BI (Business Intelligence) is a system making data useful for the team on a constant basis. Let us cover: 1) What BI is: — Turning data into CLEAR, accessible metrics and dashboards that the team uses for decisions. — The goal: so decisions are made ON DATA rather than guesses, and so data is available not only to the analyst but to everyone who needs it. 2) BI vs a ONE-OFF ANALYSIS (the key difference): — A one-off analysis (from the analytics course): one task, an analyst digs into the data, gives an answer to a specific question. Once. — BI: a CONSTANT SYSTEM. Not "answered and forgot", but live tools working continuously: • DASHBOARDS: screens with key metrics and charts that people look at REGULARLY (every day/week) to see the state of things. • TRACKED METRICS/KPIs: indicators the team follows over time. • ACCESS FOR THE TEAM: not only the analyst but managers, leaders, specialists see the data they need. • SELF-SERVICE: the ability for people to get answers to data questions themselves, without bothering the analyst each time. — BI is about data CONSTANTLY informing the decisions of many people, not about a single analysis. 3) BI components (the course topics): — METRICS AND KPIs: which indicators matter, how to define them correctly (the first module). — DASHBOARDS: how to build clear, useful panels with metrics and charts (the second module). — SELF-SERVICE BI: how to give the team the ability to ask the data questions themselves (the third module). — DATA STORYTELLING: how to convey the meaning of data rather than just show numbers (the fourth module). 4) How AI helps BI (makes it more accessible): — BUILD DASHBOARDS: AI helps create dashboards, pick metrics and charts, write data queries (code from a description, from the analytics course). — ANSWER QUESTIONS IN WORDS: AI lets you ask data questions in NATURAL language ("what are sales by region this month?") and get an answer/chart — this is powerful for self-service (non-technical staff get answers without SQL). — EXPLAIN AND SUMMARIZE: AI helps explain what the metrics show, highlight the main thing, formulate conclusions (data storytelling). — AUTOMATE: regular reports, summaries, alerts on metric changes. — AI lowers the barrier: BI becomes more accessible both for those who build it (easier to create) and for those who use it (you can ask in words). 5) But the analytics principles PERSIST (important): — Data quality, honest metrics, verification (garbage in to garbage out), interpretation traps (correlation is not causation), honest visualization — all from the analytics course applies to BI too, but ON A CONSTANT BASIS and for MANY people. An error in a dashboard the whole team uses is costlier than an error in a one-off analysis (many see it and decide on it). — AI speeds up, but the right metrics, honest dashboards, data verification are with people, as in analytics. Practical rule: BI is a system turning data into clear metrics and dashboards for CONSTANT use by the team so decisions are made on data. Unlike a one-off analysis (one task, an analyst), BI is live dashboards, tracked metrics, access for the team, self-service. AI makes BI more accessible: it helps build dashboards, answer data questions in words (self-service for the non-technical), explain and automate. But the analytics principles (data quality, honest metrics and visualization, verification, interpretation traps) persist and are even more important because a dashboard is used by many people for many decisions. The course will cover metrics/KPIs, building dashboards, self-service BI and data storytelling — how to make BI useful and honest with AI. A good BI system turns data from "what the analyst looks at" into "what the team makes decisions on daily".