Dashboards and Business Intelligence

About this course

Most companies have hundreds of dashboards and still argue about the numbers. This course shows how good BI teams avoid that: a clean reporting model in the warehouse with the grain of every table written down, layered SQL transformations with tests, metrics defined once in a semantic layer, dashboards designed around the decisions people make, pipelines that tell you when a number is late or wrong, and self-service that does not turn into a thousand forgotten reports.

Every Monday, Mangrove Market's leadership team looks at three different sales numbers: finance's net sales, store operations' till sales and the app team's order value. With Tomás, the data analyst, you rebuild the weekly trading dashboard for 320 stores and the app: a star schema for sales, stock and orders, dbt-style models and tests, one metric dictionary that finance and operations sign, a dashboard that passes the five-second test, freshness and volume checks, and a plan to retire the reports nobody opens.

The modelling and metric lessons are tool-neutral SQL that runs in your browser. The dashboard lessons apply to any BI tool, such as Power BI, Looker Studio, Looker, Tableau or Metabase, and note where they differ.

What you will be able to do

• Turn reporting questions into a star schema with a declared grain for every fact table and conformed dimensions
• Build layered, tested and documented SQL models in the warehouse in the style of dbt
• Define metrics once, with owners and exact rules for additivity, so every report agrees
• Design dashboards around the decisions their users make, and test them with real users
• Keep data fresh and trusted with schedules, freshness and volume checks, alerts and an incident routine
• Run self-service reporting with access rules, certified content, AI-assisted questions and a way to retire old dashboards

Syllabus

6 stops, about 3.5 h in total. Each stop fills in when you complete the lesson.

1. 1

   From questions to a reporting model

   Every Monday, Mangrove Market's leadership sees three different "sales" numbers for the same week. This first lesson of the course starts the rebuild of their weekly trading dashboard at the bottom: you turn the decisions people make into questions, and the questions into a reporting model of facts, dimensions and a declared grain that any later definition can be computed from.

   40 minIntermediateEpisode2 exercises

   Business intelligenceDimensional modellingSQL

2. 2

   Transforming data in the warehouse

   Raw tables from the tills and the app are not ready for a dashboard. This lesson shows how analytics teams turn them into the tested star schema you designed in From questions to a reporting model: SQL models in layers, built in the right order, checked by tests on every run, documented and reviewed like code.

   35 minIntermediateEpisode2 exercises

   dbtSQLData qualityDimensional modelling

3. 3

   Metrics everyone agrees on

   Every Monday, Mangrove's leadership sees three different "sales" numbers, and each one is correct for the team that made it. This lesson shows you how to take a metric apart, explain the gap between two numbers with a bridge, agree one definition with the people who own it, handle measures that can't be added up, and write each definition once so every dashboard gives the same answer.

   40 minIntermediateEpisode2 exercises

   Semantic layerBusiness intelligenceSQLData governance

4. 4

   Designing a dashboard for decisions

   A dashboard earns its place when the people who open it decide something faster. This lesson shows you how to design one around its users and their decisions: what goes on it, where it goes, which comparisons and colours it uses, and how to test a wireframe before you build anything.

   35 minIntermediateEpisode1 exercise

   Dashboard designBusiness intelligenceSQL

5. 5

   Fresh, tested, trusted

   A dashboard is only useful if people believe it on Monday morning. This lesson shows you how to run the chain behind the weekly trading dashboard in the right order, promise and show how fresh the data is, catch missing and odd data with volume checks, send alerts to someone who acts, and handle the morning when a number is wrong.

   35 minIntermediateEpisode1 exercise

   Data qualitySQLdbtData governance

6. 6

   Self-service without chaos

   Self-service means people answer their own questions without waiting for the data team. Done carelessly, it gives you hundreds of dashboards, five versions of "sales" and data in front of people who should not see it. This lesson shows the controls that let many people explore safely: row-level access, certified and sandbox content on governed datasets, plain-language questions with checks, and a routine for retiring what nobody uses.

   35 minIntermediateEpisode1 exercise

   Data governanceSemantic layerBusiness intelligenceSQL

7. Capstone: Rebuild the weekly trading dashboard

   A realistic project with a rubric and a model answer.

8. Final exam and certificate

   10 questions drawn at random, 70% to pass.

Capstone project

Check what you know

Questions