Business Intelligence (BI)

A practical example

Example: a marketing team connects its warehouse to a BI tool, defines metrics like pipeline-generated and cost per lead in the semantic layer, and builds a dashboard where channel managers explore campaign performance without writing SQL.

What to evaluate before investing

• Test self-service against your real data model: can non-technical users answer their own questions without creating conflicting metric definitions?
• Ask how the semantic layer is governed, since duplicated or ad-hoc metric definitions are the most common BI failure mode.
• Verify embedding and alerting options if dashboards must live inside other tools or trigger workflows on threshold changes.

Limitations and tradeoffs

BI reports what happened in data you already have; it does not fix collection gaps or unify fragmented sources.

Data pipeline quality determines BI value, so evaluate your warehouse and tracking maturity before choosing a platform.