In the age of data-driven decision-making, poor data quality is a silent yet costly threat. According to Gartner, poor data quality costs organizations an average of $12.9 million annually. For data engineers, IT leaders, and business analysts, addressing these issues is critical to maintaining operational efficiency, achieving compliance, and driving business growth.
This blog outlines the top five most common data quality issues, their impact, and actionable strategies to resolve them effectively.
What Are Data Quality Issues?
Data quality issues arise when data is incomplete, inconsistent, duplicated, or outdated, making it unreliable for analytics, reporting, or operational use. A study by Experian found that 69% of organizations believe inaccurate data impacts their ability to provide excellent customer service, highlighting the far-reaching consequences of poor data quality.
Top 5 Data Quality Issues
1. Duplicate Records
Problem: Duplicate records occur when the same entity—such as a customer, product, or supplier—is recorded multiple times in your database.
Impact:
- Skewed analytics and reporting.
- Confusion in customer service and sales.
- Increased storage and processing costs.
Example:
A CRM system contains three records for the same customer due to variations in how their name and address were entered (e.g., “John Doe,” “Jon Doe,” and “J. Doe”).
Solution:
- Use Deduplication Tools: Implement MDM or data cleaning tools like Syncari to detect and merge duplicate entries automatically.
- Enforce Data Entry Standards: Require unique identifiers (e.g., customer ID) to ensure consistency.
2. Missing Fields
Problem: Missing or incomplete data fields prevent teams from gaining full insights or executing key business processes.
Impact:
- Inaccurate analytics and reports.
- Inability to contact customers or process transactions.
- Delayed operations.
Example:
A retail database is missing 20% of customer email addresses, hampering marketing campaign effectiveness.
Solution:
- Automate Data Enrichment: Use tools to pull missing information from trusted external sources (e.g., LinkedIn or Clearbit).
- Validate at Entry: Set required fields and validation rules in your systems to ensure key data points are always captured.
3. Inconsistent Data Formatting
Problem: Variations in data formatting across systems make integration and analysis difficult.
Impact:
- Data mismatches during system integration.
- Errors in analytics models.
- Reduced operational efficiency.
Example:
Phone numbers in a database are entered in various formats, such as “(123) 456-7890,” “123-456-7890,” and “1234567890.”
Solution:
- Standardize Data Entry Rules: Implement formatting rules for critical fields like dates, phone numbers, and addresses.
- Use Transformation Tools: Utilize ETL (Extract, Transform, Load) pipelines to normalize data across systems.
Experian reports that 95% of businesses see impacts on customer trust and perception due to inconsistent data formatting.
4. Outdated or Stale Data
Problem: Over time, data becomes outdated, rendering it irrelevant or incorrect.
Impact:
- Poor decision-making based on obsolete data.
- Inefficiencies in customer outreach or logistics.
- Regulatory non-compliance risks.
Example:
A shipping company uses an outdated address to deliver a package, resulting in failed delivery and additional costs.
Solution:
- Set Data Refresh Schedules: Regularly update key datasets using automation tools.
- Enable Real-Time Synchronization: Use platforms like Syncari to synchronize data across systems in real-time.
More than one-quarter of global data and analytics employees who claim poor data quality is an obstacle to data literacy at their organization estimate they lose more than $5 million annually due to poor data quality, with 7% reporting they lose $25 million or more, according to Forrester’s Data Culture And Literacy Survey, 2023
5. Lack of Data Governance
Problem: Without clear ownership and governance policies, data becomes inconsistent, duplicated, and unreliable.
Impact:
- Reduced accountability for data accuracy.
- Inconsistent data usage across departments.
- Difficulty in achieving compliance with regulations like GDPR or HIPAA.
Example:
Two departments within a company maintain separate versions of the same dataset, each with conflicting information.
Solution:
- Implement a Data Governance Framework: Assign data stewards and establish policies for data ownership and maintenance.
- Adopt a Single Source of Truth (SSOT): Use MDM systems to create a unified data repository.
Gartner predicts that by 2027, 60% of organizations will fail to realize the anticipated value of their AI use cases due to incohesive data governance frameworks.
How to Maintain Data Quality Over Time
- Monitor Continuously
Use automated tools to scan for issues like duplicates, missing fields, and stale data regularly. - Invest in Training
Educate teams on the importance of accurate data entry and standardization practices. - Adopt Modern Data Platforms
Cloud-native and no-code platforms like Syncari simplify data quality management with built-in automation and real-time synchronization. - Track KPIs
Measure metrics like error rates, duplicate counts, and data completion levels to assess ongoing quality improvements.
Data quality issues are more than just an inconvenience—they directly impact your organization’s bottom line. From duplicate records to missing fields, each challenge can be addressed with the right combination of tools, processes, and governance frameworks.
By tackling these issues head-on, organizations can unlock the full potential of their data, improving decision-making and driving business success.
