How to preserve and verify the original export before any transformation
A marketing spreadsheet that was cleaned, filtered, and formula-augmented without preserving the original export is a workbook where each number is only as trustworthy as the person who cleaned it. The reviewer should confirm that the original export from source platform exists untouched and can be compared against the working copy. An export that was opened, cleaned, and saved over itself is no longer an export. It is a hybrid of source data and analyst edits with no way to separate the two. The reviewer should verify that the original export file is preserved with a dated filename that names the source and the export date.
The reviewer should also verify that the row count in the working copy matches the row count in the preserved export. A working copy with fewer rows than the export means rows were deleted during cleaning. If the deletion reason is documented and the deleted rows are preserved in a separate sheet, the deletion is auditable. If rows were deleted without documentation, the analysis is built on a subset of the data with no record of what was excluded and why. The reviewer should also check whether any columns were added to working copy that did not exist in the original export. Formula columns added by the analyst are fine if they are labeled and separated from original data. Columns with edited values that overwrite the original data aren't fine because the original context is lost. If the original export isn't preserved or the working copy has undocumented deletions or overwrites, the reviewer should hold any recommendation based on workbook until the export is re-downloaded and preserved.
- Confirm the original export file is preserved with a dated filename naming the source platform and export date.
- Verify row count in the working copy matches the preserved export and document any deletion reason.
- Check for edited values that overwrite original data and flag any column where the source value can't be traced.
- Hold recommendations and re-download the export if the original isn't preserved or contains undocumented changes.
How to validate table structure and data type consistency
A spreadsheet that looks structured can still fail the structural check if the headers are inconsistent, the data types are mixed within a column, or the table shape changes between refreshes. The reviewer should confirm that each column has a header, each header is unique, and the data type in each column is consistent from first data row to last. A date column that contains text-formatted dates in some rows and number-formatted dates in others will break each formula and pivot that depends on date logic. A currency column that mixes numbers formatted as text with numbers formatted as currency will produce a sum that silently excludes the text-formatted values.
The reviewer should also check if table structure is stable across refresh cycles. An export that sometimes includes an extra column because the platform changed its export schema, that sometimes includes summary rows at the bottom, or that sometimes includes column headers in a different order will break formulas that reference column positions or that expect a specific row range. The reviewer should verify the structure of two consecutive exports and flag any difference in column count, column order, or the presence of summary rows. If the table structure isn't consistent or the data types aren't uniform within each column, the reviewer should hold any recommendation and require the export to be cleaned and the structure to be documented before workbook is used as decision evidence.
- Confirm each column has a unique header and the data type is consistent in each cell from first to last row.
- Flag text-formatted numbers in currency or date columns that will silently break formulas and pivot aggregations.
- Compare two consecutive export structures and flag any difference in column count, order, or summary-row presence.
- Hold recommendations and require structural cleanup if headers or data types are inconsistent or unstable across refreshes.
How to audit formula logic with a spot-check on calculated columns
Formulas are the working logic of a spreadsheet, and they break in ways that produce numbers that look correct but are not. The reviewer should spot-check each calculated column by manually computing the formula result for three rows drawn from top, middle, and bottom of the data and comparing the manual calculation against the formula output. A formula that returns correct value for top row because the reference is correct but the wrong value for bottom row because the range was not extended when rows were added is a broken formula that has been partially verified. A spot-check that only verifies the top row creates false confidence.
The reviewer should also check for common formula failure modes. A VLOOKUP that returns the wrong column because a column was inserted to left of the lookup range. A SUM that accidentally spans into a header row. An AVERAGE that includes blank cells as zeros, pulling the average down. A formula that references an external workbook that isn't open, returning a cached value that may be stale. The reviewer should check if formula range ends at the last data row and whether any formula cell has been manually overwritten with a hard-coded value. A manually overwritten cell that sits inside a calculated column breaks the column's consistency and makes each summary cell that depends on that column unreliable. If formula logic can't be verified with a three-point spot-check or if any formula column contains a manually overwritten cell, the reviewer should hold any recommendation that depends on that column until the formulas are corrected and re-verified.
- Spot-check each calculated column by manually computing the result for rows at the top, middle, and bottom.
- Check for common failures: column-insertion lookup drift, header-row inclusion, blank-as-zero averages.
- Verify no formula cell has been manually overwritten with a hard-coded value inside a calculated column.
