What is Business Intelligence?BI
Business Intelligence (BI) is the practice of collecting, modelling, and visualising operational data so that decision-makers can answer business questions in seconds rather than building reports by hand each time.
BI sits between raw operational systems (ERP, CRM, accounting, marketing platforms) and the humans who run the business. A working BI environment has three layers: ingestion (data lands in one place), modelling (raw tables become business-meaningful facts and dimensions), and consumption (dashboards and embedded reports that answer recurring questions). The discipline most teams skip is the modelling layer — without it, every dashboard becomes a one-off SQL artefact that breaks the moment a source system changes. Codnity Data builds the modelling layer first; the dashboards follow.
What it includes
- Centralised data platform (Power BI, Azure Synapse, Snowflake, dbt-powered warehouse)
- Semantic model — business definitions encoded once, reused everywhere
- Role-level security so the right person sees the right slice
- Refresh schedule matched to source system update cadence
- Dashboards that answer recurring questions in one screen
- Self-service exploration for analysts without breaking the model
How it works
Audit decisions, not dashboards
List the 10 recurring questions leadership asks every week. The BI environment is engineered to answer those — not to mirror whatever each source system happens to export.
Land raw data in one place
Source systems push or pull into a centralised store on a defined schedule. No human Excel hop in the middle.
Build the semantic model
Star schema with fact tables for events (sales, leads, sessions) and dimension tables for context (date, customer, product, channel). Definitions agreed with the business, locked.
Wire the dashboards
One canonical dashboard per audience. Drill-throughs for exploration. Subscriptions for the regular review cadence.
Operate, observe, iterate
Track query patterns. Retire dead reports. Promote shadow analyses into the model when they prove valuable.
Frequently asked
BI vs analytics vs data science — what is the difference?
BI answers "what happened and what is happening". Analytics answers "why did it happen". Data science answers "what will happen and what should we do". Most growth-stage companies need BI first, analytics second, science later.
Power BI, Tableau, or Looker?
Power BI for Microsoft-heavy stacks and price/performance — most of our clients. Tableau for power-user analyst teams that already love it. Looker for product-engineering teams that want LookML as code.
How long does a BI rollout take?
A focused first phase (one business unit, one semantic model, three dashboards) is six to twelve weeks. Full-org BI is iterative — never "done", always evolving with the business.