How to spot and solve common data quality issues

Your marketing dashboard shows a 200% increase in conversion rates overnight. Amazing! Hats off to the marketing team.

You check the underlying data quickly, though, only to find corrupted tracking codes inflated the numbers.

Scenarios like this out daily across organizations, where data quality issues silently undermine business metrics.

For businesses focused on data-driven operations, data reliability directly impacts the ability to make informed decisions, serve customers effectively, and maintain a competitive advantage. Yet many organizations struggle to identify and resolve data quality issues before they impact the business.

Below, we'll explore some of the most common data quality issues and provide practical tips on how to implement better data governance strategies to identify and solve them.

8 common data quality issues and how to identify them

While there's an indefinite number of data quality issues out there, and each organization experiences its own unique ones, eight common problems account for the majority of incidents. Knowing how to identify and proactively address data quality issues early can prevent a waterfall of failures from sweeping across your data ecosystem.

1. Incomplete data

Missing data points create blind spots in your data analysis. Imagine running a customer retention analysis only to discover that 30% of your churn dates weren't recorded. The resulting insights would be fundamentally flawed.

Incomplete data often stems from multiple sources: failed ETL jobs, API timeouts, human error in data entry, or system migrations. In e-commerce, for instance, missing shipping addresses might indicate checkout process issues, while gaps in product inventory data could signal data warehouse systems failing to communicate with one another.

How to identify incomplete data issues: To identify incomplete data, regularly audit your critical fields for null values or missing entries. Look for patterns in the gaps. Are certain time periods or data sources consistently incomplete? Pay special attention to system boundaries where data moves between platforms, as these transition points often introduce completeness issues.

How to respond to incomplete data issues: Start by getting clear on what "complete" means for your critical data fields. What fields are required, and what can be optional? Write this down as your baseline. Then, you'll want to set up automated monitoring to alert you to any gaps or missing information.

2. Duplicate data

Beyond wasting storage, duplicate records distort aggregations and metrics. A sales report showing twice the actual revenue due to duplicated transaction records can lead to dangerously misguided business decisions.

Duplicate data is a common challenge due to the complex environments data engineers manage. Customer records can multiply when data pipelines pulling from multiple systems aren't properly integrated. Transaction data duplication can occur when retry mechanisms in distributed systems lack adequate safeguards. 

Similarly, product catalogs may spawn duplicate entries when siloed teams upload unsynced datasets to shared data lakes or warehouses. Each instance compounds the problem, making it harder to determine which version of a record is the source of truth.

How to identify duplicate data issues: Monitor for identical records across key fields and watch for sudden increases in record counts without corresponding business growth. Pay special attention after data migrations or system integrations.

How to respond to incomplete data issues: When you spot duplicate data causing trouble, start by putting some guardrails in place. Add unique constraints to your critical tables to prevent duplicate entries from sneaking in. Then, build data deduplication checks into your data pipeline to catch and clean up any doubles that might try to slip through. This essentially puts two layers of protection in place to keep your data clean and trustworthy.

3. Inaccurate data

Sometimes data exists but is simply wrong. Whether from human error, system bugs, or integration issues, inaccurate data breeds mistrust in your entire data ecosystem.

How to identify data accuracy issues: Look for incorrect data values outside expected ranges, mismatched totals between systems, and anomalies in trend lines. User feedback often provides early warning of accuracy issues.

How to respond to data accuracy issues: To tackle inaccurate data, start by setting up some smart checkpoints throughout your data pipeline. These points will check to see if your data looks reasonable and catch any suspicious values. Building in these validation rules and outlier checks at different stages helps you catch odd data before it causes problems downstream.

4. Inconsistent data

The issue of inconsistent data multiplies in modern data architectures, where information flows between numerous specialized systems and various sources. For example, customer data might be stored in separate pipelines or replicated across distributed databases, each governed by different update schedules, schema designs, or transformation rules. 