- Hold recommendations if any formula column fails the spot-check or contains a manually overwritten value.
How to check filters and pivots for hidden exclusions
A pivot table or a filtered view that summarizes data excluding a segment that would change the recommendation produces a summary that is precise and wrong. The reviewer should clear each filter in the workbook and check the total row count against the row count in each summary. If a pivot table reports data for four hundred rows but the source table contains five hundred and twenty, one hundred and twenty rows are excluded by filters, pivot configuration, or data gaps. The reviewer should identify which rows are excluded and why, and verify that the exclusion doesn't change the direction or magnitude of the recommendation.
The reviewer should also check for pivot configuration errors that distort the summary. A pivot that uses COUNT instead of SUM for a revenue field will report the number of transactions, not the revenue total. A pivot that groups dates by month but spans a date range that includes a partial month at each end will produce a month that appears to show dramatic movement when the movement is caused by having only three days of data in that month's bucket. A pivot that uses a calculated field with a formula error will propagate that error to each cell in the calculated column. The reviewer should verify each pivot configuration element including the value field aggregation method, the filter state, the grouping logic, and any calculated fields against the business question the pivot is supposed to answer. If hidden exclusions or pivot configuration errors are present, the reviewer should hold any recommendation that depends on summary and require the exclusions to be documented or the configuration to be corrected.
- Clear each filter and compare total row count against each summary to identify excluded rows.
- Document which rows are excluded and verify the exclusion doesn't change the recommendation's direction or magnitude.
- Check pivot configuration for COUNT-vs-SUM errors, partial-month grouping, and calculated-field formula errors.
- Hold recommendations if hidden exclusions or pivot configuration errors are present and undocumented.
How to gate the output and close the analysis approval
The final gate separates what the spreadsheet shows from what the team should change, and prevents the analysis from being treated as an implementation plan before it has been reviewed. The reviewer should produce a documented output that names the original data source, the transformations applied, any excluded data and the exclusion reason, the formula logic for each calculated metric, and specific recommendation the spreadsheet supports. An output that says the spreadsheet supports the campaign change isn't documented. An output that names the GA4 export dated July 9, the filter excluding internal traffic which removed twelve rows, the VLOOKUP joining campaign names to conversion data with three unmatched rows documented in the caveat, and specific budget reallocation the analysis supports from Campaign A to Campaign B by a specific dollar amount is documented.
The reviewer should produce one of three outputs. Approved when the original export is preserved, the table structure is consistent with uniform data types, the formula logic passes spot-checks on each calculated column, filters and pivots are transparent with documented exclusions, and the output names specific action the spreadsheet supports with all transformations traced. Held when any gate fails and the gap is named with a specific fix. Returned when the workbook has structural problems including a missing original export, broken formulas across multiple columns, or pivot configurations that answer a different question than the recommendation that make rebuilding from source more efficient than incremental fixes. No campaign, budget, or resource decision should be based on spreadsheet analysis until the reviewer accepts the readiness review.
- Produce documented output naming the source, transformations, exclusions, formula logic, and specific recommendation.
- Verify that any transformation in the workbook can be traced from original export to final recommendation cell.
- Produce approved, held, or returned based on whether all five analysis readiness gates pass with documented traces.
- Return when structural problems across multiple layers make rebuilding from source more efficient than fixing.
Sample Review Note
All five diagnostic gates were checked for this Marketing Spreadsheet Analysis Readiness review. The original export was preserved with a dated filename and verified against the working copy for row count match and undocumented deletions or overwrites. Table structure was validated by confirming each column has a unique header, data types are consistent within each column, and two consecutive export structures were compared for stability. Formula logic was audited with a three-point spot-check on each calculated column, checking for common failure modes including lookup drift and blank-as-zero averages, and flagging any manually overwritten formula cells. Filters and pivots were checked for hidden exclusions by clearing each filter and comparing row counts, documenting excluded rows and their impact, and verifying pivot configuration for aggregation accuracy and grouping errors. The output was gated by producing a documented recommendation naming the source, transformations, exclusions, and specific action supported, and the output was produced as approved, held, or returned.
Recheck triggers include a new source export with a different structure, a formula modification in any calculated column, a filter or pivot configuration change, a new data source added to workbook, or the passage of one reporting period without a readiness pass. If a recheck is needed, any decision based on spreadsheet analysis should be paused until the reviewer accepts the updated evidence.