saas-subscriptions.industry: 8 values, 39.8% in the top three

Distribution of industry on saas-subscriptions, because every chart built on it inherits this shape.

• healthcare — 334 rows, 13.4%
• public sector — 334 rows, 13.4%
• software — 326 rows, 13.0%
• retail — 314 rows, 12.6%
• logistics — 307 rows, 12.3%

The top three take 39.8% between them. The remaining 5 share 60.2%, which is the part that gets rendered as an unreadable stack of slivers if you plot all of them.

select industry,
       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.