Analysts spent 5 days coding reports. Now, business users query millions of records in plain English for instant results.
A North American infrastructure provider supplying 33% of United States natural gas through 30,000 miles of pipelines while managing millions of transaction records.
Centralized analysts were overwhelmed by data requests, often spending up to a week coding and validating complex reports for executives. This...
“A data request that previously took an analyst five days to complete was entered into Genie—including all the bullet points—and it returned results in just seconds.”
Natural gas processing and transportation infrastructure company.
Databricks is a Big Data company that offers a unified analytics platform for data science, engineering, and analytics teams.
Williams's Self-service analytics is part of this use case:
Related implementations across industries and use cases
Most employees couldn't reach the data without an analyst. Refinery experts now build soft sensors through conversation, no code needed.
Joining fragmented utility data took hours of manual filtering. Now, users query an AI assistant in plain English for instant charts.
15 data domains, 20-minute queries, some tables too large to touch. One hackathon later, AI fields half of all queries—customers included.
Simple questions demanded 45 minutes of SQL. An AI assistant now answers 70% of internal analytics, freeing the team from routine queries.
Complex analytics tools overwhelmed non-technical staff. Now, an AI interface allows employees to query data using simple conversation.
Translations needed line-by-line review; meetings ran one-way through interpreters. Now field expertise flows across languages.
Engineers spent weeks manually mapping well sites. Now, geospatial AI evaluates millions of permutations to maximize leasehold coverage.
With 99% of records missing metadata, teams sifted unlabeled databases. Now, AI generates column descriptions for data experts to verify.
Manual document summaries bottlenecked experienced staff. Now, AI drafts preliminary risk assessments, freeing teams for complex advisory.
Analysts spent 5 days coding reports. Now, business users query millions of records in plain English for instant results.
A North American infrastructure provider supplying 33% of United States natural gas through 30,000 miles of pipelines while managing millions of transaction records.
Centralized analysts were overwhelmed by data requests, often spending up to a week coding and validating complex reports for executives. This...
“A data request that previously took an analyst five days to complete was entered into Genie—including all the bullet points—and it returned results in just seconds.”
Natural gas processing and transportation infrastructure company.
Databricks is a Big Data company that offers a unified analytics platform for data science, engineering, and analytics teams.
Williams's Self-service analytics is part of this use case:
Related implementations across industries and use cases
Most employees couldn't reach the data without an analyst. Refinery experts now build soft sensors through conversation, no code needed.
Joining fragmented utility data took hours of manual filtering. Now, users query an AI assistant in plain English for instant charts.
15 data domains, 20-minute queries, some tables too large to touch. One hackathon later, AI fields half of all queries—customers included.
Simple questions demanded 45 minutes of SQL. An AI assistant now answers 70% of internal analytics, freeing the team from routine queries.
Complex analytics tools overwhelmed non-technical staff. Now, an AI interface allows employees to query data using simple conversation.
Translations needed line-by-line review; meetings ran one-way through interpreters. Now field expertise flows across languages.
Engineers spent weeks manually mapping well sites. Now, geospatial AI evaluates millions of permutations to maximize leasehold coverage.
With 99% of records missing metadata, teams sifted unlabeled databases. Now, AI generates column descriptions for data experts to verify.
Manual document summaries bottlenecked experienced staff. Now, AI drafts preliminary risk assessments, freeing teams for complex advisory.