How to Create Custom Datasets in Scintilla

Bekah Tatem

By Bekah Tatem, Content Coordinator

Last Updated January 7, 2025

9 min read

In this article, learn about:

  • Pre-built vs. Custom Datasets in Luminate/Scintilla

  • How to create and save a Custom Dataset

  • Editing and managing your saved Custom Datasets

  • Example Custom Dataset for data segmentation analysis


When Walmart transitioned from DSS to Lumiante/Scintilla, suppliers had to quickly adapt to a new way of pulling their reports. Although Walmart has provided template datasets to pull reports, finding one-to-one report equivalents has been challenging for suppliers.

In addition, with Walmart offering two levels of service, Basic (free) and Charter (paid), suppliers on the free service do not have access to the same level of data.

To help suppliers have more flexibility in the reports they create, Luminate/Scintilla Basic now has a feature called Custom Datasets. In this article, we'll dive into how to use this feature and create tailored reports that meet your unique business needs.

What are Custom Datasets?

Within Luminate/Scintilla, Walmart provides pre-built datasets covering various areas, such as Store Sales and inventory, Order Corecast, and eComm Sales. 

Screenshot of a dataset selection interface with options for 'Dataset' and 'Reports.' The 'Walmart dataset' option is highlighted in red, described as 'Pre-crafted datasets with recommended tables.' A 'Custom Dataset' feature allows users to build personalized datasets.Several dataset options are displayed, including:'Store Sales & Inventory': Sales and inventory metrics (104 weeks). 'DC Metrics': Supplier to DC inventory metrics (104 weeks).'Modular Plan Metrics': Product placement and modular assignments. 'Store Demand Forecast': Walmart's sales projections.
'Order Forecast': Expected order projections. 'Store Markup & Markdowns': Markup and markdown trends (104 weeks). 'eComm Sales': eCommerce sales metrics (104 weeks). 'eComm Inventory': eCommerce inventory metrics (104 weeks). The interface is structured with search and filter options for dataset selection.

While these datasets are helpful, suppliers may have reporting needs that fall outside the confines of these reports; that's where Custom Datasets come in. Custom Datasets allow suppliers to:

  • Combine tables from multiple datasets for a more holistic view.

  • Apply filters to refine data for unique use cases.

  • Save Custom Datasets for quick use in the future.

Screenshot of a dataset selection interface. The 'Dataset' tab is active with options for 'All dataset,' 'My dataset,' and 'Walmart dataset.' A highlighted 'New! Custom Dataset' section explains a flexible reporting feature with a 'Show me how' button. Next to it, a 'Custom Dataset' option allows users to build personal datasets by combining tables.

How to Create Custom Datasets

Login to Luminate > click New Report > select Custom Dataset beneath My datasets.

Screenshot of a dataset selection interface. The 'Custom Dataset' option is highlighted with a blue border and allows users to build personal datasets by combining tables. To the left, a 'New! Custom Dataset' section introduces a flexible reporting feature with a 'Show me how' button.

Select the data tables you want to include in your report:

  • You can search for specific tables or field names using the search function.

  • To see the data attributes included in each table, click on the three dots next to the table name and select Table Details. This allows you to search for an attribute by name or by its DSS alias. 

  • Make sure to select Save dataset at the bottom left-hand corner of the pop-up if you want to save this dataset for continued use.

  • Click Okay once you've finished making your selections.

Popup window titled 'Save dataset' with a text input field containing 'Store Return Details.' Below, two buttons are available: 'Don't save' (gray) and 'Save' (purple).

If you choose to save the dataset, you'll now have the chance to name it.

Popup window titled 'Save dataset' with a text input field containing 'Store Return Details.' Below, two buttons are available: 'Don't save' (gray) and 'Save' (purple).

Now your dataset is saved, it's time to create your report.

Screenshot of a report-building interface titled 'Untitled.' The 'Columns' tab is active, showing 'Store Return Details' with selectable options like 'Time Period,' 'Item Info and Item Hierarchy,' 'Store Customer Returns,' and 'Store Info.' A dropdown for selecting dimensions and metrics is visible. The top menu includes 'Preview,' 'Save,' and 'Create Report' buttons, with a 'Data refreshed' status indicator.

