Control total

The figure the source system shows for the same period and filter as an export, noted before exporting. The file's total should equal it; if not, the export is filtered, truncated or double-counted, and that is found before any analysis is.

DefinitionThe figure the source system shows for the same period and filter as an export, noted before exporting. The file's total should equal it; if not, the export is filtered, truncated or double-counted, and that is found before any analysis is.

The one-minute check

Revenue for the month on the finance screen against the file's revenue column. It catches the export that missed the last day.

Without one

A file with no control total is a claim about the spreadsheet, not the business. The report says unreconciled.

How it is used

Before exporting, note the figure the source system itself shows for the same period and filters. After loading, the sum of the file must equal it. If not, the file is incomplete or the filters differ.

Example

The finance system shows invoiced sales of $4,231,880 for the period. The export sums to $4,102,415. The difference is one branch whose week had not posted when the file was cut.

Where it goes wrong

Skipped because the file looks complete. A missing week does not look like anything.

Articles that use this term

Board and management reporting · Finance and FP&A teams

Automating the sales report: from a manual monthly pack to computed tables

A practical path for a sales operations or finance team that assembles the monthly sales report by hand: what to automate first, the definitions that have to be written down before anything can be computed, the four exports the whole pack comes from, the identity checks that replace the reconciliation week, what stays manual, and the order in which the pack's sections move from spreadsheet to computed table.

16 Sept 20263 min read Data quality and reconciliation

Control total and identity: the check against the world, and the check against itself

The difference between a control total, which tests an uploaded file against the figure the source system showed, and an identity, which tests the computed tables against each other, why both are needed and neither replaces the other, the failure each catches that the other cannot, a filtered export against a duplicated account, the order they run in, and what a report says when one of them is unavailable.

16 Sept 20262 min read How-to guides

How often should sales data be refreshed? A cadence per measure, not one for the tool

Why 'real-time' is the wrong answer to how often sales data should be refreshed, the cadence each measure actually needs, weekly for coverage and pipeline, monthly for share of wallet and concentration, quarterly for norms and segments, annually for definitions, what a refresh more frequent than the measure's cadence does to the reader, and the export schedule that follows.

16 Sept 20262 min read How-to guides

How to map a spreadsheet's columns to a roll-up: a guide to the data map

What a data map is and how to build one from any sales export: the six roles a column can play, region, team, person, account, product, value, plus date, how to tell which column is which when the headers do not say, the columns to drop, the roll-up check that proves the map is right before anything is computed, and the mistakes that produce a map that looks fine and rolls up wrong.

16 Sept 20263 min read How-to guides

How to prepare a sales export for analysis: columns, dates, identifiers and totals

A practical checklist for getting a CSV or Excel export from a CRM, ERP or billing system into a state where coverage and share measures can be computed from it: one row per fact, a header row that names the column, identifiers instead of names, one date format, no subtotals, and a control total from the source system to reconcile against.

16 Sept 20263 min read How-to guides

Sales analytics KPI definitions: thirty measures on one page, each with its formula

Thirty sales and commercial measures defined in one sentence each with the formula, grouped by what they describe: coverage and activity, wallet and depth, retention and churn, pipeline and forecast, concentration and reporting, and data quality. Each links to a full definition. Written so that a team can adopt the definitions as its own, version them, and stop arguing about what a number means.

16 Sept 20264 min read