Data quality and recordkeeping

Every indicator in Harrowgate Foods Ltd.'s new framework depended on data that someone had to record. Nina's recalculation of the injury rate in Lesson 2 had already shown what happens when records are kept differently at each site. As the new leading indicators came in, she found similar problems with inspection findings, verification checks and near-miss reports. A dashboard built on unreliable data is worse than no dashboard, because it gives false confidence. This lesson covers the legal records that the company must keep, the qualities that make data trustworthy, and the checks that Harrowgate Foods Ltd. put in place.

The legal records

In Nova Scotia, the legal reporting duties for injuries run mainly to two bodies. Employers must report work injuries that need medical attention or cause lost time to the Workers' Compensation Board of Nova Scotia (WCB), using its injury report and within the short deadline the WCB sets. Serious injuries and certain dangerous occurrences must also be reported to the Department of Labour, Skills and Immigration without delay, as the Act requires. There is no Canadian equivalent of a national injury log that every employer must keep, so the company's own injury register, which records every reported injury and near miss with its classification, is what its rates are calculated from. The company keeps its register and investigation records for at least five years.

These records serve different purposes. The WCB report starts a claim and decides benefits. The report to the Department supports its inspection and enforcement role. The company register supports measurement and learning. Elena found that the three did not always agree: one injury at Dartmouth had a WCB claim, a register entry under a different date and no investigation. Reconciling them became one of the first data quality checks.

What makes data trustworthy

Data quality is usually described by a handful of dimensions. Nina used five and gave each a concrete test:

• Completeness: every event that should be recorded is recorded, and every record has the required fields. Test: compare the register with first aid logs, clinic visits and claims.
• Accuracy: recorded values are correct, such as the classification, date and body part. Test: re-check a random sample of records against source documents.
• Timeliness: records are entered soon enough to act on and to meet legal deadlines. Test: measure the delay between event date and entry date.
• Consistency: the same thing is recorded the same way at every site and over time. Test: compare classification of similar events across sites.
• Validity: values follow the defined rules, such as a classification from the agreed list and dates in the right format. Test: automatic checks when data are entered.

A sixth quality, which Nina called traceability, ran through all of them: every number in a report should be traceable back to the records it came from. When Margaret Doyle asked why the Dartmouth rate had risen in one quarter, Nina could list the cases behind it and show which ones were new.

Common problems

The first quality checks across the three sites found the same problems again and again:

• Injuries entered days or weeks after they happened, often when a claim arrived, which meant they had been missed at the time.
• Classifications left blank or changed without a record of why.
• The same event entered twice, once by a supervisor and once by the first aid attendant, with slightly different details.
• Agency workers recorded under the agency's name, so their injuries did not appear in site figures.
• Near misses recorded only when the reporter filled in the form personally, so verbal reports to supervisors were lost.
• Hours for a month missing because the payroll export failed, which made the rate for that month impossible to calculate and the rolling rate wrong.

None of these problems was dramatic, but each one moved the numbers. Nina wrote a set of automatic checks that ran every week against the register and flagged records for review. The script below runs the main checks on a sample of January records.

# Data quality checks on an incident register
from datetime import date
register = [
    # id, site, event date, entered date, classification, body part, claim filed
    (101, "main", date(2027, 1, 4),  date(2027, 1, 4),  "first aid",  "hand",  False),
    (102, "main", date(2027, 1, 9),  date(2027, 1, 23), "recordable", "wrist", True),
    (103, "dist", date(2027, 1, 12), date(2027, 1, 12), "",           "foot",  True),
    (104, "dist", date(2027, 1, 12), date(2027, 1, 13), "first aid",  "foot",  True),
    (105, "main", date(2027, 1, 18), date(2027, 1, 18), "near miss",  "",      False),
    (106, "sauce",date(2027, 1, 20), date(2027, 1, 21), "first aid",  "eye",   True),
    (107, "main", date(2027, 1, 27), date(2027, 1, 27), "recordable", "",      True),
]
hours = {"main": 71_500, "dist": 19_800, "sauce": None}
problems = []
seen = {}
for rid, site, ev, ent, cls, part, claim in register:
    if not cls:
        problems.append((rid, "no classification"))
    if cls in ("first aid", "recordable") and not part:
        problems.append((rid, "injury with no body part"))
    if (ent - ev).days > 7:
        problems.append((rid, f"entered {(ent - ev).days} days after the event"))
    if claim and cls == "first aid":
        problems.append((rid, "claim filed but classed as first aid: review"))
    key = (site, ev, part)
    if part and key in seen:
        problems.append((rid, f"possible duplicate of {seen[key]}"))
    seen.setdefault(key, rid)
for site, h in hours.items():
    if h is None:
        problems.append((site, "hours missing for the month"))
for who, issue in problems:
    print(f"{str(who):<6} {issue}")
print(f"{len(problems)} issues in {len(register)} records")

