Sponsored By: Glean | Build with Genie Code and Glean: How Enterprise Context Makes Analytics More Actionable

For teams leaning into conversational self-service analytics with AI/BI Dashboards and Genie, Glean adds a business-aware layer that grounds vague asks in the right data, definitions and intent. The result is faster build cycles, fewer context switches and dashboards stakeholders can trust.

Why business context is the missing link in self-service BI

Dashboards only drive decisions when they reflect how the business talks and operates. Analysts need to know which definitions matter to which teams, how to slice for regions or segments and what outcome a requester expects. Without that connective tissue, updates become scavenger hunts across tools, and ad hoc investigations rarely mature into reusable assets.

Pairing Databricks' tables and metrics with enterprise context addresses that gap. Analysts can start with a fuzzy prompt and arrive at a concrete KPI view that matches the requester's vocabulary, priorities and access rights.

Inside Glean's context layer, enterprise graph plus personal graph

Glean assembles two graphs to model how work actually happens. The enterprise graph maps an organization's tools and content, from Workday org structures to Salesforce opportunities and Zendesk tickets. The personal graph tracks how each user engages with that content, including projects, collaborators and communication style.

Combined, these graphs tailor answers to the asker and the business. Glean inherits native source permissions and cites where information comes from, so guidance aligns to what a user is authorized to see and can be traced back to its origin.

How the Genie integration works in the Databricks Platform

Glean exposes an MCP server that Genie can call from within the Databricks Platform. When a request arrives, such as "build a dashboard for Frasier Automotive" or "update the support escalation health view," Genie can invoke Glean to retrieve relevant enterprise context. That can include account ownership, internal KPIs, recent communications and the tables most likely to contain the right signals.

Armed with that context, Genie generates or edits dashboards faster and with better defaults. Analysts can ask clarifying questions in place, such as definitions for churn risk or which segments a leader cares about, then immediately fold the answers into widgets, scorecards or labels. Because the integration pulls meaning from email, docs and CRM while keeping analytical data in Databricks, teams iterate without hopping between systems or restating the "why" behind every change.

Where teams get the biggest lift

• Net‑new builds from fuzzy prompts. Vague asks become actionable plans once intent, KPIs and data scope are surfaced from organizational context.
• In‑place updates without hunting for context. Change requests in email or chat flow into Genie, so metric tweaks and label changes happen where the work is done.
• Reusable notebooks and queries. Once a view works for one segment or region, Glean helps transpose the logic to another by supplying the shifted business context.
   

Put the "why" next to the "what" to ship trusted dashboards faster

Analytics teams spend too much time reconstructing intent. Embedding Glean's enterprise context inside Genie keeps meaning attached to metrics, so builders move quickly and stakeholders see outputs that mirror how the business thinks. Better context, combined with Databricks' data foundation, leads to cleaner definitions, faster iteration and higher confidence in decisions, echoing Databricks' broader perspective on AI‑driven business intelligence.