The category that eats the chart

region on saas-subscriptions has 4 values, and one of them is 46.5% of the data.

• AMER — 1,162 rows, 46.5%
• EMEA — 744 rows, 29.8%
• APAC — 427 rows, 17.1%
• LATAM — 167 rows, 6.7%

The top three take 93.3% between them. With only 4 values there is no tail to worry about, which makes this a genuinely easy column to chart.

select region,
       count(*)                                      as rows,
       round(100.0 * count(*) / sum(count(*)) over (), 1) as pct,
       round(avg(mrr_usd)::numeric, 2)             as avg_mrr_usd
from saas_subscriptions
group by 1
order by rows desc;

The second column is the one that matters. Share of rows tells you what is common; avg_mrr_usd tells you whether the common thing is the important thing. They disagree more often than not, and a chart that shows only the first is answering the easier question.

Decide what happens to the tail before you plot it. "Other" as an explicit bucket is honest; twelve slivers is not, and neither is silently taking the top eight.