Thousands of tables tracking the same metrics differently. No one knew which to trust — not humans, not AI. One governed layer changed that.
A B2B visual collaboration platform with a five to seven person analytics team serving the entire business, operating a Redshift warehouse of thousands of tables, many tracking the same metrics with different calculations and no consistent quality standards.
Data requests joined a queue behind committed work, typically waiting one to two weeks for a response. Knowing which table to trust required...
“It would easily take a week or two to get to a request. The team had to decide whether to answer one person's question or build the dashboard they had already committed to.”
Visual AI platform for team collaboration, strategy, and execution.
Databricks is a Big Data company that offers a unified analytics platform for data science, engineering, and analytics teams.
Mural's Self-service analytics is part of this use case:
Related implementations across industries and use cases
All data questions funneled through one team—anything that wouldn't make the queue went unexplored. Now teams self-serve through AI agents.
Every data question routed through engineers. Now 800+ users — 70% non-technical — query a 200-country travel dataset in plain language.
Staff filed data requests through chat and waited days for analyst replies. Now they ask the data lake directly—no SQL, no queue.
Every data question routed through engineers. Now 800+ users — 70% non-technical — query a 200-country travel dataset in plain language.
Most staff worked around the data warehouse in Excel; new reports queued behind Qlik specialists. Now executives query KPIs from Teams.
Same work tracked in four disconnected tools. AI pulls live context from all of them—drafts land ready to sharpen, not start.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Scaling AI access across a 100,000-employee workforce risked system overloads. An auto-scaling platform launched with zero downtime.
Energy market research that took weeks now closes same-day. Staff freed from chasing information to make the calls that matter.
Thousands of tables tracking the same metrics differently. No one knew which to trust — not humans, not AI. One governed layer changed that.
A B2B visual collaboration platform with a five to seven person analytics team serving the entire business, operating a Redshift warehouse of thousands of tables, many tracking the same metrics with different calculations and no consistent quality standards.
Data requests joined a queue behind committed work, typically waiting one to two weeks for a response. Knowing which table to trust required...
“It would easily take a week or two to get to a request. The team had to decide whether to answer one person's question or build the dashboard they had already committed to.”
Visual AI platform for team collaboration, strategy, and execution.
Databricks is a Big Data company that offers a unified analytics platform for data science, engineering, and analytics teams.
Mural's Self-service analytics is part of this use case:
Related implementations across industries and use cases
All data questions funneled through one team—anything that wouldn't make the queue went unexplored. Now teams self-serve through AI agents.
Every data question routed through engineers. Now 800+ users — 70% non-technical — query a 200-country travel dataset in plain language.
Staff filed data requests through chat and waited days for analyst replies. Now they ask the data lake directly—no SQL, no queue.
Every data question routed through engineers. Now 800+ users — 70% non-technical — query a 200-country travel dataset in plain language.
Most staff worked around the data warehouse in Excel; new reports queued behind Qlik specialists. Now executives query KPIs from Teams.
Same work tracked in four disconnected tools. AI pulls live context from all of them—drafts land ready to sharpen, not start.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Scaling AI access across a 100,000-employee workforce risked system overloads. An auto-scaling platform launched with zero downtime.
Energy market research that took weeks now closes same-day. Staff freed from chasing information to make the calls that matter.