102    entered 14 days after the event
103    no classification
104    claim filed but classed as first aid: review
104    possible duplicate of 103
106    claim filed but classed as first aid: review
107    injury with no body part
sauce  hours missing for the month
7 issues in 7 records

Every record in the sample raised at least one question, though not every question meant an error. Record 102 had been entered two weeks late, when the worker's claim arrived. Records 103 and 104 described the same foot injury on the same day at Dartmouth, one entered by the supervisor without a classification and one by the first aid attendant. Records 104 and 106 had claims filed but were classed as first aid, which can be correct, because a clinic visit alone does not make a case recordable, but needs review. The sauces plant's hours for January were missing. The site safety leads reviewed every flag each week, corrected the records and noted why.

Classification review

Classification is the step where most judgement is needed and where pressure, as Lesson 3 described, can act. Harrowgate Foods Ltd. separated the people who classify cases from the people whose performance the numbers reflect. Supervisors record the event and the treatment. The site safety lead proposes a classification within two days, using a written guide with worked examples. Nina reviews a sample each month across all three sites, and any change to a classification after it is entered requires a recorded reason. Disputed cases go to Elena, and her decisions are added to the guide.

Reconciliation

Once a quarter, each site reconciles its records. The safety lead compares the register with the WCB claims listing for the site, the first aid logs and the investigation tracker. Any injury that appears in one but not another is explained. Agency workers' claims are obtained from the agencies, because they do not appear on the company's WCB statements. The reconciliation is signed by the site manager, so that the person accountable for the site's results also vouches for the records.

Data for leading indicators

Leading indicators raised their own data questions. A verification checklist completed on paper and typed in later could be entered weeks late, or not at all. The company moved verifications and inspections to a simple mobile form that recorded the time and place automatically. Nina also watched for signs that data were being made up rather than collected: verification forms all completed in the same five minutes at the end of a shift, or pass rates of exactly 100 percent for months at a time. When she found both at one site, the explanation was a supervisor who had been told to complete twenty verifications a week and had no time to do them properly. The target was reduced, and the supervisor was given time.

Hours are data too

The denominator of every rate needs the same care as the numerator. Hours at Harrowgate Foods Ltd. now come from a single monthly export from the timekeeping system, prepared by payroll by the fifth working day of the following month. The export separates employee hours from agency hours and records overtime. Agency hours come from the agencies' invoices, which are checked against the gate badge records, because an invoice can include hours billed for travel or training off site. Nina keeps a log of any correction to past hours, with the reason, so that a rate that changes after it was first reported can be explained.

She also added a simple test: if a site's hours for a month differ from the average of the previous three months by more than 15 percent, the figure is queried before it is used. The test caught the failed export at Kentville and, later, a month in which Dartmouth's agency hours had been entered twice.

Privacy and access

Injury records contain personal health information, and workers will only report injuries freely if they trust how that information is used. Harrowgate Foods Ltd. limits access to the register to the people who need it, removes names from all reports and dashboards, and never reports a group so small that individuals could be identified, such as a department with one injury in a month. Investigation reports shared with the joint occupational health and safety committee describe what happened and why, without medical details beyond what the committee needs. Requests for records from outside the company go through Aisha Grant, the company lawyer.

Who owns the data

Each data set in the framework now has a named owner, who is responsible for its definition and quality, and a steward at each site, who makes sure it is collected and entered properly. Nina owns the register and the hours. Luis owns the specialist inspection register and the ammonia data. Each site safety lead is the steward for inspections, verifications and near-miss reports at that site. When a number looks wrong, everyone knows whom to ask, and the owner is expected to answer within a week with either a correction or an explanation.