7 Spreadsheet Mistakes That Are Costing You Money

A few years ago, JPMorgan lost $6 billion partly due to a spreadsheet error. Copy-paste gone wrong in an Excel model.

You probably won't lose billions. But hidden spreadsheet mistakes could be costing you thousands—in bad decisions, wasted time, or missed opportunities.

Here are 7 common mistakes and how to catch them.

1. The Invisible Duplicate

The problem: Same data entered twice (or more), inflating your totals.

This happens when:

• Importing data from multiple sources
• Copy-pasting between sheets
• Manual data entry with no checks

The cost: Overstated revenue, inflated customer counts, wrong averages.

How to catch it:

• Use Excel's "Remove Duplicates" feature (Data → Remove Duplicates)
• Check if total rows match what you expect
• Look for identical timestamps or IDs

Example: A business thought they had 1,200 customers. After removing duplicates, they had 847. Their "customer acquisition cost" was actually 40% higher than they thought.

2. The Format Trap

The problem: Numbers stored as text (or vice versa).

You'll see this when:

• Numbers won't sum correctly
• Sorting puts "9" after "80"
• VLOOKUP returns errors on matching values

The cost: Broken formulas, wrong calculations, hours of debugging.

How to catch it:

• Check for green triangles in cell corners (Excel warning)
• Try multiplying a cell by 1 (errors if it's text)
• Use VALUE() function to convert text to numbers

Example: An e-commerce report showed $0 total revenue. The import had formatted all numbers as text. The SUM formula was literally summing text—which equals zero.

3. The Hidden Filter

The problem: Filtered data that looks like all your data.

Someone applies a filter, forgets to clear it, saves the file. Now everyone working from that file is only seeing a subset.

The cost: Decisions based on incomplete data.

How to catch it:

• Look for the filter icon in column headers
• Check row numbers (if they skip, data is filtered)
• Always clear filters before analysis

Example: A manager made staffing cuts based on "declining" sales. The spreadsheet had a filter showing only one product category—which was declining. Overall sales were up.

4. The Formula Break

The problem: Formulas that reference the wrong cells after copying, inserting rows, or moving data.

Classic signs:

• Totals that don't change when data changes
• Formulas showing old data
• #REF! errors

The cost: Reports that look right but are wrong.

How to catch it:

• Click on total cells and check the formula range
• Use CTRL+` to show all formulas
• Check if inserting a row updates your totals

Example: A monthly report formula summed rows 2-50. After adding products, some were in rows 51-60. The "total" was missing 20% of revenue—for 8 months before anyone noticed.

5. The Date Disaster

The problem: Dates in different formats, or dates Excel doesn't recognize.

Common issues:

• "01/02/2024" - Is that January 2 or February 1?
• "2024-01-15" might display as a number (45306)
• Text dates that can't be sorted chronologically

The cost: Wrong trend analysis, sorting chaos, filter failures.

How to catch it:

• Format a date column and see if all dates display correctly
• Sort by date and check if order makes sense
• Use DATEVALUE() to test if Excel recognizes text as dates

Example: A sales report combined data from US and European systems. Half the dates were MM/DD/YYYY, half were DD/MM/YYYY. March data showed a huge spike—it was actually data from different months jumbled together.

6. The Blank Cell Bomb

The problem: Empty cells that break formulas or create misleading charts.

Issues caused:

• AVERAGE ignoring blanks (not the same as zeros)
• Charts with gaps or wrong interpolation
• Pivot tables creating "(blank)" categories

The cost: Skewed averages, confusing visualizations, hidden data.

How to catch it:

• Use COUNTBLANK() to find empty cells
• Decide if blanks should be zeros and fill them
• Check if your formulas handle blanks correctly

Example: Average customer rating showed 4.8/5. Great, right? But 40% of customers left no rating. The actual experience might have been much worse—unhappy customers just didn't bother rating.

7. The Accidental Edit

The problem: Raw data modified without backup.

Signs:

• Values that seem wrong but you're not sure
• Formulas replaced with hardcoded numbers
• "Fixed" data that breaks historical comparisons

The cost: Lost data integrity, unreliable historical analysis.

How to catch it:

• Keep original data in a separate "raw" tab
• Protect cells with formulas
• Use version history (Google Sheets) or save copies

Example: A quarterly analysis showed flat growth. Someone had "fixed" a formula by typing the expected value. That hardcoded number stayed frozen for three quarters while actual data changed.

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Quick Quality Check Routine

Before using any spreadsheet for decisions:

1. Check for duplicates - Use Remove Duplicates feature
2. Clear all filters - Make sure you see everything
3. Verify totals - Add up a column manually and compare
4. Check date ranges - Sort by date, see if it makes sense
5. Look for outliers - Any numbers way larger/smaller than expected?

Takes 5 minutes. Can save hours of fixing bad decisions.

The Bigger Issue

These mistakes happen because spreadsheets are fragile. They're designed for flexibility, not reliability.

For important decisions:

• Double-check critical numbers
• Have someone else review before acting
• Consider tools that validate data automatically

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