Choosing the Right OBIEE Visualization
Presenting
business insights includes choosing the appropriate data visualizations. 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.
Using
color
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 are used 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
Bar
charts should always start with zero and show nominal data values in comparison
to each 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.
Pie
Charts
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
Line
charts are the benchmark for showing data over time. Edward Tufte, the expert
in visualization 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 3rd
metric to be compared on the same chart.
Sparklines
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
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 11.1.1.6.4 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.
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