Variance Explanation Check
Compare caller-supplied expected and observed records for variance explanation check.
Description
A variance explanation connects a quantified change to its driver, period and evidence.
A variance explanation connects a quantified change to its driver, period and evidence. Label coverage helps identify missing parts of that narrative.1
Compare caller-supplied expected and observed records for variance explanation check.
When to use Variance Explanation Check
Use this tool to compare caller-supplied expected and observed records for variance explanation check.
Prepare the documented facts, aligned totals or expected/observed evidence labels requested. Choose applicable requirements and thresholds separately before interpreting a planning screen.
How to use Variance Explanation Check and understand its results
- Prepare the documented facts, aligned totals or expected/observed evidence labels requested. Choose applicable requirements and thresholds separately before interpreting a planning screen.
- Enter requiredElements, presentElements using the field table and calculation rules, then run the tool.
- Read coveragePercent, matched, missing, unexpected. The explanation below defines the units, signs, ordering and any flags or placeholder values.
- If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.
| Input field | Meaning | Value type; see rules below | Requirement |
|---|---|---|---|
| requiredElements | Required explanation elements | list of string | Required |
| presentElements | Present explanation elements | list of string | Required |
Enter requiredElements as the nonempty expected list and presentElements as observations, which may be empty. Trim entries, compare case-insensitively using en-US lowercase and collapse duplicates. Return matched/missing expected labels and unexpected observed labels. Output follows first occurrence order with the last duplicate’s trimmed spelling.
coverage%=100matchedUnique/expectedUnique Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| coveragePercent | Coverage (%) | number; minimum: 0; maximum: 100 |
| matched | Matched items | list of string |
| missing | Missing items | list of string |
| unexpected | Unexpected items | list of string |
{
"input": {
"requiredElements": [
"amount",
"driver",
"period",
"evidence"
],
"presentElements": [
"amount",
"driver",
"period"
]
},
"output": {
"coveragePercent": 75,
"matched": [
"amount",
"driver",
"period"
],
"missing": [
"evidence"
],
"unexpected": []
}
}Limitations and assumptions
No cause or supporting evidence is verified. Blank entries are errors; requirements are entirely caller-supplied and items have equal weight.
Numeric inputs, where present, must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Lists are limited to 100,000 entries; the rules above may require smaller limits. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors.
Alternative or Complementary methods
Use Ratio Narrative Check: Compare caller-supplied expected and observed records for ratio narrative check. For a complementary task, use Final Analytics Reconciliation: Recheck final analytical consistency after recorded corrections.
References
-
Variance Explanation Check — tool.io
Similar or alternative tools
- Ratio Narrative Check
Compare caller-supplied expected and observed records for ratio narrative check.
- Final Analytics Reconciliation
Recheck final analytical consistency after recorded corrections.