Data quality issues are becoming a major challenge for every organization in the context of data being an important asset. These issues, if not addressed, can lead to erroneous conclusions, wasted resources and degrade the quality of business data.
In this article, DIGI-TEXX will delve into the 8 most common data quality problems and propose practical solutions to help your business build a solid and reliable data foundation.
Duplicate Data Issues
Duplicate data is one of the most visible but most annoying data quality issues for the majority of businesses. This problem occurs when a single entity (e.g., a customer, a product) has more than one record in the database.
Causes:
- Manual data entry errors: Employees accidentally create a new record for an existing customer due to typos or lack of checking.
- System integration: When consolidating data from multiple sources (e.g., CRM, sales systems, marketing tools) without an effective reconciliation process.
- Data migration: Errors occur during the migration of data from an old system to a new system.
Negative impacts:
- Distorted analytics and reporting results (e.g., miscalculating customer numbers).
- Wasted storage space and processing resources.
- Causes a poor customer experience (e.g., sending the same marketing email multiple times to the same person).
Solution:
- Use deduplication tools: These tools use specialized algorithms and software to automatically identify and merge duplicate records based on defined rules.
- Establish a standard data entry process: Train staff and apply strict rules for data entry, requiring records to be checked for existence before creating new ones.
- Establish Master Data Management (MDM): Establish a unique and trusted ‘golden record’ for each object. All other systems will refer to this master record.
Missing or Incomplete Data
Missing data often occurs when important information fields in a record are left blank (null values). This is a serious data quality problem because it can make the record useless for analysis.
Causes:
- Users omit non-required fields in forms.
- Errors in the ETL (Extract, Transform, Load) process cause data loss.
- Poor database design, allowing important information to be missing.
Negative impacts:
- Unable to perform complete analysis.
- Analysis results are biased or inaccurate.
- Customer segmentation and personalization become difficult.
Solution:
- Set mandatory fields: Make sure that all important information cannot be left blank during data entry.
- Data Profiling: Regularly scan the database to identify columns and records with high rates of missing data.
- Use data imputation techniques: Use statistical methods (such as filling in mean, median) or machine learning models to estimate and fill in missing values in the most appropriate way.
- Automated validation rules: Warn or block records that are missing important information at the time of creation.
Inconsistent Data Formats
This is a problem that arises when the same type of information is represented in different formats. For example, dates may be stored as ’09/30/2025′, ‘Sep 30, 2025’, or ‘2025-09-30’. This inconsistency makes data aggregation, sorting, and analysis extremely complex.
Causes:
- No common data dictionary or formatting standard.
- Data is collected from multiple systems, each with its own formatting conventions.
- Human input habits.
Negative impacts:
- Makes data integration and consolidation difficult.
- Data queries and filters return incomplete results.
- Reduces the effectiveness of automated analysis tools.
Solution:
- Build and implement a Data Dictionary: Create a formal document that clearly defines the standard format for each data field (e.g., all phone numbers must be in the format +84-XXXXXXXXX).
- Standardize during ETL: Use data transformation tools to automatically parse and bring all data into a uniform format before loading into the data warehouse.
- Use input controls: In forms, use drop-down lists, date pickers, and input masks to limit user-generated formatting.
Incorrect or Inaccurate Data
Inaccurate data is information that appears to be valid in format but is actually incorrect. For example, a customer’s email address is mistyped by one character, or a product price is entered incorrectly. This is one of the most dangerous data quality issues because these errors are often difficult to detect.
Causes:
- Spelling errors, human errors.
- Outdated information that has not been updated.
- Calculation or logic errors in automated processes.
Negative impact:
- Making business decisions based on incorrect information.
- Failed deliveries, wrong emails, incorrect addresses leading to failed deliveries/mailings.
- Damaging brand reputation and customer relationships.
Solution:
- Use Data Validation Rules: Set up rules to check the validity of data (e.g., customer age must be between 0-120).
