Merge Field Service Technician Job CSV Exports Before an SLA Root-Cause Review

Service operations reviews often start with a simple question: why did response time or first-time-fix performance slip this week? The answer is usually spread across several exports. Dispatch has one file, technician completions arrive in another, and parts delay data sits in a separate queue. If those files are not aligned, the SLA review becomes opinion-heavy very quickly.

DataOlllo gives operations teams a local way to merge those CSV exports, isolate repeat miss patterns, and prepare a cleaner root-cause review.

What the SLA Review Needs

Keep in the working file	Isolate separately
Job ID	Long internal chat notes
Region or route	Customer personal details
Technician or team	Full free-text resolution narratives
Scheduled start	Unused system flags
Arrival or completion status	Attachment metadata
Delay reason and parts status	Large duplicated reference columns

A tighter working file helps the team compare routes, teams, and recurring blockers without over-sharing raw operational detail.

Where the Data Usually Splits

Source	Typical issue	Review impact
Dispatch export	Route and region codes change by team	Trends split unexpectedly
Job completion export	Completion statuses are inconsistent	First-time-fix rate becomes unreliable
Parts delay export	Delay events do not always join cleanly to the job record	Root cause stays vague
Escalation queue	Priority overrides are tracked separately	SLA misses look unexplained

A Root-Cause Review Workflow

1. Open dispatch, completion, parts, and escalation CSV exports locally.
2. Standardize fields such as job_id, region, technician_team, scheduled_window, arrival_status, completion_status, delay_reason, and parts_hold.
3. Normalize route and status labels so the same operational state is counted once.
4. Group misses by region, technician team, and root-cause category.
5. Separate misses caused by scheduling, parts, travel, and repeat visits.
6. Export one SLA review file and one unresolved-record file.

This gives leadership a clearer answer than a raw export pile.

Example SLA Review Table

Region	SLA misses	First-time-fix misses	Dominant root cause	Action owner
North Metro	31	14	Parts unavailable	Planning
Coastal South	22	9	Travel window slippage	Dispatch
Central West	18	7	Repeat diagnosis visit	Field ops
Inland East	11	3	Technician reassignment	Scheduling

Useful Root-Cause Buckets

Bucket	What it usually means
Parts unavailable	Stock or picking issue
Travel window slippage	Route planning or traffic issue
Repeat diagnosis	Incomplete first visit or triage issue
Customer not ready	Access problem outside technician control

Text Chart

SLA review focus

Parts-related misses      █████████░
Travel slippage           ███████░░░
Repeat diagnosis visits   ██████░░░░
Customer access issues    ████░░░░░░

Common Mistakes

• Treating every SLA miss as a technician performance issue.
• Reviewing delay reasons before labels are normalized.
• Keeping parts-delay data separate from the main job review.
• Letting route names change between teams without a standard map.

When to Use This Workflow

This workflow is useful for field service, repair networks, installation teams, and service administrators who need a dependable weekly review of why SLA performance moved.

Download DataOlllo

If technician job exports are still being merged manually before SLA review, try the local workflow in DataOlllo: download DataOlllo.