Ch 5 Practice — Q3 Revenue vs Plan — Schema ======================================================= Flat CSV in practice-ch5-revenue-plan.csv. One row per region. The richer nested form, including the chart-state descriptions, is in practice-ch5-revenue-plan.json. Columns ------- Column Type Description ------------ ------- ---------------------------------------------------------- region text Sales region (Americas, EMEA, APAC, LATAM). revenue_m decimal Actual Q3 revenue in millions of USD. plan_m decimal Q3 revenue plan in millions of USD. variance_m decimal revenue_m - plan_m. Negative means the region missed plan. variance_pct decimal variance_m / plan_m, as a percentage to one decimal. Row inventory ------------- - 4 rows — one per region. Total: 4 rows. Headline numbers ---------------- These are pinned by practice-ch5-revenue-plan-contract.test.mjs in the book repository. Any chart built from this data should reproduce them exactly. Claim Value -------------------- ------------------------ EMEA against plan 0.85 — exactly 85% EMEA shortfall $1.2M Quarter revenue $31.7M Quarter plan $32.2M Quarter variance under 2% Regions beating plan 3 of 4 Regional revenue sums to the quarter total, and regional plan sums to the quarter plan. Both reconciliations are tested. Deliberate design choices ------------------------- The aggregate has to look unremarkable. The quarter lands within 2% of plan while EMEA misses by 15%. If the total looked alarming, a reader would investigate regardless of how the chart was drawn, and the chapter's point would evaporate. The whole lesson is that clutter is what stops someone seeing a 15% regional miss inside a quarter that reads as fine. Three regions beat plan, one misses. A single outlier against a uniform background is the cleanest case for the one-accent-colour rule the decluttered version applies. Two or three misses would make the accent ambiguous. Values are in $M at the same scale as nadia-q3-region. Both are revenue-by-region figures, in Chapters 5 and 7. Holding them to a comparable scale means a reader moving between the two chapters is not silently recalibrating what a big number looks like. What this dataset is for ------------------------ Chapter 5's Practice with us problem. The reader is shown the chart as Copilot generated it, with every default decoration switched on, and asked to inventory each element as encodes / helps read / neither. The JSON records both chart states so the exercise can be checked: chart_states.cluttered.decorations lists what is present, and chart_states.decluttered describes what survives the removal pass. This is a practice-scope dataset, not one of the fourteen character datasets. No anchor character owns it. License ------- Released for teaching use alongside The Defensible Decision. Use freely.