The 6 Dimensions of Data Quality — Completeness
Missing data can look perfectly normal. How completeness failures distort analysis and the decisions that follow.
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Category
Data Quality
Recognize the failures that make tidy, persuasive data lead to the wrong decision.
Start with completeness: missing data does not always look broken.
Reading guide
What questions are we asking?
Data quality starts with “good enough for which decision?” A table can contain valid-looking values and still omit a whole device, period, or customer group. Start with completeness and learn to distinguish a missing observation from an observed zero.
- Is the data complete for this question, at this grain and over this period?
- Are missing records being interpreted as zero activity?
- Could a tracking change explain the apparent business change?
A suggested reading path
The 6 Dimensions of Data Quality — Completeness
Read the published opening essay: coverage gaps, MCAR/MAR/MNAR, and the business consequences of missing data.
Try it at work
Before filling nulls, make a coverage table by date and by the segment your decision depends on. Compare it with an independent source and investigate missing slices. The six-dimension series is in progress; completeness is the first published part.
Series
Data Quality Is Six Different Ways to Make a Bad Decision
A series in progress on six dimensions of data quality, written in business terms. Start with the published essay on completeness.
Explore the series →