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How to Handle Unreliable Data: Quality Management Guide

Learn how to handle unreliable data effectively. Discover methods to identify, assess, and manage data quality issues that affect analysis reliability.

RowTidy Team
Nov 22, 2025
13 min read
Data Quality, Unreliable Data, Data Management, Quality Control, Best Practices

How to Handle Unreliable Data: Quality Management Guide

If you're working with unreliable data—inaccurate, incomplete, or inconsistent information—your analysis and decisions will be flawed. 82% of business decisions based on unreliable data lead to poor outcomes and wasted resources.

By the end of this guide, you'll know how to handle unreliable data effectively—using systematic methods to identify, assess, and manage data quality issues.

Quick Summary

  • Assess reliability - Identify data quality issues and their impact
  • Classify data - Categorize data by reliability level
  • Apply strategies - Clean, validate, or exclude based on reliability
  • Monitor quality - Track data reliability over time

Common Types of Unreliable Data

  1. Inaccurate data - Wrong values, incorrect information
  2. Incomplete data - Missing critical fields or records
  3. Outdated data - Information that's no longer current
  4. Inconsistent data - Conflicting information across sources
  5. Duplicate data - Same information repeated incorrectly
  6. Corrupted data - Data damaged during transfer or storage
  7. Biased data - Data that doesn't represent reality
  8. Unverified data - Information not validated or confirmed
  9. Incomplete records - Records missing essential information
  10. Format errors - Data in wrong format causing misinterpretation

Step-by-Step: How to Handle Unreliable Data

Step 1: Assess Data Reliability

Evaluate data quality and reliability level.

Create Reliability Assessment

Check for issues:

Accuracy:

  • Compare with known correct values
  • Verify against source systems
  • Check for obvious errors

Completeness:

=COUNTBLANK(A2:A1000)/COUNTA(A2:A1000)

Shows percentage of missing values.

Currency:

  • Check data timestamps
  • Verify if data is current
  • Identify outdated records

Consistency:

  • Compare across sources
  • Check for conflicts
  • Verify consistency

Create Reliability Score

Score components:

  • Accuracy: 0-100
  • Completeness: 0-100
  • Currency: 0-100
  • Consistency: 0-100

Overall reliability:

=(Accuracy + Completeness + Currency + Consistency) / 4

Reliability levels:

  • High (80-100) - Reliable, use confidently
  • Medium (60-79) - Use with caution, verify
  • Low (40-59) - Clean before use
  • Very Low (<40) - Exclude or major cleanup needed

Step 2: Classify Data by Reliability

Categorize data based on reliability assessment.

Create Reliability Categories

High Reliability:

  • Complete, accurate, current, consistent
  • Use directly in analysis
  • No cleaning needed

Medium Reliability:

  • Mostly good, some issues
  • Clean minor issues
  • Use with verification

Low Reliability:

  • Significant issues
  • Requires cleaning
  • Verify after cleaning

Very Low Reliability:

  • Major problems
  • Exclude or major cleanup
  • May not be usable

Tag Data by Reliability

Add reliability column:

=IF(AND(Completeness>0.9, Accuracy>0.9), "High", 
 IF(AND(Completeness>0.7, Accuracy>0.7), "Medium", 
 IF(AND(Completeness>0.5, Accuracy>0.5), "Low", "Very Low")))

Step 3: Handle High Reliability Data

Use reliable data directly.

Verify Reliability

Double-check:

  • Spot-check sample records
  • Verify against source
  • Confirm accuracy

Use in Analysis

Confident use:

  • Include in analysis
  • No cleaning needed
  • Trust results

Step 4: Clean Medium Reliability Data

Fix minor issues in medium reliability data.

Identify Issues

Common issues:

  • Minor missing values
  • Small format inconsistencies
  • Slight inaccuracies

Apply Cleaning

Fix issues:

  • Fill missing values
  • Standardize formats
  • Correct minor errors
  • Validate after cleaning

Reassess Reliability

After cleaning:

  • Recalculate reliability score
  • Verify improvement
  • Reclassify if needed

Step 5: Clean Low Reliability Data

Address significant issues in low reliability data.

Identify Major Issues

Common problems:

  • Large amounts of missing data
  • Major format inconsistencies
  • Significant inaccuracies
  • Structural problems

Apply Comprehensive Cleaning

Systematic cleaning:

  1. Handle missing data
  2. Fix format inconsistencies
  3. Correct inaccuracies
  4. Standardize structure
  5. Validate data quality

Validate After Cleaning

Verify improvement:

  • Check reliability score
  • Spot-check cleaned data
  • Verify against sources
  • Confirm usability

Step 6: Handle Very Low Reliability Data

Decide whether to exclude or attempt major cleanup.

Assess Usability

Consider:

  • Can data be cleaned?
  • Is effort worth it?
  • Is alternative data available?
  • Impact of excluding

Option 1: Exclude

If not usable:

  • Remove from analysis
  • Document why excluded
  • Note impact of exclusion
  • Find alternative data if needed

Option 2: Major Cleanup

If worth cleaning:

  • Comprehensive cleaning
  • Extensive validation
  • Multiple verification passes
  • Reassess after cleaning

Step 7: Validate Data Quality

Verify data reliability after handling.

Quality Checks

Accuracy:

  • Compare with known values
  • Verify against sources
  • Check for errors

Completeness:

=COUNTBLANK(A2:A1000)

Should be minimal.