Without proper synchronization and validation mechanisms, these systems diverge over time, creating conflicting states that ripple through reporting, analytics, and customer-facing applications.

How to identify data consistency issues: Track discrepancies between related systems and monitor for unexpected changes in established relationships between data points. Build reconciliation reports comparing key metrics across systems and investigate any variances above acceptable thresholds.

How to respond to data consistency issues: Map out where your critical data lives across different systems. Think of it as creating a blueprint. You'll want to clearly establish which system is the "source of truth" for each piece of key business data. Then, document these ownership rules and set up automated sync processes to keep everything in harmony across your data ecosystem.

5. Outdated data

Stale data can be worse than no data at all. Using last quarter's pricing data for current revenue forecasts or expired customer preferences for personalization erodes business effectiveness.

The challenge of data freshness becomes increasingly complex in real-time business environments. For example, an e-commerce platform using outdated inventory data might oversell products, while a financial services application relying on stale market data could make costly trading errors.

How to identify outdated data: Keep an eye on "new data" metrics and track last-updated timestamps across critical datasets. Watch for processing delays or failed updates. Implement SLAs for data freshness based on business requirements and monitor compliance across your data pipeline. Consider implementing "time-to-live" policies for sensitive data elements.

How to respond to outdated data: Ask yourself how fresh each dataset needs to be. Some data might need updating every minute, while daily or weekly updates work fine for others.  Once you've set these "freshness" targets, build alerts that notify you when data hasn't been updated within its expected timeframe. Then, create a simple dashboard to monitor update patterns.

6. Invalid data

An incorrect email address might pass basic syntax validation but fail deliverability tests, creating a false sense of security in communication metrics. Similarly, product SKUs that deviate from established patterns might be technically valid strings but cause inventory reconciliation issues or break integration with supplier systems. The impact continues to build when invalid data passes through multiple transformation steps before ever being detected.

How to identify invalid data issues: Establish clear validation rules and monitor violation rates. Watch for spikes in error logs or failed transformations. Develop comprehensive data quality rules that go beyond basic format validation to include business logic checks and cross-field validations. Track validation failure rates by source system to identify problematic data providers or input methods.

How to respond to invalid data issues: First, determine what "valid" means for each type of data you're handling. Write these rules down so everyone can find them. Then, set up validation checks where data enters your systems. Finally, create a "quarantine zone" for data that fails these checks. This way, suspicious data gets flagged for review before it spreads to the rest of your systems.

7. Schema changes

Unexpected schema changes break data pipelines and corrupt downstream systems. A simple column rename can cascade into dozens of broken dashboards. Schema changes are particularly dangerous because they represent a "silent killer" of data quality. Unlike obvious errors like missing or duplicate data, schema changes can appear harmless at first glance. 

A developer might change a field from `customer_id` to `user_id` in one system, believing it's a simple improvement in naming consistency. However, this seemingly innocent change can trigger a chain reaction: ETL jobs fail, API integrations return errors, BI tools lose column mappings, and machine learning models trained on the old schema suddenly stop working. By the time the impact becomes visible, the damage is often widespread.

How to identify schema change issues: Watch for unauthorized schema modifications and track schema version history. Build automated testing into your deployment pipeline that validates schema compatibility across your entire data ecosystem. Perhaps most importantly, be sure to maintain documentation of schema dependencies to quickly assess the potential impact of proposed changes.

How to respond to schema change issues: When someone proposes a schema change (like renaming a column or adding a new field), make sure there's a clear review process. This means mapping out which systems might break if you make the change (like reports, dashboards, or APIs) and getting approval from the teams who rely on that data. 

8. Orphaned data

Records without proper relationships create referential data integrity issues. Orphaned data often reveals deeper systemic issues in data architecture and process flows. 

For example, an order record might become orphaned when customer data is archived without considering active orders, or when a product is deactivated while still referenced in active promotional campaigns. These broken relationships create a domino effect: Revenue reports become inaccurate because they can't properly attribute sales, customer lifetime value calculations exclude valid transactions, and recommendation engines work with incomplete product relationships. Even worse, orphaned data can hide silently in your systems for months before its impact becomes apparent in downstream analytics.

