Choosing the Right OBIEE Visualization
Presenting business insights includes choosing the appropriate data visualizations to tell a story or make a key point. That may seem easy but choosing the best one is quite powerful. Getting the best use of data visualizations is and often overlooked but it is important part of bi design. Let’s looks at some of the best practices with OBIEE and present some common mistakes to keep in mind.
Color is an import part of any BI tool. Developers should consciously choose a color palate for most reports and dashboards. Color must be consider when conditional formatting, creating map views or graphs, and for the overall feel of the dashboard. Colors schemes fall into 3 categories.
1. Sequential schemes are suited to ordered data that progress from low to high. Lightness steps dominate the look of these schemes, with light colors for low data values to dark colors for high data values.
2. Diverging schemes put equal emphasis on mid-range critical values and extremes at both ends of the data range. The critical class or break in the middle of the legend is emphasized with light colors and low and high extremes are emphasized with dark colors that have contrasting hues.
3. Qualitative schemes do not imply magnitude differences between legend classes, and hues areused to create the primary visual differences between classes. Qualitative schemes are best suited to representing nominal or categorical data.
Check out ColorBrewer2.org for ideas on choosing the best color scheme for the message you are trying to convey. Below the heat map pivot table is using a diverging scheme to highlight the top values but show a sequence for the other ‘Top’ values. The map is using a 12 tiered sequential theme to show populations from low to high with the darkest blues representing the most populated counties. The table to the right shows a Qualitative scheme that just distinguishes product types into 3 categories.
Which Chart Should I chose?
Bar charts should always start with zero and show nominal data values in comparison toeach other. They can be vertical or horizontal. Design should always try to avoid horizontal scrolling which could dictate the orientation of the bar chart. Utilize features like section scrolling or graph prompts to maximize dashboard real estate and offer the cleanest looking charts. The bar chart to the right starts at zero, compares products, allows the user to section slide for month over month numbers, and allows for prompting on the company using a graph prompt.
Stacked Bar Chart
Stack bar graphs can be confusing if not used appropriately. Stacking numbers like percentages, or loosely related dimensions can lead to misleading results. The total is the most clearly identified number of the display and should be the most relevant fact on display. It is best practice to set the largest stack on the bottom as much as possible. Colors or patterns should be easily distinguished and use a qualitative scheme. Area charts show the stacked relationships (totals) best flowing over time.
Most often, pie charts are misused to communicate part-to-whole scenarios where line or bar charts would be much more effective. They should not be done in #D, have a limited number of slices, and be used to show percentage of the whole. Many visualization experts dislike them as they tend to be misused.
Line charts are the benchmark for showing data over time. Edward Tufte, the expert invisualization argued that it’s a good idea to look at what he called the data ink ratio and showed how the removal of certain chart elements can increase its readability. For instance you don’t need to draw a box around the chart area. Also you can use the ends of axis lines to display the minimum and maximum value in the data. Highlight what’s important. Although it is possible to tell hundred stories using a single line chart, it makes a lot of sense to keep the focus on just one story. Therefore you should highlight just one or two important lines in the chart, but keep the others as context in the background.
Scatter Plots and Bubble Graphs
Scatter plots are great options for displaying relationships between two quantitative variables, even with exceptionally large sets of data. Best practices around scatter plots include removing fill color where possible, visually identifying groups when multiple groups are plotted together (shapes, images, shades of color), displaying trend lines and using trellis charts to reduce complexity. Bubble charts limit the number of points that can be plotted but allow for a 3 metric to be compared on the same chart.
A KPI typically communicates the here and now, but it does not effectively showcase historical performance or trends. To add context to your KPI, it is a best practice to supplement it with sparklines. Sparklines are data-intense, design-simple, word-size graphics that provide a quick sense of historical context. When designing sparklines in reports, it is helpful to also highlight the minimum point and the maximum point.
Trellis charts are a small series of charts that much like sparklines also provide a very fast visual comparison of trends over time periods. In 126.96.36.199.4 they are available as simple or advanced trellis charts. Think of a pivot table on steroids, where you can show charts in context of a 2 axis pivot table. It really allows for the maximization of data consumption on one page. In the example below the columns represent time periods during the day, the rows represent the flights distance in 3 buckets, and the bubble chart shows 4 different metrics in each in cell. The color represents performance rating, the vertical axis shows the number of routes, the horizontal axis shows the % late, and the bubble size is the number of flights. It paints a picture of shorts routes having more flights and diminishing performance as the day progresses These are a few examples of visualizations in OBIEE. Choosing the one that best tells the storey is the key to good dashboard design.