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  • 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.

You want to explore how revenue is affected by certain demographics. Begin

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B1 US revenue data data source visualization item visualize

Blend Data in Oracle Data Visualization

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    Chris Lyu posted on 2018/09/26
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