Business intelligence your leadership team actually uses

Most BI programmes stall for the same reason. The dashboards were built before anyone agreed what the numbers mean. Brainforge fixes that order: assess the estate, model the data, govern the definitions, then build the reporting on top.

In plain terms

What Business Intelligence means in practice

Brainforge delivers business intelligence consulting services that fix the foundation under your dashboards. We assess your BI estate, model the data, govern metric definitions, and build reporting your leadership team trusts.

Where teams get stuck

Assess, model, and govern the BI layer so leadership decisions stop depending on spreadsheet pulls.

Leadership asks for a number and gets three different answers from three different dashboards.

Reporting depends on spreadsheet exports, copy and paste, and whoever remembers how the query was built.

We bought a BI tool and adoption never happened, so the licence spend is not earning anything.

The warehouse exists but business teams still cannot answer basic questions without an analyst.

Nobody owns metric definitions, so every new dashboard re-litigates the same arguments.

What we deliver

Core Business Intelligence

BI assessment and strategy

A review of your reporting estate, tooling, and metric definitions, with a sequenced plan for what to preserve, rebuild, or retire.

Warehouse and data modelling

The modelled foundation behind trusted reporting, built on Snowflake, BigQuery, Databricks, or your existing warehouse.

Semantic layer and metric governance

One agreed definition per metric, with owners, so two teams stop reporting two different numbers for the same word.

Reporting and self-serve enablement

Governed dashboards and the training that lets business teams answer their own questions without waiting on an analyst queue.

How deep it goes

Capabilities behind the work

The same delivery primitives (context, controls, and review) show up across every engagement.

BI assessment and estate review

We find out what you have, what it costs, and what is worth keeping.

Reporting estate inventoryTool and licence reviewDashboard rationalisationMigration risk mapping

Data modelling and warehouse foundation

The modelled layer that makes reporting consistent instead of dependent on one person's query.

Dimensional modellingdbt or warehouse-native transformationData quality testsDocumentation and lineage

Metric definition and governance

Agreeing what each number means before anyone builds a tile.

Metric definition workshopsSemantic layer designDefinition ownership and review cadenceReconciliation against source systems

Reporting and dashboard delivery

Governed reporting built on the foundation, not stapled on top of it.

Executive and operational dashboardsRole-based reportingAutomated refreshAdoption measurement

Team enablement

Leaving your team able to run and extend the system without us.

Analyst onboardingDocumentation handoverSelf-serve trainingGovernance operating model

The stack we build with

Tooling our team runs in production

Frameworks, platforms, and infrastructure we implement, integrate, and operate end to end.

Warehouses

SnowflakeBigQueryDatabricksRedshift

Transformation

dbt

BI and semantic layers

OmniLookerTableauPower BISigmaRill

Data activation and quality

FivetranPolytomicHightouch

What changes

Outcomes you can point to

A reporting estate assessment that names what to keep, rebuild, and retire.
A modelled data foundation with documented, tested transformations.
One governed definition per metric, with a named owner and a review path.
Dashboards leadership checks daily, not dashboards that get exported once a quarter.
Business teams enabled to self-serve on trusted data.

Proof in production

See where this work shows up.

Clean data for smarter shipping ops

Stella Source unified quoting, CRM, and operations data into a governed analytics hub with role-based dashboards, cutting quote turnaround 60%.

Read the governed analytics hub case study →

From single warehouse to a nationwide fulfillment advantage

How we built a unified shipment data model that gave full visibility across 3PLs and carriers and cut average shipping cost 15%.

Read the fulfillment data case study →

Common questions

Business Intelligence, straight answers

What does business intelligence consulting cost?

Engagements start with a scoped assessment that produces an estate review, a prioritised plan, and metric definition recommendations. Most teams begin there and then sequence the modelling and dashboard work from what the assessment finds.

Should we buy a new BI tool before hiring consultants?

No, and we will tell you that directly. Tool selection before metric definition is the most common way BI programmes waste a licence. We assess what you have first, and often the answer is to use your existing tool properly rather than replace it.

Do you replace our existing warehouse or BI platform?

Usually not. Most engagements build on the warehouse and platform you already own. When a migration is genuinely warranted, we map it as a bounded sequence rather than a wholesale replacement.

How is this different from hiring a BI developer or analyst?

A hire works inside whatever foundation exists. A consulting engagement fixes the foundation, the metric definitions and the modelling, then enables your team to run it. That is faster than continuing to work around numbers nobody trusts.

Do you work with Power BI, Tableau, and Looker?

Yes, along with Omni, Sigma, and Rill. We are not tied to one platform. The recommendation follows your team, budget, and what you already run.

How do you handle metric definitions across teams?

We run definition workshops with the people who argue about the numbers, convert the outcome into a governed semantic layer, and assign a named owner per metric with a review cadence. Definition drift is a process problem before it is a tooling problem.

How we work