Analytics

Build trusted executive and operational analytics directly on governed data.

Databricks SQL, AI/BI dashboards, Genie spaces, and semantic models only create value when the metrics underneath them are consistent. We design the analytical layer the business can actually use.

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Executives should not have to debate the numbers before they can debate the decision.

When every team calculates revenue, pipeline, or margin differently, dashboards become arguments. Moving BI onto Databricks without a semantic foundation reproduces the same fight on a faster engine.

Metric drift

The same KPI exists in three warehouses, two CRM reports, and a spreadsheet that finance actually uses.

Semantic layer as a slide

Definitions are documented in Confluence and ignored in SQL.

Self-service without guardrails

Genie and dashboards are opened on tables that were never modeled for business questions.

Outcomes

One semantic foundation

Metrics and business definitions remain consistent across dashboards, Genie, applications, and AI.

Executive-ready datasets

Gold models designed for grain, time, and the questions leadership actually asks.

Governed self-service

Analysts and operators can explore without creating a second version of the truth.

Capabilities

Databricks SQL
AI/BI Dashboards
Genie Spaces
Semantic models
KPI architecture
Analytical datasets
Executive dashboards
Embedded analytics
Self-service analytics

Analytics is a consumption contract on top of gold data—not a charting exercise.

We design KPI architecture, semantic models, and the Databricks SQL / AI/BI surface together so Genie and dashboards query the same governed definitions.

01
Define the decisions
Which operating and executive questions must be answered the same way every time.
02
Model the metrics
Grain, owners, source of truth, and the gold datasets that will carry them.
03
Publish the consumption layer
SQL warehouses, dashboards, Genie spaces, and permissions aligned to Unity Catalog.

Common scenarios

Executive reporting rebuild

Leadership reporting is still assembled manually because platform metrics cannot be defended.

Operational analytics

Teams need daily or intra-day views that match the same definitions used in the board pack.

Genie on governed data

Natural-language analytics is useful only after the semantic layer and permissions exist.

Why this approach

Definitions before dashboards

If the metric is not stable, visualizing it faster does not help.

Same numbers in every surface

SQL, AI/BI, Genie, and downstream applications should not each invent revenue.

Performance is part of trust

A correct dashboard that cannot be queried during a meeting will not become the system of record.

Questions

Do you replace our existing BI tool?

Not automatically. Some organizations standardize on Databricks SQL and AI/BI. Others keep an existing BI tool on governed gold tables. The architecture decides. The non-negotiable is a single semantic foundation.

What is Genie useful for?

Genie is Databricks' natural-language interface over Unity Catalog data. It is effective when spaces are built on modeled, governed datasets with business context. It is a poor substitute for undocumented raw tables.

Can analytics work start before migration is finished?

Yes, on the domains that already have trusted gold data. Waiting for a complete estate conversion is how reporting stays in spreadsheets.

Related services

Related insights

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Building Trusted Executive Analytics on Databricks

Executives do not need more dashboards. They need a small number of numbers that survive contact with finance, operations, and the next question.

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Databricks vs Traditional Data Warehouse Architecture

The useful comparison is not brand versus brand. It is whether one architecture can support engineering, warehousing, governance, and AI without copying data into a second estate.