Analytics & Business Intelligence

What is the difference between Analytics and BI?

Business Intelligence (BI) has historically focused on descriptive reporting and dashboards: it answers "what happened?" Analytics also covers diagnostic ("why?"), predictive ("what will happen?"), and prescriptive ("what should we do?") approaches. In practice, the two terms are often used together.

Why learn SQL for analytics?

SQL remains the universal language for querying structured data, regardless of the tool. An analyst who masters advanced SQL (CTEs, window functions, complex aggregations) can work on BigQuery, Snowflake, Redshift, DuckDB, or any cloud warehouse without relearning basics.

What is a semantic layer?

A semantic layer is an abstraction between raw tables and BI tools that defines shared metrics, dimensions, and calculation rules. It ensures all dashboards use the same KPI definition. Tools like dbt Semantic Layer, Cube.js, or LookML implement this.

How do you choose good KPIs?

A good KPI must be measurable, tied to a strategic goal, actionable (a change should trigger a decision), and understandable by stakeholders. The most common mistake is tracking too many indicators: 3 to 5 key metrics per level (team, department, company) is usually enough.

Which deck should I start with?

Start with Analytics and BI Fundamentals, then SQL for Data Analysis — these are prerequisites. Then Statistics, KPIs & Metrics, Data Visualization, and finally Semantic Layers & Self-Service BI.