"Accurate" means a figure is close to the true or accepted value for its measure. Its source, method, and time period all matter. "Granular" means the figure is split into smaller units. Detail and correctness are separate qualities: a detailed figure can be wrong, while a broad total can be right.
Breaking a number down doesn't fix it
Splitting a wrong number into smaller parts does not fix the error. It may spread the error across more rows. Take a reported average hiring time of 34 days. The team validates the source records, date range, and method. The result matches those agreed rules, so it is accurate for that report. Yet it blends all roles and hides the gaps between them.
Accurate, not granular: "The 34-day average matches the validated source records, date range, and method. It is still one blended figure, so it does not show results by role."
Breaking a number down can also introduce new errors
The reverse error can also happen. The team splits the same average by role, which adds useful resolution. But two role figures come from an old data pull, not the agreed current one. The report is now granular, but those two figures are not accurate for this report.
Granular, not accurate: "The report now shows each role, but two figures use an old data pull. Those rows are detailed but not accurate for the stated period."
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See how Lyra Practice works →Misdiagnosing the fix
If a figure is wrong, saying "let's get more granular on this" aims at the wrong problem. More rows will not repair the source or method. If a figure is too broad, saying "let's double-check the accuracy" also misses the need. A correct total may still be too broad for the decision.
Misdiagnosing the fix: instead of "let's get more granular" when two figures use old data, say "let's recalculate those roles from the agreed current pull" -- fix accuracy first, then choose the needed detail.
The mistake to avoid
Do not assume that more detail makes a figure more correct. Also, do not assume that a correct figure has enough detail. Test each quality on its own. A fix for the wrong quality leaves the real need open.
Why the two get bundled into one complaint
People may say "this data is wrong" when they mean "this data isn't broken down enough." They may also raise both issues at once. Do not guess what they intend. Ask which figure, source, period, or group they need. Then check accuracy and detail as separate steps. Record the agreed definition before anyone acts on the result.
Practice scenarios
Practice using granular in situations like:
- deciding whether a data complaint is about correctness (accurate) or resolution (granular)
- catching a granular breakdown that introduced new errors in specific pieces
- asking the right diagnostic question before fixing a data-quality complaint
Useful practice phrases:
- "This is accurate at the aggregate level, but it's not granular -- it doesn't break down by role."
- "This is granular now, but two of the role-level numbers aren't accurate -- they used old data."
- "Is the problem that it's wrong, or that it's too blended to answer the question?"
Accurate says the figure meets a stated test. Granular describes its resolution: how far it is split into smaller units. One quality does not prove the other.
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