- Overview
- Platform setup and administration
- Platform setup and administration
- Platform architecture
- Data Bridge onboarding overview
- Connecting a Peak-managed data lake
- Connecting a customer-managed data lake
- Creating an AWS IAM role for Data Bridge
- Connecting a Snowflake data warehouse
- Connecting a Redshift data warehouse (public connectivity)
- Connecting a Redshift data warehouse (private connectivity)
- Reauthorizing a Snowflake OAuth connection
- Using Snowflake with Peak
- SQL Explorer overview
- Roles and permissions
- User management
- Inventory management solution
- Commercial pricing solution
- Merchandising solution
Supply Chain & Retail Solutions user guide
Data requirements
This page describes the customer data required by the UiPath solution for merchandising. For details on how to connect and ingest data into the Peak platform, see Data ingestion.
The UiPath solution for merchandising relies on customer data to generate insights about product performance and demand-shaping opportunities. This data represents business information that already exists in the organization's enterprise systems and is integrated into the Peak platform during onboarding.
Business users do not create or maintain this data manually in the solution interface.
How data is used by the solution
The merchandising solution uses customer-provided business data to evaluate product performance and identify merchandising opportunities.
In practice:
- Product and assortment data defines the items and categories included in analysis.
- Sales and demand history provides historical context for demand patterns and product performance.
- Pricing and promotion history captures historical pricing decisions and promotional activity that influence demand.
- Inventory availability data provides operational feasibility context for merchandising actions.
- Location and channel context enables analysis across stores, regions, or channels.
During onboarding, these datasets are mapped and integrated into the Peak platform. The quality and consistency of this data directly influence the relevance of generated insights.
Required data categories
To support merchandising insights, the following business data categories are typically required:
| Business data category | Description |
|---|---|
| Product and assortment data | Defines the products and categories included in merchandising analysis. |
| Sales and demand history | Provides historical transaction data used to understand demand patterns and product performance. |
| Pricing and promotion history | Captures historical prices, markdowns, and promotional activities that influence demand. |
| Inventory availability data | Represents current inventory levels used to ensure merchandising actions are operationally feasible. |
| Location or channel context | Defines where products are sold, enabling analysis across stores, regions, or channels. |
Data sources and implementation details
These datasets are typically sourced from existing enterprise systems, such as:
- ERP systems
- Retail or sales platforms
- Pricing systems
- Data warehouses
The specific sources, formats, and ingestion methods depend on the customer environment and are configured during onboarding.