Quick Summary

• NERIS has been the exclusive national reporting system since the start of 2026, so departments now have a meaningful body of NERIS-only data to review.
• Getting reports submitted isn't the same as submitting accurate, complete, and consistent data.
• A mid-year audit catches completeness, timestamp, location, and consistency problems before they compound.
• Most data-quality issues trace back to manual entry and disconnected systems.
• Automated capture and clean RMS export reduce errors at the source rather than fixing them after the fact.

Why Mid-Year Is the Right Time to Audit

There are two good reasons to run this check now rather than at year's end. The first is that you finally have enough data to see patterns. A handful of reports won't reveal a systematic problem, but several months of them will show whether a field is routinely blank or a timestamp is consistently off.

The second reason is that bad habits compound. If a particular field is being skipped or a location is being mis-entered, every month that passes adds more flawed records to the pile. Catching it mid-year means correcting the practice before it defines a full year of data that you'll later rely on for grant applications, ISO reviews, and operational decisions. A short audit now saves a long cleanup later.

It's worth remembering how far that data travels. The records you file feed community risk assessments, justify staffing and apparatus requests, support grant narratives, and factor into ISO evaluations. When a chief stands in front of a council or a review board, the strength of that case rests on numbers pulled straight from these reports. Data that's incomplete or inconsistent doesn't just look sloppy, it quietly weakens every argument built on top of it.

The Data Quality Checklist

Work through these areas one at a time. Each is a common failure point, and each is fixable once you can see it.

Completeness: Are Any Fields Routinely Blank?

Pull a sample of recent incidents and look for fields that are consistently empty or defaulted. NERIS captures richer, all-hazards detail than the old system, and it's easy for crews to skip fields they aren't used to completing. Flag any field that's blank more often than it should be, and find out whether it's a training gap or a workflow problem.

A useful trick is to sort a month of incidents by type and scan for outliers. If structure fires are missing details that medical calls capture fine, or one shift's reports look thinner than another's, the pattern usually points straight to the fix. Completeness problems are rarely random, and once you see the shape of them, they're often a short conversation at roll call away from being solved.

Timestamp Accuracy: Do Your Times Hold Up?

Response-time data is only as good as the timestamps behind it. If your times depend on responders relaying them over the radio and someone keying them in later, they're prone to estimation and error. This is where automated capture helps most. StreetWise status buttons record precise times the instant a responder taps en route, arrived, or available, straight to the server, without relying on busy dispatchers or crowded radio channels. Check whether your recorded times reflect what actually happened or what someone remembered afterward.

Location Accuracy: Are Incidents Landing at the Right Address?

Wrong incident locations are both an operational hazard and a data-quality problem. Audit whether incident addresses and coordinates in your records match where units actually responded. Inaccurate geocoding skews everything built on top of it, from response-time analysis to deployment planning.

Benchmark and Narrative Detail: Is the Story Captured?

Good records tell the story of the incident, not just its outline. Check whether key benchmarks and the narrative are being captured consistently. StreetWise carries incident benchmarks such as Patient Contact Made, along with tactical waypoints, directly into the record with precise timestamps, so the detail is captured as the call unfolds rather than reconstructed hours later.

Consistency Across the Data Chain: Does It Match From CAD to Record?

Finally, trace a few incidents from CAD through to the final record and check that the data agrees at each step. Discrepancies usually appear where information is manually transferred between systems. A bi-directional CAD interface that keeps StreetWise, the CAD, and the records system aligned reduces the drift that creeps in when data is re-keyed by hand.

Where Data Quality Problems Actually Come From

If your audit turns up issues, the root cause is usually one of two things: manual entry or disconnected systems. Every time a human transcribes a time, an address, or an action from one place to another, there's a chance for error, and every seam between two systems that don't talk to each other is a place where data gets lost or altered.

That's why the durable fix isn't more diligence at the keyboard, it's capturing data cleanly at the source and moving it between systems automatically. When status times are recorded by a button tap, when benchmarks flow into the record on their own, and when incident data exports straight into your RMS in XML or JSON without re-keying, you remove the steps where quality erodes. The StreetWise CADlink tablet MDT is designed around exactly this kind of capture-once, use-everywhere workflow. For background on the transition itself, our NFIRS to NERIS guide covers what changed and why.

Turning the Audit Into a Routine

The most useful thing you can do with this checklist is run it more than once. A single mid-year audit catches the current problems, but a quarterly rhythm keeps them from returning. Assign the review to a NERIS administrator on your team, work the same checklist each time, and track whether the issues you flagged last quarter actually got fixed. Data quality isn't a project with an end date, it's a habit that protects every decision your data informs.

FAQ

What should a NERIS data quality audit check?

At minimum, check completeness (are fields routinely blank), timestamp accuracy, location and geocoding accuracy, benchmark and narrative detail, and consistency across the data chain from CAD to final record. Sampling recent incidents across each of these areas will surface most systematic problems.

Why is NERIS timestamp accuracy so important?

Response-time analysis, deployment planning, and performance reporting all rest on accurate timestamps. Times relayed by radio and entered by hand are prone to error, while times captured automatically at the moment of action are far more reliable and reduce the cleanup burden later.

How often should we audit our NERIS data?

A mid-year audit is a strong starting point, but a quarterly review works better for keeping quality high. Running the same checklist on a regular cadence catches recurring issues early and confirms that earlier fixes actually stuck.

Can our response software improve NERIS data quality?

Yes. Software that captures times and benchmarks automatically and exports incident data directly into your records system reduces the manual entry and system-to-system transfers where most errors originate. That addresses data quality at the source rather than after the fact.

Good Data Is a Choice You Make Now

NERIS gives the fire service a more capable, modern platform, but it doesn't guarantee good data any more than the old system did. That part is still up to each department, and the choices you make in these first months set the tone for the years of records that follow. A short, honest audit now, repeated on a regular schedule, is the difference between data you can trust and data you have to apologize for.

If your audit surfaces problems rooted in manual entry or disconnected systems, the fix is to capture cleaner data at the source. To see how StreetWise records precise times, benchmarks, and locations and moves them into your RMS automatically, contact the StreetWise team for a walkthrough.