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.