Official resource
V3 Public Data Quality Dashboard
Completeness and quality measured across a national public-safety dataset.
Why it matters
Shows what measuring your own data quality actually looks like, rather than describing it.
Data Quality: Garbage In, Garbage Out
More data does not automatically create better information. Missing values, inconsistent definitions, inaccurate timestamps, duplicate records, workflow differences and changes in documentation can distort the conclusions leaders draw.
The short version
Every chart, dashboard, predictive model and AI system inherits the strengths and weaknesses of the information underneath it. Before asking what the data says, ask whether it is trustworthy enough to answer the question. Data quality is an operational issue rather than an IT one: most fire and EMS data is created during or immediately after real work. A firefighter changes unit status, a dispatcher creates a timestamp, a paramedic completes an ePCR, an officer selects a classification. Small differences in workflow become large differences across thousands of incidents — and a dashboard cannot correct a definition that everyone interprets differently. If turnout time suddenly improves dramatically after a software update, the first question is not what the crews did differently. It is whether the way the timestamp is created changed.
Start here
If you read one thing on this subject, read this.
Go deeper
Official resource
NFIRS Data Quality
USFA's documentation of data-quality problems in the national fire dataset. NFIRS is being retired in favour of NERIS.
Why it matters
Historical, but the problems it documents are exactly the ones that follow a department into NERIS.
Official resource
NERIS Data Dictionary
The definitions behind every NERIS field.
Why it matters
Most data-quality arguments are definition arguments. This is where the definitions live.
Official resource
Collecting Good EMS Data
Why accuracy and completeness in patient-care documentation affect care, public health and provider safety.
Why it matters
Makes the case to crews in terms of patients rather than compliance.
Questions to ask your vendor
- Where did this data originate?
- Who enters it?
- Is entry automatic or manual?
- What does a blank value mean?
- Has the definition changed?
- Has the workflow changed?
- Did the software change?
- Are there obvious outliers?
- Are records duplicated?
- Are all shifts and stations documenting the same way?
- Does the denominator make sense?
- Can we reproduce the result?
This is a reading list, not a guide
Everything above was published by someone else, and is here because it is the clearest treatment of the subject we could find and verify. The Hub’s own guide to this topic is still being written. If you know a better source than the ones listed, that is worth telling us before it is.
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