Consistency:

  • Check across sources
  • Verify consistency
  • Identify conflicts

Create Quality Report

Summary:

Metric Before After Target
Reliability Score 65% 92% >85%
Accuracy 70% 95% >90%
Completeness 80% 98% >95%
Consistency 75% 94% >90%

Step 8: Monitor Data Reliability

Track data quality over time.

Set Up Monitoring

Regular checks:

  • Weekly for active datasets
  • Monthly for all datasets
  • Before major analysis
  • After data updates

Track Reliability Trends

Monitor:

  • Reliability scores over time
  • Quality metrics
  • Issue patterns
  • Improvement trends

Alert on Issues

Set up alerts:

  • Notify when reliability drops
  • Flag quality issues
  • Alert on new problems
  • Report reliability changes

Real Example: Handling Unreliable Data

Before (Unreliable Data):

Reliability Assessment:

  • Accuracy: 70% (some wrong values)
  • Completeness: 75% (missing values)
  • Currency: 60% (outdated records)
  • Consistency: 65% (inconsistencies)
  • Overall: 67.5% (Low Reliability)

Issues:

  • Missing values in 25% of records
  • Some outdated data (6+ months old)
  • Format inconsistencies
  • Some inaccurate values

After (Handled):

Reliability Assessment:

  • Accuracy: 95% (errors corrected)
  • Completeness: 98% (missing filled)
  • Currency: 90% (outdated removed)
  • Consistency: 94% (standardized)
  • Overall: 94.25% (High Reliability)

Handling Applied:

  1. Filled missing values intelligently
  2. Removed outdated records
  3. Standardized formats
  4. Corrected inaccuracies
  5. Validated quality

Reliability Handling Framework

Assessment → Classification → Action

  1. Assess - Evaluate reliability
  2. Classify - Categorize by level
  3. Action - Apply appropriate strategy:
    • High: Use directly
    • Medium: Clean minor issues
    • Low: Comprehensive cleaning
    • Very Low: Exclude or major cleanup
  4. Validate - Verify improvement
  5. Monitor - Track over time

Mini Automation Using RowTidy

You can handle unreliable data automatically using RowTidy's intelligent quality management.

The Problem:
Handling unreliable data manually is time-consuming:

  • Assessing reliability
  • Classifying data
  • Cleaning issues
  • Validating quality

The Solution:
RowTidy handles unreliable data automatically:

  1. Upload dataset - Excel, CSV, or other formats
  2. AI assesses reliability - Evaluates accuracy, completeness, currency, consistency
  3. Classifies data - Categorizes by reliability level
  4. Applies cleaning - Fixes issues based on reliability level
  5. Validates quality - Ensures data is reliable
  6. Downloads reliable data - Get trustworthy dataset

RowTidy Features:

  • Reliability assessment - Evaluates data quality automatically
  • Intelligent classification - Categorizes data by reliability
  • Targeted cleaning - Applies appropriate fixes based on reliability level
  • Quality validation - Ensures data is reliable after handling
  • Reliability reporting - Shows before/after reliability scores
  • Continuous monitoring - Tracks data quality over time

Time saved: 6 hours handling unreliable data → 3 minutes automated

Instead of manually handling unreliable data, let RowTidy automate the process. Try RowTidy's reliability management →

FAQ

1. How do I assess data reliability?

Evaluate accuracy (compare with known values), completeness (check missing data), currency (verify timestamps), consistency (check across sources). Calculate reliability score. RowTidy assesses reliability automatically.

2. What's considered unreliable data?

Data with low accuracy, high missing values, outdated information, inconsistencies, or format errors. Reliability score <60% generally considered unreliable. RowTidy identifies unreliable data.

3. Should I exclude unreliable data?

Depends on reliability level: very low (<40%) = exclude, low (40-59%) = clean first, medium (60-79%) = clean minor issues, high (80%+) = use directly. RowTidy suggests appropriate action.

4. How do I improve data reliability?

Clean issues (missing, inconsistencies, errors), validate against sources, standardize formats, remove outdated data, verify accuracy. RowTidy improves reliability automatically.

5. Can I use unreliable data in analysis?

Use with caution: high reliability (80%+) = use directly, medium (60-79%) = verify, low (40-59%) = clean first, very low (<40%) = exclude. Always note reliability level in analysis.

6. How do I monitor data reliability?

Set up regular checks (weekly/monthly), track reliability scores over time, monitor quality metrics, set up alerts for drops. RowTidy provides monitoring.

7. What's the difference between unreliable and inconsistent data?

Unreliable is broader (includes inaccurate, incomplete, outdated). Inconsistent is subset (format/value variations). Unreliable data may include inconsistencies, but also other quality issues.

8. Can I automate handling unreliable data?

Yes. Use Python scripts, Power Query workflows, or AI tools like RowTidy for intelligent automation.

9. How do I validate data reliability after handling?

Check reliability score improvement, spot-check cleaned data, verify against sources, compare before/after metrics. RowTidy validates automatically.

10. Does RowTidy handle all types of unreliable data?

RowTidy handles most common unreliable data issues: inaccuracies, missing data, inconsistencies, format errors. For severe corruption or complex business logic, may need specialized tools.

Related Guides

Conclusion

Handling unreliable data requires systematic approach: assess reliability (accuracy, completeness, currency, consistency), classify by reliability level, apply appropriate strategies (use directly, clean, or exclude), validate quality, and monitor over time. Use Excel, Python, or AI tools like RowTidy to automate the process. Proper handling ensures data reliability and analysis accuracy.

Try RowTidy — automatically handle unreliable data and get trustworthy, analysis-ready datasets.