M05-05 · AI + Data & Decision Science

Business Intelligence and Dashboard Design

Teaches students to design, build, and maintain production dashboards that stakeholders actually use to make decisions. Covers BI tool proficiency, dashboard scoping through stakeholder interviews, metric definition documentation, and the critical distinction between exploratory analysis and production reporting. Students learn that a dashboard nobody uses is a failure, regardless of how beautiful it is.

25 Hours

7 Learning objectives

Create Bloom's ceiling (?)

4 Competencies

What You'll Be Able To Do

Learning Objectives

Objectives

Depth

Scope a dashboard project by interviewing stakeholders: what decisions it informs, who sees it, required metrics, time granularity, and filter dimensions — then negotiate scope to 7-10 actionable metrics Apply
Build production dashboards in BI tools (Looker/Tableau/Metabase): KPI cards, trend charts, comparison bar charts, detail tables, and interactive filters connected to dbt mart tables Apply
Write metric definition documentation for every dashboard element: name, definition, SQL source table, caveats, owner, and last-verified date Create
Evaluate whether an analysis need should be served by a production dashboard (recurring question, automated data) or an ad hoc notebook (one-time question, manual analysis) Evaluate
Implement data freshness indicators and stale-data alerts so stakeholders know when dashboard numbers are current vs. outdated Apply
Design self-serve analytics capabilities: saved queries, pre-built filters, and guided exploration that reduce ad hoc requests to the data team Create
Analyze dashboard usage patterns to identify underused dashboards for retirement and high-demand questions that need new dashboards Analyze

Levels: Remember · Understand · Apply · Analyze · Evaluate · Create — highest demands most original thinking.

Core Competencies

What You'll Master

Dashboard Scoping

Stakeholder interviews, metric negotiation, use-case validation, and preventing the "dashboard nobody uses" failure mode.

BI Tool Proficiency

Building, formatting, and maintaining dashboards in Looker, Tableau, or Metabase with production-quality data connections.

Metric Documentation

Writing definitions that prevent "why don't my numbers match?" conversations, with source tables, caveats, and ownership.

Exploratory vs. Production

Knowing when to invest in productionizing a recurring analysis vs. delivering a one-off notebook, and managing the transition between them.

Portfolio Artifact

What You'll Build

Production Dashboard with Documentation — Student builds a complete production dashboard in a BI tool: 7-10 metrics with KPI cards, trend charts, and filters, connected to a modeled data layer (dbt mart tables). Includes a stakeholder scoping document (who uses it, what decisions it informs, what was excluded and why), metric definitions for every element, a data freshness monitoring configuration, and a 1-page "how to use this dashboard" guide for non-technical stakeholders.

Tools You'll Use

Industry Tools, Not Toy Projects

Looker / Tableau / Metabase

Industry-standard BI platforms for building production dashboards with interactive filters and data connections.

dbt

Data transformation tool providing the modeled data layer (mart tables) that powers reliable dashboards.

SQL

Warehouse query language for building custom metrics and validating dashboard data against source tables.

Google Docs / Notion

Documentation platforms for metric definitions, stakeholder scoping documents, and dashboard guides.

Claude

AI assistant for dashboard design review, metric definition drafting, and stakeholder communication.