Portfolio / Expertise / Variance Analysis
Variance Analysis
Most variance analysis reports the delta and stops. I treat it as detective work: verify the data, classify the cause, trace the root, then wire the finding into the next forecast. The variance is trivia; the decision it enables is the point.
The Workflow — How I Approach It
Tie out the data first
A variance from bad data is a fake signal. Reconcile actuals to the ledger before measuring anything.
Isolate the delta
Break the variance down by line item, period, and business unit so it can actually be explained.
Classify the cause
Structural, seasonal, or one-time? Each type demands a different response — never a one-line excuse.
Trace the root
Drill to the transactions, pricing, or timing behind the number until the story is complete.
Translate to a decision
What to fix (a cost trend), what to act on (a pricing signal), what to smooth (a timing difference).
Wire it into the forecast
Update the plan and follow up next month — an analysis that changes nothing was wasted.
My Operating View — The 2 Cents
The “why” is always findable. My 2 cents: if you don’t know whether a variance is structural, seasonal, or one-time, you don’t know anything yet. Classify first, then act. And never let an unreconciled trial balance poison the analysis — check the data before you chase the number.
What Worked & What Didn’t
What Worked
- The classify-then-trace framework made commentary sharp instead of generic.
- Connecting each variance to the next forecast tightened forecast error every cycle.
What Didn’t
- Reporting percentages without insight — the report died quietly until next month.
- Analyzing from raw, unreconciled data — chasing variances that were really bad entries.