Anomaly variance explanation

Purpose

Explain why a reported metric differs materially from its comparison value. The output separates arithmetic drivers from contextual evidence, ranks causes by quantified contribution, and gives a reviewer a traceable basis for follow-up.

Scope

Analyze one metric, comparison period or plan, and the dimensions that explain its movement. Cover volume, rate or price, mix, timing, data completeness, and documented operational events. Excluded: changing source data, inventing a target, forecasting beyond the supplied evidence, or assigning corrective work to people.

Data basis

• KPI table with metric, period, actual, plan_or_prior, dimension, unit, and source_row_id.
• Transaction or event table with date, quantity, price_or_rate, category, customer_or_account, and status.
• Forecast, budget, calendar, launch, outage, or pricing documents in scope.
• Metric definition and materiality threshold supplied by the company.

Result

Produce a variance bridge sheet with one row per metric segment and a memo that states the absolute and percentage variance, quantified drivers, evidence citations, confidence, and unresolved data gaps.

Quality criteria

• The bridge reconciles actual minus comparison to the sum of classified drivers, subject to a stated rounding tolerance.
• Every driver names its formula, source row or document page, and confidence level.
• The same period, unit, currency, and population are used on both sides of the comparison.
• Timing effects, excluded rows, and unproven hypotheses remain visibly separate.
• No explanation is called causal without supporting evidence.

Instructions

Use the metric definition and company materiality rule before interpreting movement. Calculate absolute change as actual - comparison and relative change only when the comparison denominator is non-zero. Decompose revenue-like measures into volume, rate, and mix only when the source fields support that decomposition; otherwise label the residual. Rank explanations by quantified contribution, then evidence strength, and label a contribution as “hypothesis” when it is supported only by correlation or a dated event note. Cite metric-table rows for figures and document section or page for contextual claims. Preserve negative, zero, and missing values rather than converting them to blanks.

Adapt before use

• Map KPI, transaction, dimension, date, unit, and source-ID columns to the company data model.
• Add the metric definition, comparison hierarchy, materiality threshold, and rounding tolerance.
• Define currency conversion, timezone, fiscal calendar, and treatment of cancellations or late postings.