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
Deep dives required formal requests to a small data team. Now, staff ask questions in Slack to spot upsell trends instantly.
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.
Deep dives required formal requests to a small data team. Now, staff ask questions in Slack to spot upsell trends instantly.
Every 'why is this number moving?' queued behind the domain team. Now leaders ask Genie directly and get the answer on the spot.
Disconnected systems trapped buyer data and complicated B2B orders. Now, AI-powered commerce unifies buying across showrooms and screens.
Every new meeting question triggered days of data prep. Finance teams now ask the data lake directly and test hypotheses in minutes.
Manual document summaries bottlenecked experienced staff. Now, AI drafts preliminary risk assessments, freeing teams for complex advisory.
Multilingual expansion meant days-long vendor queues and manual file conversions. DeepL solved both, cutting turnaround by more than half.
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
Deep dives required formal requests to a small data team. Now, staff ask questions in Slack to spot upsell trends instantly.
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.
Deep dives required formal requests to a small data team. Now, staff ask questions in Slack to spot upsell trends instantly.
Every 'why is this number moving?' queued behind the domain team. Now leaders ask Genie directly and get the answer on the spot.
Disconnected systems trapped buyer data and complicated B2B orders. Now, AI-powered commerce unifies buying across showrooms and screens.
Every new meeting question triggered days of data prep. Finance teams now ask the data lake directly and test hypotheses in minutes.
Manual document summaries bottlenecked experienced staff. Now, AI drafts preliminary risk assessments, freeing teams for complex advisory.
Multilingual expansion meant days-long vendor queues and manual file conversions. DeepL solved both, cutting turnaround by more than half.