Here are a few things to keep in mind while creating your report:

  • To create any report, you need at least one dimension, one metric, and a time period filter. 

  • In Custom Datasets, there is no function to compare multiple time periods. If you need to do this, we recommend pulling multiple reports and combining them in excel. 

  • You can toggle between Columns and Filters options in the top left corner of your screen.

  • If you're struggling to find dimensions/metrics you're used to in DSS, hover your mouse over the name to reveal more details and the DSS alias.

Related Reading: DSS Aliases in Luminate

Screenshot of a data selection interface showing a tooltip for 'Accounting Department Number.' The tooltip displays its name, alias ('Acct Dept Nbr'), and description ('The parent department number of two or more department numbers'). The interface lists various selectable data fields, including 'Accounting Department Description,' 'Activity Code,' 'All Links Item Number,' and 'Base Retail UOM Code,' with checkboxes next to each field.

How to Edit or Delete Your Custom Datasets

Once you've saved a Custom Dataset, you can return to the New Report screen to see your saved datasets.

Screenshot of a dataset selection interface under 'My dataset (BETA).' The 'Store Return Details' dataset, created by 'Me,' is highlighted in red. Next to it, options for creating a 'Custom Dataset' and a 'New! Custom Dataset' feature are visible, with a 'Show me how' button.

By clicking the three dots in the top right corner of the tile, you can edit, rename or delete the dataset.

Screenshot of the 'Store Return Details' dataset with a dropdown menu open, showing options to 'Edit,' 'Rename,' or 'Delete' the dataset. The menu is highlighted in red.

Custom Dataset Example: Data Segmentation

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:

Attribute Needed for ReportDSS AliasWalmart Dataset
Prime Item NumberPrime Item NbrItem Info and Item Hierarchy
All Links Item DescriptionPrime Item DescItem Info and Item Hierarchy
Store NumberStore NbrStore Info
City NameCityStore Info
State or Province NameStateStore Info
POS Quantity-This YearPOS QtyStore Sales
Store On Hand Quantity-This YearCurr On Hand QtyStore Inventory

Here are the steps to pulling that information from Luminate/Scintilla using a Custom Dataset:

Login to Luminate > click New Report > select Custom Dataset beneath My datasets. Screenshot of a dataset selection interface. The 'Custom Dataset' option is highlighted in red, allowing users to build personal datasets by combining tables. The 'New! Custom Dataset' section is visible on the left with a 'Show me how' button.

Select desired tables:

  • Item Info and Item Hierarchy

  • Store Info

  • Store Sales

  • Store Inventory

Screenshot of the 'Custom Dataset' selection window. The right panel lists selected tables: 'Item Info and Item Hierarchy,' 'Store Info,' 'Store Sales,' and 'Store Inventory.' The 'Save dataset' checkbox is checked, and the 'Okay' button is highlighted in purple. Options to clear selections and cancel are also visible.

Save the Custom Dataset.

Screenshot of a 'Save dataset' popup. The text field contains 'Item Analysis.' Below, there are two buttons: 'Don't save' (gray) and 'Save' (purple, highlighted in red).

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

Screenshot of the 'Columns' panel for the 'Item Analysis' dataset. It includes a search bar and expandable categories: 'Time Period,' 'Item Info and Item Hierarchy' (2/103 selected), 'Store Info' (3/35 selected), 'Store Inventory' (1/34 selected), and 'Store Sales' (1/12 selected).

Set filters.

Screenshot of the 'Filters' panel. It includes a required filter for 'Walmart Calendar Week' set to 'WMT Last 4 weeks.' An optional filter for 'Catalog Item ID' is applied with the value '123123123.' Options to add or remove filters are available.

Click Create Report.

Screenshot of a toolbar with options for 'Preview' (locked), 'Save' (dropdown), and 'Create Report' (highlighted in red and purple button).

Fill out the required fields and create the report.

Screenshot of the 'Create Report' page. The report name is set to 'Item Analysis.' The selected report format is '.XLSX.' The schedule is set to 'Today,' and email notifications are enabled. Options for 'Run Once' or 'Run Continuously' are available.

Once the report has been created, it can take 30 minutes to an hour 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 Luminate/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.

Related Reading: What is Segmentation Analysis in Retail?

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