Appliance insights bottlenecked through Slack—hours or days, shaped by who you asked. Now product teams query in plain English.
A 100-year-old global appliance manufacturer transitioning to software-driven connected products, with IoT data scaling across regions in deeply nested, inconsistent formats.
Product teams had no direct access to appliance data, routing all insight requests to the data team via Slack. Even simple questions could take hours...
“The data was there, but it was messy and scattered. A lot of our work was just making it usable.”
Global manufacturer of household appliances for cooking, cleaning, and cooling.
Databricks is a Big Data company that offers a unified analytics platform for data science, engineering, and analytics teams.
Electrolux Group'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.
Checking market share meant waiting for reports. Now, managers ask a Teams AI bot for instant, governed answers.
Deep dives required formal requests to a small data team. Now, staff ask questions in Slack to spot upsell trends instantly.
Deep dives required formal requests to a small data team. Now, staff ask questions in Slack to spot upsell trends instantly.
15 data domains, 20-minute queries, some tables too large to touch. One hackathon later, AI fields half of all queries—customers included.
Every new meeting question triggered days of data prep. Finance teams now ask the data lake directly and test hypotheses in minutes.
Testing physical prototypes delayed evaluations by weeks. Now, researchers use AI consumer personas to simulate product trials in hours.
Admin tasks bogged down 364k consultants. Copilot now drives 40M+ actions, reclaiming $150M in time for client advisory.
Multilingual expansion meant days-long vendor queues and manual file conversions. DeepL solved both, cutting turnaround by more than half.
Appliance insights bottlenecked through Slack—hours or days, shaped by who you asked. Now product teams query in plain English.
A 100-year-old global appliance manufacturer transitioning to software-driven connected products, with IoT data scaling across regions in deeply nested, inconsistent formats.
Product teams had no direct access to appliance data, routing all insight requests to the data team via Slack. Even simple questions could take hours...
“The data was there, but it was messy and scattered. A lot of our work was just making it usable.”
Global manufacturer of household appliances for cooking, cleaning, and cooling.
Databricks is a Big Data company that offers a unified analytics platform for data science, engineering, and analytics teams.
Electrolux Group'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.
Checking market share meant waiting for reports. Now, managers ask a Teams AI bot for instant, governed answers.
Deep dives required formal requests to a small data team. Now, staff ask questions in Slack to spot upsell trends instantly.
Deep dives required formal requests to a small data team. Now, staff ask questions in Slack to spot upsell trends instantly.
15 data domains, 20-minute queries, some tables too large to touch. One hackathon later, AI fields half of all queries—customers included.
Every new meeting question triggered days of data prep. Finance teams now ask the data lake directly and test hypotheses in minutes.
Testing physical prototypes delayed evaluations by weeks. Now, researchers use AI consumer personas to simulate product trials in hours.
Admin tasks bogged down 364k consultants. Copilot now drives 40M+ actions, reclaiming $150M in time for client advisory.
Multilingual expansion meant days-long vendor queues and manual file conversions. DeepL solved both, cutting turnaround by more than half.