14 Most Common Data Quality Issues and How to Fix Them

Are hidden data quality issues skewing your results?

From missing values and duplicates to outdated or inconsistent entries, these issues seem minor, but their impact can ruin your reports, dashboards, and decision-making processes.

Identifying bad data streamlines AI-led processes, optimizes BI workflows, and empowers teams to make real-time, data-driven decisions. That’s why identifying and addressing data quality issues early is crucial for ensuring the security of your business operations.

In this blog, we’ll explore the 14 most common data quality issues and provide actionable steps mixed with Hevo’s automation capabilities. 

Keep your data clean, consistent, and trustworthy!

Why Data Quality Issues Can’t Be Ignored?

Inaccurate or incomplete data doesn’t just create confusion; it weakens the very foundation of analytics, decision-making, compliance, and revenue performance.

Let’s break down the impact:

1. Disrupts analytics

When datasets contain missing values, duplicates, or inconsistent formats, even the most advanced data pipeline tools deliver misleading results. Instead of identifying new opportunities or patterns, your dashboards end up reflecting a distorted version of reality.

For instance, marketing teams may double down on campaigns that seem profitable while targeting the wrong audience based on skewed segmentation data.

2. Weakens decision-making

Executives and teams depend on accurate data to make decisions. Outdated or incorrect information causes poor resource allocation, flawed business strategies, and operational inefficiencies that compound over time.

For example, in 2012, JPMorgan’s “London Whale” trading loss of over $6 billion was partly attributed to a spreadsheet error.

3. Increases compliance risks

Regulatory frameworks such as GDPR, HIPAA, and SOC 2 demand transparency, accuracy, and traceability in how data is stored and used. Poor-quality data makes compliance reporting error-prone, increasing the risk of penalties, audits, and legal exposure.

Beyond regulations, inaccurate records can compromise internal governance, making it harder to track user consent and verify data sources.

4. Impact on revenue

Every flawed entry in your CRM or data warehouse translates into a tangible business cost. Incorrect pricing, duplicate leads, or outdated contact information result in missed sales opportunities, wasted marketing spend, and poor personalization.

For example, IBM reported that poor data quality costs the U.S. economy over $3.1 trillion annually.

Here’s a real-life example of how Hevo solved data quality issues:

Company: Postman, a leading API development and testing platform used by millions of developers worldwide.

Challenge: Postman faced frequent data pipeline breakages, inconsistent data flows, and limited connector support with their previous ETL tool.

Solution: Hevo Data provided a robust, no-code integration platform that ensured accurate, real-time data flow across multiple SaaS and internal sources. Its data pipeline automation, covering error handling, reliable connectors, and schema management, eliminated broken pipelines and data inconsistencies.

Hevo has great coverage, they keep their integrations fresh, and the tool is super reliable and accessible. The team was very responsive as well, always ready to answer questions and fix issues. It’s been a great experience!

Prudhvi Vasa

Head of Data, Postman

Results: Postman saved 30–40 developer hours each month, eliminated recurring data failures, and empowered analysts to onboard new data sources within an hour.

The Most Common Data Quality Issues (With Fixes)

Below are the most common issues businesses face and how to fix them effectively (with Hevo):

1. Duplicate data

Duplicate records distort analytics and waste resources; for instance, marketing teams might target the same customer multiple times. These duplicates usually arise from multiple data sources feeding into your system without unique identifiers or synchronization logic.

How to fix:

• Manual fix: Regularly deduplicate records using unique IDs and enforce data entry standards across teams.
• Automated fix: Implement automated deduplication logic within your ETL or CRM pipeline to identify and merge duplicates before data reaches analytics tools.

Hevo’s fix:

Leverage Hevo’s transformation layer to define custom deduplication rules at the pipeline level. You can use SQL-based or drag-and-drop transformations to remove duplicate records during ingestion.

2. Inaccurate data

Missing or incorrect values lead to misleading insights. A single blank field in revenue or region data can translate into flawed reports. This happens due to manual errors or broken integrations.

How to fix:

• Manual fix: Validate key fields periodically and introduce mandatory fields in forms or CRMs. Use sampling audits to catch recurring gaps.
• Automated fix: Add data validation rules during ingestion (e.g., “order amount must be > 0”) to automatically reject or flag incomplete data.

Hevo’s fix:

Use Hevo’s built-in data validation rules to detect incomplete records. You can configure alerts for failed validations to fix issues proactively.

3. Inconsistent formatting

Data stored in inconsistent formats, such as dates, currencies, or naming conventions, causes misalignment between systems. For example, “US” vs. “United States” or “01/10/25” vs. “10/01/25” can lead to reporting discrepancies and integration failures.

