CloudRevenue - Teaching Dataset
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Synthetic cloud subscription revenue across 8 product families x 247 tenants x 36 months. Designed to support practice for the chart-selection, dashboard-design, and AI-direction chapters of The Defensible Decision.

Wholly synthetic. Any resemblance to a real company's data is coincidental.

Files
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- products.csv - 8 product families
- tenants.csv - 247 enterprise tenants
- revenue.csv - ~21,400 rows of tenant x product x month revenue
- SCHEMA.txt - full schema and known imperfections

What it represents
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A 3-year window (2023-06 through 2026-05) of monthly cloud subscription revenue for a fictional B2B cloud platform vendor with 8 product families spanning Data, Infrastructure, Security, Operations, and Productivity segments. The tenant base is 247 enterprises across 4 regions and 8 verticals, with realistic size-band distribution skewing toward SMB and Mid-Market.

The dataset is sized to support real practice: aggregate views, drill-downs, time-series analysis, and segmentation all return non-trivial result sets. Joins across the three tables are inexpensive.

What it does NOT represent
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- Customer-level contact information (none present by design)
- Cost data (revenue-only)
- Product feature usage (MAU only, no feature-level telemetry)
- Pipeline / forecast (closed revenue only)

Deliberate imperfections
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This dataset includes the kinds of imperfections real data has. Chapters 4, 5, 9, and 13 reference the cleanup work explicitly.

- Region naming inconsistency - ~12% of NA tenants stored as North America instead of NA
- Null MAU - ~2% of revenue rows have null MAU (telemetry outage)
- In-flight billing close - last 2 months (2026-04 and 2026-05) have ~30% null revenue
- Mid-window churners - 12 tenants churned between 2024-06 and 2025-08; revenue stops at churn_month

The imperfections are intentional. A chapter exercise that pretends the data is perfect is teaching the wrong discipline.

Quick start
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# Inspect the schema
cat SCHEMA.txt

# Sample the data
head revenue.csv
head tenants.csv
head products.csv

# Load into Power BI:
# 1. Get Data -> Text/CSV -> select all three CSVs
# 2. Power Query auto-detects column types
# 3. Build relationships: revenue.tenant_id -> tenants.tenant_id (many-to-one)
#                        revenue.product_id -> products.product_id (many-to-one)
# 4. Mark "month" as date hierarchy

Reference BRD
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The CloudRevenue starter BRD is the BRD for this dataset. Chapters 2 and Appendix E use it as the practice artifact for the BRD-authoring discipline.

License and reuse
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Released for teaching use alongside The Defensible Decision. Use freely in classes, workshops, and your own practice. No attribution required beyond what's already in this README.