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.
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 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
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.
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 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 3rd 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
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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