Datasets / Generated

Support tickets

A year of tickets with topics, priorities and response times.

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3,358 rows8 columns Generated

Worked examples

Each of these is a real run: the pipeline picked the columns, shaped the rows and chose the form. Open the trace to see what it decided and why.

Median first response, by priority

The median, not the mean: response times have a long tail and one ticket left over a weekend moves an average and not a median.

what is the median first response time by priority? — 4 rows charted from 3,358, drawn as a bar

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How it decided

Finding the data67 ms

Support tickets: 3,358 rows, 8 columns. priority and first_response_hours.

Shaping the data9 ms

3,358 rows in, 4 out across 2 columns. The median, not the mean: response times have a long tail and one ticket left over a weekend moves an average and not a median.

Choosing the chart6 ms

bar. Few categories, one measure, an obvious ordering: bars.

Applying defaults1 ms

Palette, spacing, axis titles and legend placement applied. 0 issues found.

Ticket volume by topic

A count of rows per topic - the question asks about volume, so nothing is averaged and nothing is weighted.

which topics generate the most tickets? — 10 rows charted from 3,358, drawn as a hbar

Ask this yourself See the working

How it decided

Finding the data60 ms

Support tickets: 3,358 rows, 8 columns. topic, counted. No other column is needed for a volume question.

Shaping the data15 ms

3,358 rows in, 10 out across 2 columns. A count of rows per topic - the question asks about volume, so nothing is averaged and nothing is weighted.

Choosing the chart5 ms

hbar. Topic names are sentences, so the bars run horizontally rather than tilting the labels 45 degrees.

Applying defaults1 ms

Palette, spacing, axis titles and legend placement applied. 0 issues found.

Schema

8 columns, typed as the pipeline sees them — which is how it knows what can go on a time axis and what can be summed.

Column	Type	Example
ticket_id	number	5001
opened_at	date	2025-01-01
topic	text	Login
priority	text	Urgent
first_response_hours	number	1.14
resolution_hours	number	5.12
satisfaction	number	4
reopened	boolean	False

First 8 rows

ticket_id	opened_at	topic	priority	first_response_hours	resolution_hours	satisfaction	reopened
5001	2025-01-01	Login	Urgent	1.14	5.12	4	False
5002	2025-01-01	Performance	Normal	13.38	35.03	5	False
5003	2025-01-01	Billing	Normal	7.9	22.86	4	False
5004	2025-01-01	Billing	Low	5.71	25.64	3	False
5005	2025-01-01	Performance	High	3.75	2.53	2	False
5006	2025-01-01	SQL editor	High	7.83	1.2	3	False
5007	2025-01-01	Charts	Normal	10.69	66.76	3	False
5008	2025-01-01	Data import	Low	1.93	29.01	4	False

Ask it something else

what is the median first response time by priority?which topics generate the most tickets?does satisfaction fall when resolution takes longer?

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