Workflow 8
In this workflow, we’ll walk through the steps to create a self-service report. Begin by navigating to the Data Center and accessing the Data Sandbox to use retail store purchase and sales data. Next, create a datastore using this data. Then, move to the Report module to build your visualization. Add a report name and description if needed, select the newly created datastore, and explore the data to generate insights.
Step-by-Step Instructions
Navigate to the Data Center module.
Create a new Data Sandbox for Retail Store Purchase and Sales Data.
Once the Data Sandbox is created, proceed to create separate Datastores for both Retail Store Purchase Data and Sales Data.
4. Navigate to the App Menu and open the Report Module to create the visualization.
5. Provide a Report Name and, if required, a description.
6. Choose the Existing Data Store option to create a Self-Service Report.
7. Select the Purchase Data Store and click Create Report.
8. Begin creating KPIs for the Product Procurement Overview report.
9. Search for Purchase Amount and plot a KPI tile showing the Total Purchase Amount of products.
10. Search for Quantities and plot a KPI tile showing the Total Quantity Purchased.
11. Create a chart by searching for Brand Name and Purchase Amount to identify the most preferred brand, using charting properties for customization.
12. Search for Product Name and Purchase Amount to create a chart of the Top Purchased Product Items.
13. Explore additional KPIs using the Retail Store Purchase Data as needed.
14. Finalize the report and save it with the name “Product Procurement Overview.”
15. Provide a name and description for the report, if necessary.
16. Choose the existing data store option to create a self-service report.
17. Select the purchase data store and then click on "Create Report."
18. Begin creating KPIs for the "Product Procurement Overview" report.
19. Search for the purchase amount, then plot the KPI to create a KPI tile for the total purchase amount of the product.
20. Search for quantities, then plot the KPI to create a KPI tile for the total quantity purchased.
21. Create a chart by searching for "brand name" and "purchase amount" to identify the most preferred brand, utilizing the charting properties.
22. Search for "product name" and "purchase amount" to create a chart of the top purchased product items.
23. Explore additional KPIs using the retail store purchase data.
24. Finally, provide the report name as "Product Procurement Overview."
Search for "billing date" and "net sale value," then plot the KPI tile for the net value of goods sold.
Search for "billing date" and "quantities," then plot the KPI to create a KPI tile for the total quantities sold.
Begin exploring the chart by searching for "product categories" and "net sale value," then plot the chart utilizing the charting properties to create the "Most Valued Categories" chart.
Next, search for "product categories" and "net sale value," then plot the chart using the charting properties to create the "Least Valued Categories" chart.
Finally, provide the report name as "Product Sale Overview."
Start creating KPIs for the ‘Insights’ report using the Machine Learning (ML) view.
In addition to the standard view, there is the capability to create an ML view, which includes four ML tasks: customer segmentation, anomaly detection, time series analysis, and sentiment analysis.
First, use the customer segmentation ML task. Search for "city," "net sale value," "basic cost," "tax amount," and "discount." Plot the chart by clicking on the ML tab, then select the segmentation ML task. Choose the feature column and specify the cluster count, then select the operator and click "Predict.".
Next, use the time series analysis ML task. Search for "billing date" and "net sale value," then plot the chart. Select the time series analysis, set the feature column to "net sale value," and specify the prediction count as 10. Click "Predict," and the system will forecast the net sale value for the next 10 days based on the provided data.
Search for "store list" and "quantities," then plot the chart. Select the anomaly detection task and choose the feature column. Predict the quantities for the store "Wonders," flagging any significantly higher values across all entries as anomalies.
Finally, provide the report name as "Insights," change the theme, and then save the report.
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