SERVICE OPERATIONS — DATA DICTIONARY
All records are original synthetic practice data generated for Data Analyst Career. No real people or businesses are represented. You may use these files in personal learning and public portfolios with source credit to DataAnalystCareer.com. Label all results as synthetic. These simplified datasets are not real business benchmarks.

Grain: one support ticket. Snapshot: 2026-06-30 23:59 UTC.
Timestamp format: YYYY-MM-DD HH:MM. All timestamps are UTC.

ticket_id: unique, non-null ticket identifier.
created_at: ticket creation timestamp.
resolved_at: resolution timestamp; blank means unresolved at snapshot. Import blank as null, not a zero date.
team: Billing, Technical, or Account.
priority: High, Normal, or Low.
category: ticket reason; separate from the owning team.
sla_target_hours: elapsed-hour resolution target: High=24, Normal=72, Low=120.

METRIC RULES
Closed resolution hours = resolved_at - created_at in elapsed hours.
Open age hours = fixed snapshot - created_at for unresolved tickets.
SLA attainment = closed tickets with resolution hours <= target / all closed tickets.
Open beyond target = unresolved tickets with age hours > target. Report separately.
Compute percentiles only over closed-ticket resolution durations, state the percentile convention, and report open-ticket backlog alongside them. These are elapsed hours, not business hours. There are no pauses, reopenings, team transfers, or holidays modeled.

IMPORT CONTROL TOTALS
Rows / distinct ticket_id: 420
Closed tickets: 360
Open tickets: 60
Closed tickets meeting SLA: 139
Sum of closed-ticket resolution hours: 32452
Closed-ticket SLA attainment: 0.38611111 (decimal fraction)

CHECKS
Unique ticket IDs; created_at <= snapshot; resolved_at >= created_at and <= snapshot where populated; targets consistent with priority. Check that open + closed = total and that time differences are calculated in hours.