Business intelligence (BI) covers the processes and software that turn an organisation's data into reports, dashboards and analyses used to monitor performance and guide decisions. Typical flow: data is extracted from operational systems, loaded into a warehouse, modelled into agreed metrics and then explored with BI tools.
Why it matters
BI gives teams a shared, trusted view of sales, operations, finance and customers, replacing scattered spreadsheets. It is mostly descriptive and diagnostic: what happened and why. Predictive work is usually grouped under data science. Its value depends on data quality and consistent metric definitions; two dashboards calculating "revenue" differently erode trust quickly.
Example
Source: orders database -> ETL -> data warehouse
Model: monthly_revenue = SUM(order_total) grouped by month
Report: dashboard showing revenue trend, top regions, refund rate
Users: filter by date and region without writing SQLCommon mistakes
- Building many dashboards no one uses instead of tracking a few key KPIs.
- Letting each team define metrics separately.
- Ignoring data freshness, so reports look current while the data is days old.
Related
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