gray values instead of colors in scatter and bubble graphs.
Temporal data is uniformly spaced divisions of time such as years, weeks, and seconds. Due to established conventions, temporal dimensions should generally be plotted on a graph's horizontal axis.










