A monthly dataset is not ready to report just because it has accumulated rows. In one incident described by Juan Camilo Auriti, a scheduled job repeatedly created audits while two spellings of the same URL remained separate. The result: row volume looked like activity, but it did not represent the number of distinct domains being measured. His account offers a practical way to close a reporting window: wait for the data, count the entities that answer the question, select records deterministically, and show the denominator behind every aggregate.
The month row volume stopped meaning what I thought it meant
Juan Camilo Auriti describes publishing a monthly report from audit data, using findings from the reporting window that had just ended. In the incident he recounts, two spellings of the same URL were treated as separate rows. A scheduled job read one spelling and wrote the other, creating repeated audits.
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Auriti reports that 86.9% of the audit-table rows were produced by that loop. For one domain, the table held 1,831 rows before cleanup; after the job was corrected and the records were merged, it held 19. These are figures from his account, not independently verified measurements or benchmarks for other datasets. Auriti’s DEV Community post
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Close the reporting window before writing the numbers
A monthly close begins with a boundary: which dates belong to the month, and when is the data for those dates considered available? Auriti’s approach is to wait until the reporting window has ended and its data exists before writing numerical findings. If a section cannot be supported yet, he drops it rather than dressing an incomplete result as a trend.
This avoids a common reporting trap: treating a partial month as if it were a completed one. A figure that changes simply because late-arriving records have not landed is not a stable monthly result. The reporting contract should make the time window and the point at which data is considered closed explicit.
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Count the unit the report is actually about
Rows, events, accounts, domains, and people are different units. The right count depends on the question. For a report about domains in a period, Auriti’s example uses one observation per domain in the reporting window rather than treating every audit row as a separate domain.
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That choice requires a deliberate entity key. In this example, the key is a domain; in another dataset it could be a customer ID, device ID, or another stable identifier. URL canonicalization also needs a defined policy: decide which variations represent the same entity, then apply that rule consistently before grouping or comparing records. Do not assume that removing duplicates is always correct; repeated events may be meaningful when the report is about event volume.
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Make record selection deterministic
When several records exist for one entity in a period, the report needs a stated rule for choosing or combining them. Auriti’s SQL example selects the latest row per domain and orders by timestamp. Without the ordering, a query may return an arbitrary row, so repeated runs or database execution plans may not select the same observation.
“Latest record wins” is a policy, not a universal truth. It fits only when the latest observation is the one the report intends to represent. Some datasets instead need a sum, a maximum, a first event, or a record selected using a different business rule. The key and selection logic should match the meaning of the metric.
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Show the denominator and how missing values behave
Averages need context. In Auriti’s example, the query counts domains separately from domains with a score. That distinction matters because SQL’s avg function skips NULL values: the mean can describe fewer observations than the entity count suggests.
For a useful monthly result, make clear how many entities are in scope, how many have a usable value, and which records contribute to the aggregate. If those counts differ, readers can see that missingness is part of the result rather than mistaking the mean for a complete measure of every entity.
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Use a repeatable quality review, not a single duplicate check
Duplicate detection is one useful check, but it cannot establish that a dataset is complete or internally coherent. DHIS2’s data-quality guidance recommends regular reviews matched to the frequency of data collection and connected to a feedback cycle so errors can be corrected. Its health-information-system documentation describes checks for completeness and timeliness, internal consistency, external consistency, and denominator consistency. These are adaptable review dimensions, not a requirement that every dataset use every measure. DHIS2 Data Quality Principles
- Completeness and timeliness: Compare received records or reports with what was expected, and check whether they arrived on time. DHIS2 gives reporting-rate completeness as received reports divided by expected reports, multiplied by 100%.
- Internal consistency: Check relationships between variables, patterns over time, outliers, validation rules, and plausible minimum and maximum values.
- External consistency: Compare with an independent source where one exists and where the definitions are sufficiently aligned.
- Denominator consistency: Confirm that the population or set of entities used as the denominator is appropriate and consistently defined.
The useful principle is cadence: a review should happen often enough to find errors while they can still be corrected, with its checks suited to the data’s purpose.
Quick Recap
A practical monthly-close checklist
- Define the period. Record the start and end of the reporting window and the rule for when its data is ready to close.
- Check expected coverage. Compare what arrived with what should have arrived; inspect timeliness and gaps before calculating findings.
- Confirm the entity key. Decide what counts as one entity for each reported measure, and apply the canonicalization rule consistently.
- Inspect row-to-entity differences. Compare raw row counts with distinct entity counts and investigate unusually large gaps or concentrations.
- Specify selection logic. If one record per entity is required, state the rule and order the records so the result is repeatable.
- Expose missingness. Report the number of entities in scope and the number with valid values used by each aggregate.
- Run consistency checks. Review related fields, time patterns, outliers, plausible ranges, and denominators as relevant to the dataset.
- Write only supported findings. If the period is not complete or a result cannot be supported, leave the numerical claim out rather than implying certainty.
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