How to fix:

• Manual fix: Align data entry formats across departments and document naming standards.
• Automated fix: Apply formatting and normalization transformations at the pipeline level to ensure uniformity.

Hevo’s fix:

Define format normalization steps directly within the Hevo data pipeline editor. You can automate these transformations so every dataset entering your warehouse adheres to consistent formats across all sources.

4. Outdated data

Outdated data leads to poor business decisions. Outdated customer preferences, expired product details, or old financial records can mislead teams and reduce campaign effectiveness.

How to fix:

• Manual fix: Set clear data refresh policies and periodically audit key datasets.
• Automated fix: Enable incremental syncs that capture only new or changed records automatically.

Hevo’s fix:

Set up incremental or real-time sync modes in Hevo to keep your data up to date. Pair this with Hevo’s pipeline monitoring dashboard to track updates and receive alerts if syncs fail.

5. Ambiguous data

When teams interpret data fields differently, for example, “customer” meaning new leads for marketing but active buyers for sales, reports become inconsistent. Ambiguous definitions create confusion and lead to conflicting KPIs.

How to fix:

• Manual fix: Create a centralized data glossary or dictionary defining key business terms and metrics.
• Automated fix: Apply consistent metadata tagging and standardized schemas across systems to maintain clarity and traceability.

Hevo’s fix:

Use Hevo’s schema management features to enforce uniform field definitions and naming conventions across all connected sources.

6. Hidden or Dark Data

“Dark data” refers to information that exists but isn’t used, often buried in logs, emails, or legacy systems. This unused data creates blind spots, preventing organizations from leveraging valuable insights.

How to fix:

• Manual fix: Conduct data discovery tasks to locate and catalog unused datasets. Eliminate obsolete data and bring relevant sources into your analytics ecosystem.
• Automated fix: Use data integration and cataloging tools to connect siloed sources and automatically surface hidden datasets.

Hevo’s fix:

Connect Hevo’s 150+ pre-built integrations to uncover and unify siloed datasets. You can integrate lesser-used sources, like support tickets, logs, or ad platforms, into your main analytics stack, transforming dark data into valuable insights.

7. Orphaned data

Orphaned data are records without ownership, source clarity, or a clear purpose, piling up across systems. These records lead to duplication and trust issues since teams can’t validate them. Over time, it inflates storage costs and weakens governance.

How to fix:

• Manual fix: Assign clear data owners and maintain a centralized catalog that documents each dataset’s source and purpose.
• Automated fix: Deploy metadata management tools that automatically tag datasets, track lineage, and maintain contextual links.
  

Hevo’s fix:

Use Hevo’s data lineage and pipeline tracking to visualize data flow from source to destination. You can assign ownership and maintain full context across every pipeline, ensuring all datasets remain traceable and governed.

8. Unstructured data (Text, Images, Logs, etc.)

Unstructured data, like logs, documents, and text inputs, lacks a consistent schema, making it difficult to analyze or integrate with structured systems. These datasets cause data silos and lost opportunities for insight.

How to fix:

• Manual fix: Define a process to tag, classify, and convert unstructured data into standardized formats for easier storage and access.
• Automated fix: Use ETL tools capable of parsing unstructured sources and transforming them into analytics-friendly formats.
  

Hevo’s fix:

Hevo supports semi-structured formats such as JSON and XML, allowing you to parse, clean, and standardize data during ingestion. You can apply transformation logic within pipelines to make unstructured data ready for analysis.

9. Data overload

Collecting vast amounts of data without purpose creates “data noise.” Analysts spend more time filtering irrelevant metrics than deriving insights. Without context or prioritization, valuable insights get buried under unnecessary volume.

How to fix:

• Manual fix: Audit existing data sources and remove low-utility or redundant datasets to streamline analysis.
• Automated fix: Implement pre-ingestion filtering and transformation rules that only capture business-relevant attributes.
  

Hevo’s fix:

Use Hevo’s selective data ingestion to load only the tables and fields that matter. Combined with pre-load transformations, you can filter unnecessary data at the source, keeping your warehouse lean and high-impact.

10. Data downtime (Pipeline or ETL Failures)

Pipeline failures or integration downtime can disrupt reporting, delay insights, and erode stakeholder confidence. Downtime often stems from schema changes, broken connections, or failed syncs.

How to fix:

• Manual fix: Conduct regular pipeline audits and maintain documentation for schema dependencies and update cycles.
  
• Automated fix: Set up real-time monitoring, health checks, and alerts to detect and resolve pipeline issues proactively.
  

Hevo’s fix:

Hevo provides real-time monitoring and alerting that tracks pipeline health. Detect sync errors instantly and take corrective action to minimize data downtime and keep analytics uninterrupted.

