Common Data Mistakes and How to Avoid Them
Published on: December 28th, 2025
Introduction:
People make mistakes with Data not because they are careless, but because few people stop to check if the data makes sense, is consistent, or even usable. More often than not, this leads to wrong conclusions, wasted time, and missed opportunities.
In this lesson, we will walk through the most common data mistakes small businesses make and the best ways to avoid them.
Mistake 1: Collecting Data Without a Purpose
Tracking everything “just in case” without knowing why is a huge no. You collect dozens of columns you will never have use for, or act upon, and clog your database.
We need to remember that more data does not always lead to more insights. It creates noise and wastes precious time.
To avoid it, always ask these questions before creating that new column:
Why do I need this data?
What decision will this data help me make?
Will this be reviewed regularly?
When making these decisions, the rule of thumb is that if you can’t explain it, you don’t need it.
Mistake 2: Inconsistent Data Entry
Inconsistent data entry happens when the same information is entered in different ways. For example:
A Body Lotion is entered as “Lotion”, “Body Lotion”, or “lotion”.
Price is manually entered as “$10”, “10”, or “ten dollars”.
Dates entered as “05/07/2025”, “7th May 2025”, or “May 7th 2025”.
Why this is a problem:
Your spreadsheet treats these as different values, breaking summaries and charts.
How to avoid it:
Use dropdown lists for categories and products
Set clear formatting rules for dates and numbers
Add notes or instructions at the top of your sheet
Mistake 3: Ignoring Missing or Incorrect Values
Leaving blanks, nulls, or incorrect entries unchecked. Sometimes, it could be in the form of:
Empty quantities or prices
Sales totals that don’t add up
Missing customer names or dates
Why this is a problem:
Missing data skews the totals, makes the trends unreliable, and the decisions made may not represent the true picture.
How to avoid it:
Enforce required fields.
Review new entries weekly.
Flag missing values for follow-up instead of ignoring them
Mistake 4: Mixing Raw Data With Calculations
Manually typing totals, discounts, or summaries into your raw data table easily becomes a problem when the data values are updated, losing integrity.
What it looks like:
Overwriting formulas with values
Editing totals instead of fixing source numbers
Combining notes and calculations in the same column
Why this is a problem:
You lose accuracy and can’t trace the origin of the numbers.
How to avoid it:
Keep raw data in one sheet.
Do calculations in a separate sheet.
Let formulas do the calculations.
Mistake 5: Not Reviewing Data Regularly
After collecting data, it should be common practice to review it regularly. Doing this prevents unchecked errors, identifies trends easily, and helps with decision-making.
Why this is a problem:
Data is as useful as it is accurate. Timely reviews help with accuracy.
How to avoid errors:
Set a weekly or bi-weekly data check
Look for:
Unusual spikes or drops
Missing values
Formatting issues
Quick Self-Check: Are You Making These Mistakes?
Ask yourself:
Why do I collect each data point?
Do I enter data the same way every time?
Do my totals update automatically?
Do I review my data regularly?
Case Study: Bessie’s “Almost Right” Data
Bessie’s skincare business has grown steadily. She has tracked sales, customers, and inventory every week and feels confident that she is “doing data right.” However, something feels off.
Although her revenue looked healthy, the cash was always tight. Promotions worked sometimes, but not consistently. When she tried to create simple charts, the numbers didn’t add up, so an audit was done on her spreadsheet.
What She Discovered
Too Much Data, No Direction - They had tracked customer birthdays, instagram handles, and delivery notes, but never used them for any decisions.
Inconsistent Entries - Some entries were saved as “Body scrub”, or “scrub”, and dates were written as 05/25/2025, May 25th, or 25/05/2025.
Missing and Ignored Values - with missing quantity, missing values for total sale, or no customer name.
Manual Calculations - They had typed totals directly into the sheet at some point. Unfortunately, when prices changed, the old totals stayed the same.
No Regular Review - The data was only checked by month-end, and by then, it was too late to fix errors or respond to trends.
How Bessie Fixed It
Removed unwanted columns.
Created dropdown lists for products and categories to avoid spelling mistakes.
Standardized date and number formats.
Used formulas instead of manual totals.
Set a 15-minute weekly data check every Friday
Within two months:
The reports finally matched her cash flow.
Promotions became more predictable.
Decisions felt clearer and faster
Reflection Prompt
Which of Bessie’s mistakes sounds most familiar to you right now? Pick one and fix it this week.
Wrap-Up
Data mistakes are an indicator of the need to build simple systems in your business with clear rules, simple templates, and consistent habits.
Do you need help cleaning your data?
We are here to help!
