Each team had its own pipeline; analysts burned their days reconciling workbooks. Now anyone types a question and gets one trusted answer.
A global athletic apparel brand operating across wholesale, e-commerce, and owned retail, with a data estate spanning inventory, supply chain, demand planning, and sales, where direct-to-consumer conditions can shift within hours.
Different teams built their own data pipelines and arrived at different answers to the same question, leaving analysts to spend most of their day...
“AI is moving at a speed no one’s ever seen before. Having a platform, an ecosystem and a capability to meet that speed is important — because the business is moving just as fast.”
Global retailer of athletic apparel, footwear, and accessories.
Cloud-based data warehousing, processing, and analytics platform.
Under Armour's Self-service analytics is part of this use case:
Related implementations across industries and use cases
Every 'why is this number moving?' queued behind the domain team. Now leaders ask Genie directly and get the answer on the spot.
Appliance insights bottlenecked through Slack—hours or days, shaped by who you asked. Now product teams query in plain English.
15 data domains, 20-minute queries, some tables too large to touch. One hackathon later, AI fields half of all queries—customers included.
Appliance insights bottlenecked through Slack—hours or days, shaped by who you asked. Now product teams query in plain English.
15 data domains, 20-minute queries, some tables too large to touch. One hackathon later, AI fields half of all queries—customers included.
Every 'why is this number moving?' queued behind the domain team. Now leaders ask Genie directly and get the answer on the spot.
Hours searching SharePoint, days waiting for QA, reports rewritten when templates changed. Now two agents answer instantly from live docs.
Energy market research that took weeks now closes same-day. Staff freed from chasing information to make the calls that matter.
With 99% of records missing metadata, teams sifted unlabeled databases. Now, AI generates column descriptions for data experts to verify.
Each team had its own pipeline; analysts burned their days reconciling workbooks. Now anyone types a question and gets one trusted answer.
A global athletic apparel brand operating across wholesale, e-commerce, and owned retail, with a data estate spanning inventory, supply chain, demand planning, and sales, where direct-to-consumer conditions can shift within hours.
Different teams built their own data pipelines and arrived at different answers to the same question, leaving analysts to spend most of their day...
“AI is moving at a speed no one’s ever seen before. Having a platform, an ecosystem and a capability to meet that speed is important — because the business is moving just as fast.”
Global retailer of athletic apparel, footwear, and accessories.
Cloud-based data warehousing, processing, and analytics platform.
Under Armour's Self-service analytics is part of this use case:
Related implementations across industries and use cases
Every 'why is this number moving?' queued behind the domain team. Now leaders ask Genie directly and get the answer on the spot.
Appliance insights bottlenecked through Slack—hours or days, shaped by who you asked. Now product teams query in plain English.
15 data domains, 20-minute queries, some tables too large to touch. One hackathon later, AI fields half of all queries—customers included.
Appliance insights bottlenecked through Slack—hours or days, shaped by who you asked. Now product teams query in plain English.
15 data domains, 20-minute queries, some tables too large to touch. One hackathon later, AI fields half of all queries—customers included.
Every 'why is this number moving?' queued behind the domain team. Now leaders ask Genie directly and get the answer on the spot.
Hours searching SharePoint, days waiting for QA, reports rewritten when templates changed. Now two agents answer instantly from live docs.
Energy market research that took weeks now closes same-day. Staff freed from chasing information to make the calls that matter.
With 99% of records missing metadata, teams sifted unlabeled databases. Now, AI generates column descriptions for data experts to verify.