11. Irrelevant data

Including non-actionable data in reports clutters dashboards and distracts teams from core metrics. Irrelevant datasets occur when multiple teams feed inconsistent KPIs into centralized dashboards.

How to fix:

• Manual fix: Clearly define reporting objectives and KPIs, and exclude vanity metrics from dashboards.
• Automated fix: Configure filtered datasets or transformation workflows that exclude non-essential attributes before loading.
  

Hevo’s fix:

With Hevo’s pre- and post-load transformations, you can clean, filter, and enrich data before it reaches your BI layer.

12. Human error

Manual data entry or configuration mistakes, from typos to mismatched fields, remain one of the biggest causes of bad data. These errors compound across systems and can completely distort analytics.

How to fix:

• Manual fix: Establish strict data entry protocols and schedule regular validation checks to detect inconsistencies early.
• Automated fix: Automate data ingestion, mapping, and validation to eliminate dependency on manual inputs.
  

Hevo’s fix:

Hevo minimizes manual involvement through fully automated ingestion and transformation pipelines. Once configured, workflows run consistently and error-free, ensuring accuracy across every data stream.

13. Schema drift

Schema drift occurs when the structure of incoming data changes unexpectedly, such as renamed fields or new columns, breaking pipelines and corrupting downstream analytics. It’s a major risk in dynamic data environments with frequent source updates.

How to fix:

• Manual fix: Maintain schema version documentation and manually review changes before integration updates.
• Automated fix: Implement automated schema detection and alerting systems that flag or adapt to changes in real time.
  

Hevo’s fix:

Hevo automatically detects schema drift across data sources and adapts pipelines accordingly. You can configure it to either auto-update schema mappings or send alerts for manual review, ensuring uninterrupted data flow.

14. Lack of data lineage & traceability

Without clear visibility into where data originates, how it’s transformed, and where it’s used, organizations struggle to trust analytics. Missing lineage makes compliance audits difficult and slows down root-cause analysis.

How to fix:

• Manual fix: Maintain documentation or flow diagrams that map how data moves across systems and transformations.
• Automated fix: Use lineage-tracking tools that log every transformation, sync, and dependency automatically.
  

Hevo’s fix:

With Hevo’s built-in lineage tracking, you can visualize data flow from source to warehouse, including transformations, sync frequency, and destinations. Teams achieve complete traceability and simplify audits, debugging, and compliance reporting.

How to Fix Data Quality Issues Step by Step?

We have broken down the process of fixing data quality issues in this section, along with Hevo’s assistance in the process:

1. Assess current data quality

Use data profiling and auditing to evaluate key quality dimensions such as completeness, accuracy, consistency, and timeliness. Identify high-impact tables, recurring errors, and data sources contributing to poor quality.

How Hevo helps:

Hevo’s built-in observability and profiling help monitor pipelines, detect anomalies, and catch schema issues early.

2. Define clear data quality standards

Establish a shared understanding of what “good data” means for your organization. Define data validation rules (e.g., mandatory fields, valid ranges, unique identifiers) and standardized formats (dates, currencies, naming conventions).

How Hevo helps:

With Hevo, you can set validation rules and transformations to automatically flag or filter invalid records before loading.

3. Automate data validation

Manual cleanup is error-prone and doesn’t scale. Automate your data validation, cleansing, and transformation workflows to catch issues before they disrupt analytics or reporting. Deduplication, normalization, and enrichment should be built into your ingestion process.

How Hevo helps:

Hevo’s no-code transformations standardize data automatically, keeping it consistent in real time.

4. Implement monitoring & alerts

Set up real-time monitoring and alerting for key metrics like data freshness, volume anomalies, and schema changes. Proactive alerts minimize downtime and help teams prepare for system failures.

How Hevo helps:

Hevo’s monitoring alerts teams instantly to pipeline issues, schema drift, or ingestion failures.

5. Establish governance & ownership

Long-term data quality requires clear ownership and governance structures. Assign data owners for each domain, define SLAs for issue resolution, and document lineage for transparency.

How Hevo helps:

Hevo provides pipeline-level lineage, audit trails, and activity logs, helping teams trace data flow from source to destination.

Turning Data Chaos into Clarity With Hevo

High-quality data is the foundation of every reliable business decision. Poor data can distort analytics, slow down workflows, increase compliance risks, and directly impact revenue.

Proactive monitoring and automation are key. Maintaining data quality ensures your teams can trust the insights they rely on, act faster, and optimize operations across AI-driven processes and BI workflows.

Modern tools like Hevo simplify this process by automating validation, transformation, and pipeline monitoring, allowing you to focus on strategic decisions rather than firefighting bad data.

Start your 14-day free trial with Hevo and see how automated pipelines keep your data clean, consistent, and analytics-ready. 

FAQs on Data Quality Issues