Aglet

Learn From Repeated Missing Versus Zero Confusion

A value-state lesson preserves the smallest period example where no observation and explicit zero were read alike. Record source membership, measure output, chart treatment, labels, and decision context. Then improve the metric dictionary or report review so future changes do not silently flatten availability into a number.

Keep the lesson for the next incident

  1. Keep a state fixture

    Retain no-row, explicit-zero, positive, null, and unavailable examples with expected card, table, chart, and export outputs. Record filters, period, measure definition, and source revision. Preserve the original ambiguous display beside the agreed state vocabulary.

  2. Classify earlier confusion

    Review prior questions for imputation, chart interpolation, hidden null handling, total logic, and labels that used zero for no data. Group named cases by missing contract or review signal. Avoid claiming that one display rule explains every measure or chart.

  3. Assign state stewardship

    Make the metric owner maintain value definitions and the report owner check them after aggregation, filter, or visualization changes. Add a trigger when a new measure introduces a blank state or a chart changes interpolation. State which cases the fixture detects and which require policy judgment.

What to carry forward

The learning record is complete when it links confusion to a missing state definition or review cue, preserves a four-state fixture, and names an owner and trigger. Keep intentional imputation explicit so future readers know when zero is a policy choice rather than an observed value.

Technical background: Metabase documentation.

Keep the decision with the work.

Use a Work Item in Aglet to record the problem, the evidence you have, and the next decision. Add an owner and priority, then keep updates in the discussion so the next person can follow the reasoning.

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