Reported impact
Business outcome
Reported outcomes include 90% Reduction in analyst backlog for data requests; 100+ Employees empowered with near-instant, self-serve analytics.
DatabricksEnergyEnterprise Knowledge SearchReal-time AnalyticsAI and Machine LearningData AnalyticsData PlatformGenerative AI / LLM2025 DatabricksDB#ff3621EnergyNorth AmericaHistorically, business intelligence was centralized: analysts used tabular perspectives and legacy reporting tools, which became increasingly obsolete as data volumes and demands surged. “We would get requests from…100+ Employees empowered with near-instant, self-serve analyticsSep 17, 20256Enterprise Knowledge Search|Real-time AnalyticsGenie · Microsoft Teams · Unity Catalog0 2025-09-17 6
Williams
Enterprise Knowledge Search · Real-time Analytics
Historically, business intelligence was centralized: analysts used tabular perspectives and legacy reporting tools, which became increasingly obsolete as data volumes and demands surged. “We would get requests from executives or business leads — what are our top 25 customers or what was asset utilization last quarter — but each answer required days of coding, iterative validation, and collaboration between technical and non-technical stakeholders,” shares Irma Glover, Commercial Technology Manager at Williams.
100+ Employees empowered with near-instant, self-serve analytics
90% Reduction in analyst backlog for data requests
Solution
Williams, a premier provider of large-scale natural gas infrastructure across North America, connects the continent’s growing supply with surging global demand for reliable, cleaner energy. With more than 30,000 miles of pipelines, 6,000 employees, and over 962 commercial customers, Williams manages immense transaction volumes spanning upstream, midstream, and downstream operations. Quick, actionable insights are key to their goal of helping commercial representatives understand customer needs. By implementing Databricks Genie , Williams empowered 100+ employees across business areas to better understand customer needs and behaviors faster. The result: democratized analytics, faster answer-turnaround, and a strong data-driven culture that adapts to rapid changes in energy demand and regulatory requirements.
Data flow
Operational, customer, and transactional data lands in Databricks, is transformed in the lakehouse under Unity Catalog governance, and is served to downstream users and applications through Genie, Microsoft Teams.
Solution architecture
- Serving
- Genie Lets business users ask natural-language questions over governed enterprise data.
- Microsoft Teams Acts as the conversational delivery channel for the data assistant experience.
- Governance
- Unity Catalog Applies centralized governance, lineage, and access controls across shared data assets.
Architecture clues
- Natural-language analytics is exposed through Genie.
- Runs on Azure.
- Uses Unity Catalog for centralized governance and access control.
Evidence from the source
100+ Employees empowered with near-instant, self-serve analytics
90% Reduction in analyst backlog for data requests
Quick, actionable insights are key to their goal of helping commercial representatives understand customer needs.
Williams, a premier provider of large-scale natural gas infrastructure across North America, connects the continent’s growing supply with surging global demand for reliable, cleaner energy.
With more than 30,000 miles of pipelines, 6,000 employees, and over 962 commercial customers, Williams manages immense transaction volumes spanning upstream, midstream, and downstream operations.
Transparency Evidence & methodology
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
How scores are calculated
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Customer reference value96/100
- Problem clarity20/20Measures whether the customer's operational problem is specific enough to compare.
- Implementation depth25/25Rewards named components, data flow, integrations, and architecture detail.
- Outcome specificity16/20Measures whether the reported benefit is concrete rather than a generic claim.
- Quantified impact25/252 metrics describe measurable impact.
- Deployment scale10/10Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
- Source authority20/25The main evidence is an official vendor-published customer story.
- Claim traceability21/2510 of 12 key fields link to a named source.
- Direct evidence20/205 direct source quotes retained.
- Date provenance10/10The publication date comes from the source page or publisher.
- Source transparency8/101 source reference document publisher, URL, authority, retrieval, and supported fields.
- Implementation specificity10/10Rewards source-backed components, data flow, quantified impact, and a specific outcome.
Traceability
Sources & provenance
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
- https://www.databricks.com/customers/williamsDatabricksFirst-party
Williams customer story
Supports- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes
- Publication date
Date evidence
- Published on source page Sep 17, 2025 · Databricks · First-partyOpen date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.
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