How to identify orphaned data: Regularly audit relationship integrity and monitor foreign key violations. Watch for spikes in unmatched records after system updates. Implement regular health checks that verify referential integrity across your entire data ecosystem—not just within individual databases.

How to respond to orphaned data issues: Build some safety nets into your database. Add referential integrity constraints that prevent records from losing their connections, like making sure an order can't exist without a matching customer. Keep an eye on relationship health, and maintain a clear flow of how your data links together. This makes it much easier to prevent orphaned records and troubleshoot any issues that come up.

The impact of data quality issues on businesses

Poor data quality costs organizations an average of $12.9 million annually according to Gartner's data quality assessment in 2021. Yet this figure only represents direct costs like lost revenue and wasted resources. The true impact ripples throughout organizations in a few critical ways:

Operational inefficiency

When data quality issues strike, they paralyze operations across departments:

• Data engineers reportedly spend up to 61% of their time investigating and fixing data quality issues instead of building new capabilities.
• Marketing teams waste hours manually reconciling campaign data across different platforms when automated reporting breaks.
• Finance departments delay critical reports while validating numbers across multiple systems.
• Sales teams lose productivity by double-checking customer information across CRM and billing systems.
• BI teams have to rebuild dashboards and reports when underlying data structures change without notice.

The cost multiplies as issues cascade—a single data quality problem can force dozens of employees to pause their work while engineers investigate root causes.

Decision paralysis

Poor data quality erodes organizational confidence in data-driven decision-making:

• Executives delay strategic decisions when they can't trust their metrics.
• Product teams hesitate to launch features without reliable A/B test results.
• Sales leaders question revenue forecasts when they find inconsistencies.
• Marketing managers revert to "gut feel" for budget allocation when attribution data proves unreliable.
• Operations teams maintain excessive safety stock when they can't trust their inventory data.

This hesitation to act on data creates missed opportunities and competitive disadvantages that compound over time.

Customer experience degradation

Finally, data quality issues directly impact customer relationships in ways that damage brand reputation:

• Customers might receive duplicate emails because of syncing issues between marketing systems.
• Support teams provide incorrect information based on outdated customer records.
• Personalization efforts backfire when recommendation engines use corrupted preference data.
• Billing errors occur due to mismatched product and pricing data.

What makes these customer impacts particularly damaging is that they often go unreported. For every customer who complains about a data-driven issue, there are likely many more who simply take their business elsewhere.

These cascading effects demonstrate why data quality management isn't just an engineering concern; it's a business imperative that affects every aspect of modern operations. Organizations need systematic approaches to detect and resolve data quality issues before they impact business outcomes.

How a data observability tool improves data quality

Just like you can't fix a leak you haven't found, you can't improve data quality without measuring it first. The faster you spot issues, the faster you can resolve them. Manual checks can help, but it isn't a scalable approach, and it'll always leave you playing catch-up—discovering problems only after they've already impacted the business. 

Data observability tools help solve this issue with key capabilities like:

• Automated anomaly detection
• End-to-end lineage tracking
• Schema change monitoring
• Data freshness tracking
• Quality metric trending
• Cross-system consistency checking

My monitoring your data pipeline from ingestion to end destination, data observability helps you catch data quality issues before they impact your business.

Improve the quality of your data easily with Metaplane

Building trust in your data requires both proactive monitoring and rapid response when issues arise. Metaplane provides the comprehensive observability toolkit data engineers need to prevent inaccuracies and maintain high-quality data across their organization.

Don't wait for your stakeholders to report data issues. Take control of your data quality today by signing up for a Metaplane account. Our monitoring and alerting platform gives you confidence in your data, letting you focus on driving business value instead of putting out fires. 

Sign up for Metaplane today and see the difference that automated data observability can make.