字幕列表 影片播放 列印英文字幕 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.