Snowflake Retail Global

Under Armour

Enterprise Knowledge Search · Real-time Analytics

Fragmented systems, workbook-driven reporting, and inconsistent definitions kept planners and executives from getting fast, trusted answers, making it hard to keep pace with rapid retail decisions and emerging AI demand.

Read the original story Explore all use cases

A week to produce.

Days to compile by hand

Delivered daily.

Run once a month.

Reported impact

Business outcome

Under Armour turned product-margin reporting from a week-long monthly exercise into an automated daily process, delivers leadership reporting by morning instead of after days of manual work, and uses conversational AI to speed onboarding and operational decisions.

Solution

Under Armour standardized analytics and AI on Snowflake so product, sales, inventory, supply-chain, and planning data could support daily reporting and conversational AI without moving data between tools. Operational and commercial data now land on one Snowflake platform that acts as the single source of truth for analytics, business intelligence, and the golden reports that anchor trusted metrics. Secure Data Sharing and Data Clean Rooms extend that foundation, while Snowflake CoWork gives knowledge workers a conversational interface to ask questions against approved data and Snowflake CoCo helps developers build faster. The same architecture powers agentic workflows such as an internal help desk and speeds onboarding by letting new employees learn processes through AI. By combining governance, live shared data, and user-facing AI on one platform, Under Armour turned reporting into daily decision support and made natural-language access practical across the enterprise.

Data flow

Inventory, supply-chain, demand-planning, sales, and product data are centralized in Snowflake and anchored to trusted golden reports. Secure Data Sharing and Data Clean Rooms extend that governed foundation, while CoWork and CoCo turn the same approved data into conversational answers, developer assistance, and agentic workflows.

Solution architecture

5 components · 4 layers

Compute
- Snowflake CoCo Supports developers and agentic workflows that build, diagnose, and extend AI-enabled data experiences faster.
Storage
- Snowflake Acts as the central platform for governed retail, planning, and analytics data used across the enterprise.
Serving
- Secure Data Sharing Extends trusted Snowflake data to collaborators without moving or duplicating the underlying datasets.
- Snowflake CoWork Lets knowledge workers ask natural-language questions against governed data and receive actionable answers quickly.
Governance
- Data Clean Rooms Provides governed collaboration patterns for sensitive data while preserving trust and control.

Architecture clues

AI-generated answers are anchored to golden reports so natural-language insights start from trusted business definitions.
The team spent five years building observability, governance, and reliable data models before scaling conversational AI.
Under Armour emphasizes not moving data, using Snowflake to combine analytics, collaboration, and AI on one platform.

Evidence from the source

A week to produce.

Delivered daily.

Run once a month.

Snowflake CoWork has been a game changer for us to truly enable our end users with conversational AI in a controlled manner.

Snowflake has provided us with a single source of truth for analytics, AI and business intelligence.

Transparency Evidence & methodology 75/100 reference value · 77% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value75/100

Problem clarity20/20
Measures whether the customer's operational problem is specific enough to compare.
Implementation depth25/25
Rewards named components, data flow, integrations, and architecture detail.
Outcome specificity20/20
Measures whether the reported benefit is concrete rather than a generic claim.
Quantified impact0/25
0 metrics describe measurable impact.
Deployment scale10/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.

Evidence confidence77%

Source authority20/25
The main evidence is an official vendor-published customer story.
Claim traceability21/25
10 of 12 key fields link to a named source.
Direct evidence20/20
5 direct source quotes retained.
Date provenance0/10
The publication date has no documented provenance.
Source transparency8/10
1 source reference document publisher, URL, authority, retrieval, and supported fields.
Implementation specificity8/10
Rewards source-backed components, data flow, quantified impact, and a specific outcome.

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

Snowflake
Under Armour customer story

First-party
https://www.snowflake.com/en/customers/all-customers/case-study/under-armour/

Source type
Vendor-published customer story
Supports
- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes

Date evidence

First archived Sep 2, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
Open date source
Sitemap last modified Aug 21, 2026 · Snowflake · Publisher metadata Publisher-reported modification timestamp; not a publication date.
Open date source

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

Similar use cases

See all like this

Google Cloud Modified · Jun 26, 2026 Kogan.com Retail Australia Kogan.com needed to eliminate brittle export-based BI pipelines, shorten slow answer cycles for business users, and expose analytics conversationally without losing semantic consistency or data-level security. 150 non-technical team members Enterprise Knowledge SearchIT Operations Automation BigQuery · Looker · Model Context Protocol (MCP) fleet server

Google Cloud Archived · Mar 26, 2026 Fluent Commerce Retail Global Fluent Commerce needed to turn retailer fulfillment data into trusted, actionable insights for business users instead of trapping operational questions in engineering backlogs and static reports. Enterprise Knowledge SearchReal-time Analytics BigQuery · Looker

Snowflake Archived · Sep 16, 2025 TUI Group Travel and Hospitality Hanover, Germany Decentralized business-unit data practices and complex analytics tooling made it hard for nontechnical users to discover the right data, understand schemas, and get answers quickly. 1 hour to develop its first AI use case Enterprise Knowledge SearchReal-time Analytics Cortex Analyst · Cortex Search · Snowflake