- Verify with a third party: Use external services to validate the accuracy of addresses, phone numbers, or emails.
- Periodic data audits: Conduct random or comprehensive audits to review and verify the accuracy of important data.
Outdated or Stale Data
Outdated data is information that was once accurate but is no longer relevant to current reality. For example, the old address of a customer who has moved, or the contact information of a partner who has left a job.
Causes:
- The natural change of information over time.
- Lack of a regular data update process.
- Data stored for too long without being reviewed.
Negative impact:
- Marketing and sales campaigns are targeted to the wrong audience.
- Trend analysis based on old data leads to incorrect forecasts.
- Resources are wasted reaching out to contacts that no longer exist.
Solution:
- Data Refresh Schedule: Automate or schedule routine updates to data from primary sources.
- Use Timestamps: Record the date each record was last created or updated. This makes it easy to identify and filter out old data.
- Data Retention Policy: Establish a data lifecycle policy, defining when data should be updated, archived, or deleted.
Data Integrity Issues
Data integrity is about the consistency of the structure and relationships in the database. Problems arise when these relationships are broken, for example, an ‘order’ record references a ‘customer’ that has been deleted. This undermines the reliability of the entire system.
Causes:
- Poor database design, no relational constraints.
- Application errors allow deletion or update of data without checking related records.
- Errors in data migration that cause loss of links.
Negative impacts:
- Creates meaningless ‘orphan records’.
- Reports may return incorrect or incomplete results.
- Corrupts the application’s business logic.
Solution:
- Use Referential Integrity Constraints: Take advantage of DBMS features (such as foreign keys) to automatically prevent actions that break relationships between tables and data with each other.
- Transaction Management: Ensures that complex database updates are executed as a single transaction: either completely successful or completely failed, with no in-between states.
- Careful database design: Carefully plan and design the database schema to accurately reflect business data relationships.
Data Validity and Rule Violations
Invalid data is data that does not conform to pre-defined business rules or formats. For example, an improperly structured postal code, or a percentage discount for an order that exceeds the maximum allowed.
Causes:
- Omitting validation steps during data entry.
- Changing business rules without updating logic in the system.
- Manual data entry overrides controlled values.
Negative impacts:
- Causing errors when implementing automated processes.
- Leads to inaccurate financial or operational decisions.
- Degrading the overall quality of the data set.
Solution:
- Implement a Business Rule Engine: Centralize the definition and enforcement of business rules, ensuring they are applied consistently across all applications.
- Real-time validation: Integrate validation processes right into the input interface to provide instant feedback to users.
- Regular rule audits: Regularly review business rules to ensure they are still relevant and applied correctly.
Data Consistency Across Systems
Inconsistencies across systems are one of the most complex data quality problems in large organizations. This problem occurs when the same information (e.g., a customer name or address) has different values in the CRM system, the accounting system, and the logistics system.
Causes:
- Data Silos: Different departments use separate systems without connection and synchronization.
- Update schedules vary between systems.
- Lack of an overall data management strategy.
Negative impacts:
- Creates confusion and inefficiencies in operations.
- Unable to get a comprehensive 360-degree view of customers.
- Aggregating reports from multiple sources becomes unreliable.
Solution:
- Implement Master Data Management (MDM): This is the most radical solution, creating a central, single, and trusted source of data.
- Use a Data Integration Platform: Set up automated workflows to synchronize data between systems in a timely and consistent manner.
- Build a Data Governance Framework: Clearly assign roles and responsibilities for ownership and management of important data assets, ensuring consistency and accountability.
Conclusion
Data quality is not a one-time project, but a long-term and continuous process. Data quality issues can arise from many causes, from human error to complex system problems, but they can be completely managed and resolved. Start your journey to improve data quality today. Contact DIGI-TEXX for advice on professional data processing, analysis and cleaning solutions, helping to turn your data assets into a powerful driving force for growth.
=> Read more:
