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Common Mistakes to Avoid in
DATA CLEANSING
www.damcogroup.com
+1 609 632 0350
Contact Us
01
DUPLICATE DATA IS A
SILENT THREAT
If you don’t remove duplicates, you may end
up with distorted analysis and wasted
storage.
02
Mismatched date styles
or number formats can
break your analysis and
cause misleading
insights.
INCONSISTENT
FORMATTING
CONFUSES SYSTEMS
03
MISSING VALUES CAN’T
BE IGNORED
Leaving out missing values can impact data
quality. Always handle them carefully
through imputation or removal.
04
DATA VALIDATION
SHOULDN’T BE SKIPPED
Unvalidated data is risky. Always verify
accuracy before moving forward with your
datasets.
05
TEXT DATA NEEDS
CLEANING TOO
Special characters, spelling issues, and
inconsistent casing can disrupt your analysis.
Don’t overlook them.
06
If left unaddressed,
outliers can skew
your results and
misrepresent the
true picture.
OUTLIERS CAN MISLEAD
YOUR INSIGHTS
07
STANDARDIZATION IS KEY
TO CONSISTENCY
Without uniform data formats, integration
becomes a challenge. Standardize before
analysis.
Backing up raw data helps
avoid irreversible damage
during the cleaning
process.
NEVER START WITHOUT
A BACKUP
08
09
Identify problems and
prioritize steps before
diving into data cleansing.
It saves time and improves
accuracy.
DON’T CLEAN
WITHOUT A PLAN
10
AVOID OVER-CLEANING
Excessive edits can delete valuable data. Clean
carefully without stripping useful information.
Follow For More Tips
Clean Data Leads to Smarter Decisions
Avoid these mistakes for reliable, high-quality data. Want professional
help with data cleansing? Let’s connect!
Found This Helpful
Enhance. Optimize. Succeed.
+1 609 632 0350 info@damcogroup.com
|
www.damcogroup.com/data-cleaning-and-formatting-services

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Common Mistakes to Avoid in Data Cleansing

  • 1. Common Mistakes to Avoid in DATA CLEANSING www.damcogroup.com +1 609 632 0350 Contact Us
  • 2. 01 DUPLICATE DATA IS A SILENT THREAT If you don’t remove duplicates, you may end up with distorted analysis and wasted storage.
  • 3. 02 Mismatched date styles or number formats can break your analysis and cause misleading insights. INCONSISTENT FORMATTING CONFUSES SYSTEMS
  • 4. 03 MISSING VALUES CAN’T BE IGNORED Leaving out missing values can impact data quality. Always handle them carefully through imputation or removal.
  • 5. 04 DATA VALIDATION SHOULDN’T BE SKIPPED Unvalidated data is risky. Always verify accuracy before moving forward with your datasets.
  • 6. 05 TEXT DATA NEEDS CLEANING TOO Special characters, spelling issues, and inconsistent casing can disrupt your analysis. Don’t overlook them.
  • 7. 06 If left unaddressed, outliers can skew your results and misrepresent the true picture. OUTLIERS CAN MISLEAD YOUR INSIGHTS
  • 8. 07 STANDARDIZATION IS KEY TO CONSISTENCY Without uniform data formats, integration becomes a challenge. Standardize before analysis.
  • 9. Backing up raw data helps avoid irreversible damage during the cleaning process. NEVER START WITHOUT A BACKUP 08
  • 10. 09 Identify problems and prioritize steps before diving into data cleansing. It saves time and improves accuracy. DON’T CLEAN WITHOUT A PLAN
  • 11. 10 AVOID OVER-CLEANING Excessive edits can delete valuable data. Clean carefully without stripping useful information.
  • 12. Follow For More Tips Clean Data Leads to Smarter Decisions Avoid these mistakes for reliable, high-quality data. Want professional help with data cleansing? Let’s connect! Found This Helpful Enhance. Optimize. Succeed. +1 609 632 0350 info@damcogroup.com | www.damcogroup.com/data-cleaning-and-formatting-services