You want to explore how revenue is affected by certain demographics. Begin
by creating a project and adding the first data source. Columns that contain
numbers are assumed to be measures such as store ID, however you need to treat
these columns as attributes. Review the column characteristics, hide the columns
you don't need, and add the data source to the project. Four data elements are now
hidden in the data set. Make sure that the aggregation method for units and
revenue is set to sum and then add the data source to the project. Switch to
visualize mode to begin building visualizations. Select the first data
element and then use the control key to select other relevant columns. Drag them
to the canvas and 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're able to view the highest values first. A marquee can be
created by dragging the cursor over specific values and right-clicking
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. The demographic detail is in another
spreadsheet. Upload the demographics details and switch back to visualize
mode. 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 verses 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. 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.
Find out more at: oracle.com/data_visualization.