SAMPLE: Vacation Races × BI Consulting Services — Race Series Performance Board

Season scorecard — 2026 vs 2025

As of 4 Sep 2026 · completed + upcoming events

①

Registration pacing — cumulative, 2026 vs 2025

24 half-month periods · Jan–Dec

Solid line is 2026 to date (through early September); dashed line is 2025 over the same calendar. Bars show registrations taken in each half-month of 2026.

2026 cumulative2025 cumulative (same period)2026 per period

②

What this board answers

Three questions from the brief

③

The itinerary — 2026 race calendar across the parks

Bubble = registrations · Route = calendar order

Every event on the published calendar plotted where it is run. The dashed route joins the season in date order; the strip beneath is the same season as a timeline. Hover a park for its numbers.

Completed 2026Upcoming (registration open)Filtered out

④

Events ranked — registrations & net revenue

By event · vs prior year

Net registration revenue after refunds. Upcoming events show registrations to date against the same point in 2025.

⑤

Cost per finisher by event

Direct event cost ÷ finishers

Permits, shuttles, timing, medals and aid stations divided by finishers. Completed events only — upcoming events show budgeted cost against projected finishers.

⑥

Refunds & deferrals

Share of registrations · by event

Refunds leave the season; deferrals roll to next year's event and stay on the balance sheet as deferred revenue.

⑦

Merchandise & add-ons

Shuttles · iTab medals · photos · merch

Revenue on top of the entry fee, with attach rate (share of registrants who bought each add-on).

⑧

Finance close view — recognised vs deferred

Month-end · Jan–Aug 2026 actual, Sep–Dec plan

Entry fees are recognised when the event runs; everything collected for a future start sits in deferred revenue. Days-to-close is the finance team's own lag from month-end to books closed.

Recognised in monthDeferred balance at month-endDays to close

⑨

Ask the data

AI layer · mock

A natural-language question answered from the same model that drives this board — no exports, no pivot tables.

Which events are pacing behind last year, and how much revenue is at risk if they finish at today's gap?

⑩

Private AI on your own server

The "ask the data" panel above is built to run on a GPU server inside your own network: an open-weight model over your own documents, with every answer citing the file it came from. No per-seat licence, works offline, and nothing leaves the building.

How it sits in your environment

One appliance-class server on your rack or in a secured closet. Your files are indexed where they already live; the model runs locally; answers come back with the source page attached.

What that means in practice

1
Your data only, on your hardwareAn open model over your own documents and exports. No third-party AI processor, no data-transfer agreement to review.
2
Every answer cites its sourceReplies point to the file and page they were drawn from, so people check the answer instead of trusting it blind.
3
No per-seat licenceThe whole team can ask questions. Cost is the server and our build, not a monthly number that grows with headcount.
4
Works offlineIf the internet connection goes down, the answers still come. Nothing depends on a vendor's uptime or pricing changes.