Business Intelligence
Semantic models, reporting and visualization that turn governed data into decisions people actually make.
How data reaches a decision
What it is
Understanding Business Intelligence
Business intelligence is the layer between a data platform and a decision: a semantic model defining what each metric means, and reporting built around the decisions people have to make. The visualization tool is the least consequential part, because the definitions underneath it determine whether the numbers are trusted.
Why it matters
A governed semantic layer is the practical fix for the most common data complaint in any organization, which is that different teams report different numbers for the same thing. Defining a metric once, in one place, is what makes reporting arguable about strategy rather than about arithmetic.
Problems it addresses
- “Every team reports a different number for the same metric.”
- “We have hundreds of dashboards and nobody knows which is current.”
- “Reporting is rebuilt by hand every month.”
- “Nobody can explain how a figure on a board pack was calculated.”
Capabilities
Semantic modeling
One governed definition per business metric, consumed by every report rather than re-implemented in each.
Report and dashboard design
Built around a decision and a cadence, not around what was easy to extract.
Self-service enablement
Curated datasets and guardrails so business teams can answer their own questions safely.
Report estate rationalization
Retiring the accumulated long tail of unowned dashboards.
Embedded analytics
Reporting delivered inside the application where the work happens.
Use cases
Metric standardization
One agreed definition of revenue, churn or utilization across the organization.
Executive reporting
A board pack generated from governed data rather than assembled manually.
Operational dashboards
Near-real-time views for the teams running a process.
Before you commit
Prerequisites, governance and risks
The questions that determine whether this will work in your environment, rather than in a demonstration.
What has to be true first
- A governed data platform underneath
- Agreed metric ownership
- Willingness to retire reports, not only add them
Governance
Certification and ownership. Every published report needs an owner and a review date, and the estate needs a retirement path, otherwise the dashboard sprawl that BI was meant to fix simply moves to a new tool.
Risks
- Dashboard sprawl without ownership
- Metric definitions drifting between reports
- Self-service without guardrails producing confident but wrong analysis
Where Business Intelligence leads
Every connection below is recorded in the content itself, and each one says why it is here.
Capabilities
Technologies
- dbtCommonly considered alongside Business Intelligence.
- SnowflakeCommonly considered alongside Business Intelligence.
- DatabricksCommonly considered alongside Business Intelligence.
- Microsoft FabricCommonly considered alongside Business Intelligence.
- Data GovernanceCommonly considered alongside Business Intelligence.
Sectors
Explore solutions using Business Intelligence
Open the guided finder with this technology already selected, and see which capabilities apply to your goal, sector and environment — with the reasoning shown.
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