Now that we understand the basics of building a Custom Dataset, let’s look at a real-time example. Let’s say a supplier wants to do a data segmentation analysis on an item with low instock levels but high weeks of supply.
The goal of this analysis is to divide store data into distinct groups or segments to identify microtrends much more quickly than drilling down to individual store-level.
To pull the data needed for this analysis, we need a custom report that combines tables from multiple datasets to extract all the attributes needed for the report.
Here’s the information we need:
1. Here are the steps to pulling that information from Scintilla using a Custom Dataset:
Login to Scintilla > click New Report > select Custom Dataset beneath My datasets.
2. Select desired tables:
- Item Info and Item Hierarchy
- Store Info
- Store Sales
- Store Inventory
3. Save the Custom Dataset.
4. Configure the columns with the needed dimensions:
- Prime Item Number
- All Links Item Description
- Store Number
- City Name
- State or Province Name
- POS Quantity – This Year
- Store On Hand Quantity – This Year
5. Set filters.
6. Click Create Report.
7. Fill out the required fields and create the report.
Once the report has been created, it can take 20-70 minutes for the report to be ready to download. After the report is ready, you can use the segmented data to identify trends, such as which geographic areas are experiencing stock imbalances or sales anomalies.
These insights allow for targeted interventions to optimize inventory and improve supply chain efficiency.
This is just one example of how Custom Datasets in Scintilla can be used to meet specific business and reporting needs.
If you’ve not yet explored Custom Datasets, we highly recommend trying them. They’re a powerful tool for unlocking deeper insights into your Walmart business and rebuilding reports you used to access in DSS.