Not all data is useful.

In fact, bad data is one of the biggest reasons reports fail, dashboards mislead, and business decisions go wrong.

But what actually makes data good or bad?

This article breaks it down in simple, beginner-friendly terms, so you can confidently evaluate data quality — even if you’re new to data analysis.

Why Data Quality Matters

Good data helps you:

• Make accurate decisions
• Build trustworthy dashboards
• Spot real trends
• Gain stakeholder confidence

Bad data leads to:

• Wrong conclusions
• Poor business decisions
• Lost time fixing errors
• Lack of trust in analytics

Data quality is more important than fancy tools.

What Makes Data “Good”?

Good data has several key characteristics.

1. Accuracy

Good data reflects real-world truth.

Examples:

• Correct sales figures
• Accurate dates
• Proper calculations

If the numbers are wrong, everything built on them is wrong.

2. Completeness

Good data has minimal missing values.

Bad example:

• Customer table with missing emails or locations

Good example:

• Required fields are filled consistently

Missing data can distort analysis and trends.

3. Consistency

The same data should not contradict itself.

Bad data:

• “Nigeria” in one column, “NG” in another
• Different date formats in the same dataset

Good data:

• Standardized formats and naming

Consistency makes analysis reliable.

4. Timeliness

Good data is up to date.

Old data can be misleading:

• Last year’s sales data for today’s decisions
• Outdated customer information

Timely data supports relevant insights.

5. Relevance

Good data answers a specific question.

Bad data:

• Collecting dozens of fields you never use

Good data:

• Focused on the problem you’re solving

More data doesn’t mean better analysis.

6. Uniqueness

Good data avoids duplicates.

Bad data:

• Same customer counted multiple times
• Duplicate transactions inflating totals

Duplicates lead to incorrect metrics.

What Makes Data “Bad”?

Bad data usually shows up in predictable ways.

1. Missing or Null Values

• Empty cells
• Incomplete records

This can skew averages and totals.

2. Duplicate Records

• Same row appearing multiple times
• Repeated IDs

Duplicates inflate results.

3. Inconsistent Formatting

• Text mixed with numbers
• Different date formats
• Capitalization issues

This breaks queries and charts.

4. Incorrect or Impossible Values

Examples:

• Negative ages
• Future dates for past events
• Sales values that don’t make sense

These are red flags.

5. Biased or Incomplete Data

If data doesn’t represent reality:

• Missing certain user groups
• Collected from biased sources

Your conclusions will also be biased.

How Data Analysts Turn Bad Data Into Good Data

This process is called data cleaning.

Common steps include:

• Removing duplicates
• Fixing data types
• Filling or removing missing values
• Standardizing formats
• Validating ranges

Tools used:

• Excel
• SQL
• Python
• BI tools

Cleaning often takes more time than analysis.

How to Check If Your Data Is Good

Ask these questions:

• Does this data answer my question?
• Are there missing or duplicate values?
• Do the numbers make sense?
• Is the data recent enough?
• Can I trust the source?

If the answer is “no” to many of these, the data needs work.

Common Beginner Mistakes

Trusting data blindly
Skipping data cleaning
Ignoring missing values
Using outdated datasets
Overlooking duplicates

Good analysts always question the data first.

Good data is:

• Accurate
• Complete
• Consistent
• Relevant
• Timely

Bad data isn’t always obvious but it always affects results.

Before building dashboards or running analysis, check your data quality first.
That habit alone will make you a better data professional.

FAQs