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A monthly report can be numerically correct and still answer the wrong question. In Juan Camilo Auriti’s account, a URL-normalization loop repeatedly created audit rows, so row counts and aggregates reflected the loop’s behavior more than the number of domains being reported on. The practical fix is to close the reporting window first, define what counts as one entity, select records deterministically, and show readers the denominator behind each result.
How duplicate rows made a monthly report misleading
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 remained separate rows. A scheduled job read one spelling and wrote the other, causing repeated audits.
The author reports that 86.9% of the audit-table rows were produced by this loop. For one domain, the table contained 1,831 rows before correction and 19 after the loop was fixed and records were merged. These are figures from Auriti’s account, not independently verified audit results or general benchmarks. Juan Camilo Auriti’s account on DEV Community
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The larger problem was not simply that there were duplicates. Aggregates built from the table gave more weight to domains affected by the loop. A report intended to describe domains could instead describe how often the faulty job revisited them. Row volume is not necessarily entity volume.
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Close the reporting window before writing findings
First establish the start and end of the reporting period and wait until that period has ended and its data is available. Auriti describes removing sections when the evidence was not yet there rather than using premature language about trends. That keeps an incomplete month from being presented as a finished measurement.
Completeness also depends on what was expected to arrive. A monthly table can look full while missing late or absent inputs, so define which reports or observations should exist for the period and check what actually arrived. DHIS2’s data-quality guidance recommends regular reviews at a frequency suited to data collection, with a feedback process to identify and correct errors. It discusses this in the context of health information systems; the review cadence and checks should be adapted to the dataset at hand. DHIS2 Data Quality Principles
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Count the thing the report is about
If the question is about domains, count distinct domains—not audit rows. Before deduplicating, define the entity key: the field or combination of fields that identifies one domain for this report. Decide how URL variants are canonicalized, too. Normalizing a URL can be useful, but the rule must preserve distinctions that matter to the data’s meaning.
Then make the reporting rule explicit. Auriti’s example uses one observation per domain in the reporting window. That may suit a report about each domain’s latest state, but it is not a universal rule: a report about changes over time may need every observation. The correct entity key and deduplication policy depend on what the rows represent and what the report promises to measure.
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Select records deterministically
When a report needs the latest observation for each entity, specify both the entity grouping and the ordering used to choose a row. Auriti describes selecting the latest row per domain and ordering by timestamp. Without an ordering rule, a query that chooses one row per group may return an arbitrary record rather than the latest one.
For a reliable monthly close, the selection rule should also handle ties. If two rows for the same entity share the latest timestamp, define a stable secondary sort key or another business rule. Otherwise, repeated runs can choose different rows even when the source data has not changed. The particular tie-breaker must match the dataset; the available account does not establish one.
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Show the denominator and missing values
A mean needs context: how many entities were in scope, how many had a usable score, and what happened to missing values? Auriti’s example separately counts domains and domains with a score. That distinction matters because SQL’s AVG ignores NULL values: the displayed mean may be calculated from fewer observations than the entity count.
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Use a repeatable quality review
DHIS2 groups useful data-quality reviews around completeness and timeliness, internal consistency, external consistency, and denominator consistency. Its examples include checking related variables against each other, looking for inconsistencies over time or outliers, applying validation rules and min-max checks, and reviewing denominator data. For reporting-rate completeness, it defines the measure as received reports divided by expected reports, multiplied by 100%.
These are adaptable checks, not a mandatory checklist for every monthly dataset. A practical close can choose the tests that fit the data and the report’s purpose:
- Completeness and timeliness: compare expected inputs with received inputs and note late arrivals.
- Internal consistency: check relationships among fields, changes over time, valid ranges, and suspicious outliers.
- External consistency: compare against an appropriate independent reference where one exists.
- Denominator consistency: verify that the population or entity count used for rates and averages matches the stated reporting question.
- Pipeline behavior: inspect whether normalization, retries, or scheduled jobs can create repeated records, and compare row counts with distinct-entity counts.
A check is useful only if an unexpected result leads to investigation and, where appropriate, correction. DHIS2 describes reviews as part of a feedback cycle so errors can be identified and corrected as they occur; its guidance is dated October 11, 2023.
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Quick Recap
A monthly close that readers can trust
- Freeze the period: state the reporting window and wait for it to close before publishing numerical findings.
- Confirm expected inputs: compare what arrived with what should have arrived, including timeliness where it affects the result.
- Define the entity: document the key and any canonicalization rule so equivalent records are handled consistently.
- Choose the observation rule: decide whether the report needs every event, one latest observation, or another selection—and specify deterministic ordering.
- Run quality checks: compare rows with entities, review missingness and plausible values, and investigate suspicious repetition or shifts.
- Publish the denominator: state the entity count and, for measures that omit missing values, the number of observations actually included.
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