Five minutes with mrr_usd in saas-subscriptions

Someone asked what is in mrr_usd on saas-subscriptions, and the honest answer took one query.

2,500 rows, no nulls, 2,269 distinct values. Monthly recurring revenue at last observation.

The five-number version: min 11, p10 13, median 88, p90 930, max 9,751. The mean is 447.

The p10 to p90 band — 13 to 930 — is where ordinary rows live, and it is the pair worth quoting when somebody asks what to expect. Min and max describe the two strangest rows in the table and nothing else; they are useful for spotting impossible values and misleading for everything else.

select
  count(*)                  as rows,
  count(mrr_usd)            as present,
  count(*) - count(mrr_usd) as nulls,
  count(distinct mrr_usd)   as distinct_values
from saas_subscriptions;

The mean sits 410% above the median, which is the whole story: anything that reports the average of mrr_usd is reporting a number most rows are below. Put the median next to it or drop the mean.

Where this bites: a filter like mrr_usd > 447 reads as "above average" and selects a minority of rows — a smaller minority than the phrase suggests to whoever asked for it. If the request was "the typical ones", the threshold they meant was 88.

The grain is one row per account, which is the context every one of those numbers depends on. None of them survive a change of grain, which is why "profile the column" and "profile the table" are the same job. Full schema, and the CSV, on the dataset page.