You're building interactive dashboards for a bookstore chain in Dar es Salaam, Tanzania — plotly charts for exploratory analysis and Metabase dashboards for daily use, designed for two audiences with different data literacy.
The discipline skills: interactive visualization with plotly (drill-downs, hover details, cross-filters), self-service dashboard design for users who explore data fluently and users who need the questions already answered, and verifying that interactive paths work at every filter combination without breaking promises.
The AI-direction lesson: planning before prompting. When a session juggles metric definitions, data quality rules, and dashboard configuration at the same time, AI loses track. The skill is decomposing the work before starting — deciding what AI needs for each piece, in what order, with what context. Context curation enters: feeding AI the metric definitions and quality rules produces measurably better output than "build me a dashboard." What you include in the session determines what AI can do.
Your Role
You're building the interactive dashboards. Two tools this time: plotly for interactive charts in Jupyter, and Metabase for the persistent dashboards the team visits daily. The choice between them is about who sees the output and how.
You're designing for two audiences from the same data. The owner gets an exploratory dashboard where she controls the drill-downs. Her managers get a guided view where the questions are already set up. That second design is the harder problem.
What's New
Last time, you built a Metabase dashboard with information hierarchy, accessibility features, and automated metric validation for Diego's gym chain. You designed for where the primary KPI sits, who can read the colours, and whether the layout survives a new location opening.
This time, the dashboard is interactive. Clicking a bar should answer a follow-up question. A filter that produces an empty result with no explanation breaks a promise. And the audience splits: one dashboard for someone who explores data fluently, another for someone who needs the questions already answered.
plotly is new. Plan mode is new. Both enter in the first build unit.
The hard part is the dual-audience design. A drill-down that works for Amina overwhelms a store manager who does not know what to click.
Tools
- Python 3.11+ (via Miniconda, "analytics" environment)
- DuckDB
- Jupyter Notebook
- pandas
- plotly (new — interactive visualisations)
- Metabase (via Docker — continuing from P3/P4)
- Docker
- Claude Code (plan mode introduced)
- Git / GitHub
Materials
- POS data export — a single CSV covering three stores, 18 months of transactions across books, stationery, and cafe items.
- Data dictionary — column definitions and business terminology for the POS export.
- Interactive dashboard design guide — interaction paths, self-service patterns, minimum-data-point thresholds, guided versus exploratory design.
- CLAUDE.md — project governance file with client context, work breakdown, and verification targets.