Data value is only as good as its quality. But many organizations go wrong when they make decisions based on poor data.

Data quality issues are very common in all industries. They are a business issue that costs much more to address than to avoid.

Below, we look at the common ones and how to fix them.

Incomplete data

This happens when records don’t contain essential fields. For example:

• Customer contact information without email addresses
• Transaction records without a date or time
• Product without price information.

Lack of information makes it difficult to analyze data. It skews reporting and creates problems for downstream teams.

The solution starts with enforcing data entry requirements at the source. This might be:

• Mandatory fields
• Field validation
• Automated checks.

The checks identify incomplete records before they are added to the system.

Duplicate records

This is one of the most insidious and irritating problems for technical teams and users. It happens when the same customer, a product, or a transaction is duplicated in a dataset. It could have small spelling, formatting differences, or different identifiers that hinder automatic systems from recognizing it as the same record.

This leads to:

• Overcounts
• Confusing metrics
• Poor customer experiences.

Hiring data consultants will help in implementing a master data management approach. They can come up with a structured means of identifying, merging, and preventing duplicates at scale.

Inconsistent data formats

In some cases, data is recorded inconsistently by multiple systems at multiple entry points. It can be difficult to deal with when needed for comparison or integration.

For example:

• DD/MM/YYYY and MM/DD/YYYY
• Phone numbers with and without country codes
• Product categories with different names in different departments, etc.

These inconsistencies create poor data that is not reliable for analysis or integration.

The answer lies in standardization. Create a data dictionary that specifies the format for each type in the organization. To maintain the format, use measures like:

• Input validation
• Data pipeline transformation rules
• Regular format checks.

Inaccurate data

This is data that is there. But it is factually incorrect. It could be:

• A misspelled address
• A wrong product description
• A mis-entered finance figure.

This may seem valid data until someone reacts and finds the error in the results.

Inaccuracy often enters datasets through:

• Manual data entry errors
• System migration issues
• Poor integration between platforms that don’t map fields correctly.

Inaccuracy can be solved through:

• Validation of sources
• Cross-system reconciliation checks
• Documented procedures for identifying and correcting errors if they are found.

Concluding words

Data quality is a discipline. It needs to be managed by the proper processes, tools, and organizational commitment. You should focus on data quality at a foundational level. It is always cheaper to keep quality data than it is to operate on data that is not reliable.