Term

Business Intelligence (BI)

Business Intelligence is the category of strategies, processes, and technologies that turn structured business data into reports, dashboards, and ad-hoc queries that humans read to make decisions. I draw a hard line between three things that vendor decks blur. BI is human-readable consumption of structured data. Analytics is the broader umbrella that also covers statistical and predictive work. Data Science is modelling-first, ML-adjacent, and ships outputs other software consumes more often than dashboards a CFO reads.
Reviewed by 7wData
Mentioned Data Warehouse

Why it matters

BI is where the org’s data investment becomes visible to non-data people. A great data warehouse without a BI layer is a data team’s project, not the org’s asset. For AI, BI dashboards are the most common surface where model outputs land in front of business users: a churn score, a forecast, a next-best-action recommendation. AI-augmented BI (natural-language query, auto-insights, narrative generation on top of charts) is the dominant 2025-2026 product evolution in this category, and the layer where most non-technical users first meet a model output in production.

Where you’ll encounter it

Three contexts. A vendor pitch positions a “BI platform” (Tableau, Power BI, Looker, ThoughtSpot, Mode, Sigma) and the differentiator is usually a mix of governance, semantic layer, and AI-augmentation story. A CFO asks “where is our BI” and the honest answer is the dashboard portal, not a strategy deck. An internal RFP picks between BI tools on a multi-quarter timeline, because migration cost is real and switching is rare. One thing worth saying: BI maturity in an org is a stronger predictor of analytical decision-making than data-team headcount. A small team with a working BI layer beats a large team without one.

Part of the 7wData AI Glossary. Tracking how concepts like this move in the expert conversation: daily signals at ins7ghts.com.