You want to explore how revenue is affected
by certain demographics.
Begin by creating a project and adding a data
source.
Columns that contain numbers are assumed to
be measures, such as store ID.
However, you need to treat these columns as
attributes.
Right-click the columns you don’t need in
the data set and select Hide.
Four data elements are now hidden in the data
set.
Make sure that the aggregation method for
Units and Revenue is set to Sum.
Switch to Visualize mode to begin building
visualizations.
Select a data element, and then use the control
key to select other relevant columns.
Drag them to the canvas.
Begin exploring the data by swapping Depot
Name with Item Type.
By positioning the mouse over a value, and
using the right-click menu to sort the data,
you are able to view the highest values first.
Drag the cursor to create a marquee.
Right-click inside the marquee area to keep
only the selected values.
Now that you are focused on exploring the
highest revenue-producing item types, you
want to extend the data by adding demographics.
Next, take a look at the connections in the
Source Diagram.
A connection by zip code is made with the
other source automatically.
Now, begin to examine the impact on revenue
by selecting the education demographic data
element.
Drag average education to the Trellis Rows
drop target.
It looks like the highest revenues generated
are for those who have achieved an education
level of 15 years.
You’d like to see if the revenue goals were
met for these item types as well.
Do this by adding the target revenue data
source.
Two connections are recommended.
Review all the characteristics and include
a third connection that matches store sales
with target revenue based on dates.
Verify the match and return to Visualize mode.
Now, create a revenue calculation for the
daily sales versus target revenues.
Double-click data elements and operators to
create the expression, and then validate it.
Both measures are from different sources.
Add a second visualization to explore revenue
variances by copying the existing visualization
and selecting the location on the canvas to
paste it.
Delete average education and depot name from
the chart you just added.
Replace revenue with Revenue Variance from
the My Calculations folder …
…and item type with order date.
Focus the visualization on 2016 by adding
a marquee, and keep only those values.
The filter is applied to both visualizations.
You notice that for most of this time period,
target revenues were below expectations.
Now that you’ve finished, save the project.
Based on this exploration, you now have a
better understanding of the revenue generated
for specific item types.
In this video, I showed you how to create
a project, open and blend data sources, swap
columns, limit data, and create a calculation.
Learn more at docs.oracle.com
