9 Common Data Quality Problems and How to Fix Them

Data quality problems like inconsistent formats, outdated records, and human errors are frequent hurdles for organizations. By establishing clear data standards, automating checks, and integrating data governance tools, businesses can resolve these issues, ensuring reliable data for better decision-making and compliance.

You can have all the cutting-edge analytics tools, dashboards, and models in place, but if your data isn’t reliable, none of it matters. Poor-quality data continues to be a major roadblock for businesses, costing them time, money, and opportunities. 

A recent McKinsey survey revealed that 82% of organizations spend at least one full day each week fixing master data issues. Even more staggering, 66% rely on manual reviews to monitor and manage their data quality. 

This is a huge drain on resources and productivity.

Inconsistent, incomplete, and inaccurate data are among the most common issues organizations face. These problems prevent teams from generating meaningful KPIs, making timely decisions, or getting a clear view of their customers. As a result, businesses end up stuck, firefighting data errors instead of driving growth.

The worst part? If you’re handling sensitive customer data, these quality issues can put you at risk of non-compliance, which can affect both security and privacy standards. 

In this blog, we’ll dive into the most common data quality problems, explain why they happen, and, most importantly, show you exactly how to fix them before they spiral out of control.

What are data quality problems?

A data quality problem occurs when your data is inaccurate, incomplete, inconsistent, outdated, or duplicated. These issues undermine the reliability of your data and, by extension, the decisions made from it. 

Often rooted in years of ungoverned system growth and poor documentation, these problems are not easy to spot or fix, especially once they’ve spread throughout an organization’s data ecosystem.

Why most data quality problems start at the source

The majority of data quality issues originate in legacy systems, those outdated, sprawling applications and databases that grew without proper oversight. Over time, new systems, processes, and teams have built upon these foundation layers, compounding old problems that weren’t fixed or identified early on.

Common examples:

• Default values: A field that defaults to a date of 01-01-1900, which never gets updated.

• Free text in controlled fields: When numeric fields unexpectedly contain text, like a product code mixed with letters and numbers.

• Duplicate records: Two entries for the same customer due to variations in input methods or system migrations.

• Missing documentation: If a field’s intended use or valid values aren’t recorded anywhere, it’s easy for data entry teams to misuse it.

Since these issues often don’t appear until later in the data pipeline (or after significant processing), downstream teams are unaware of their existence and often inherit them, unknowingly propagating them through reports, dashboards, and analytics.

How to identify data quality issues early

Early identification is key to managing data quality problems before they escalate. Look for these signs, which indicate that your data may be headed for trouble:

• Frequent manual corrections: Are analysts constantly cleaning or fixing reports due to errors?

• Analysts spending more time cleaning data than analyzing it: If your team is firefighting every month, data quality is likely slipping.

• Discrepancies between dashboards and business outcomes: If your data insights aren't matching real-world performance, something is off.

• Increasing user complaints: If users begin distrusting the data, it’s a clear sign that there are issues.

• Inconsistent formats across datasets: Data in one system may be in “MM/DD/YYYY” format, while another uses “DD-MM-YY.”

• ETL failures: Data ingestion errors or schema mismatches are often the result of hidden data issues that break pipelines.

By tracking these early warning signs, you can pinpoint data quality problems before they become costly, time-consuming issues.

9 common data quality problems

Data quality is the foundation of any successful data-driven decision-making process. However, many organizations face persistent challenges in maintaining high-quality data.

In this section, we explore the nine most common data quality problems to help you understand what to look for and how to address them effectively.

1. Inaccurate data

Inaccurate data refers to information that is incorrect or does not align with reality. This can include errors that occur during data entry, discrepancies between systems, or outdated records that no longer reflect the current state of affairs.

Common causes:

• Human error: Manual entry mistakes, like typing errors or entering incorrect data, are common sources of inaccuracies.

• Outdated systems: Legacy systems may hold old or incomplete information, leading to gaps between what’s reported and the actual data.

• Unverified inputs: Data that hasn't been validated or cross-checked, such as customer info entered without confirmation, can lead to inaccurate records.

Business impact:

• Misreporting: Unreliable data leads to incorrect reports and poor decision-making, affecting areas like sales forecasts and financial planning.

• Wrong insights: Inaccurate data skews analysis, resulting in misguided strategies.

• Failed outreach: Incorrect data can cause marketing campaigns or sales efforts to miss their target.

Fix:

• Verification tools: Implement verification tools that cross-check incoming data against trusted databases or external sources.

• Validation rules: Create strict validation rules to check for discrepancies in the data at the point of entry.

• Cross-referencing: Validate data by comparing it with trusted sources like LinkedIn or Clearbit.

2. Incomplete data

 Incomplete data refers to missing essential information or attributes that are necessary for analysis, reporting, or decision-making. This can happen when key fields are left blank, not captured, or overlooked during data entry or processing.

Common causes:

• Optional fields left blank: Users may skip optional fields on forms, resulting in missing data.

• Poor form design: Unclear or poorly designed forms may cause users to unintentionally omit important fields.

• Data entry oversight: Users might forget to input critical information, especially when handling large volumes of data or assuming the data exists elsewhere.

• Data migration issues: System upgrades or migrations can cause certain fields or values to be overlooked or not transferred correctly.

Business impact:

• Inability to segment: Missing data points, like customer contact or purchase history, prevent accurate audience segmentation and affect targeted marketing.

• Inaccurate targeting: Missing details, such as demographics or preferences, lead to ineffective marketing and wasted spend.

• Compliance gaps: Missing data, like consent records, can result in compliance risks, especially in regulated industries.

Fix:

• Mandatory fields: Design forms and databases to make key fields mandatory, ensuring that essential information is always captured before submission.

• Real-time form validation: Implement real-time validation to notify users when they’ve skipped essential fields, so the data entry process is complete and accurate.

• Enrichment APIs: Use third-party data enrichment APIs (e.g., Clearbit, ZoomInfo) to fill in missing information, such as adding a phone number or email address based on publicly available data.

3. Duplicate records

Duplicate records occur when the same entity is entered multiple times in a database or system, often with slight variations in the data. This results in redundant data that can cause confusion, inaccuracies, and inefficiencies in business processes.

Common causes:

• Lack of unique identifiers: Without unique IDs or primary keys, the same entity may be recorded multiple times with different identifiers.

• Inconsistent entry conventions: Different users may enter the same information in varying formats, such as “John Smith” vs. “Smith, John.”

• Merging of data sources: During system migrations or integrations, duplicates can occur if the process doesn’t identify and handle them properly.

• Manual data entry errors: Variations in spelling, formatting, or details introduced during manual data entry can result in duplicate records.

Business impact:

• Inflated KPIs: Duplicates distort KPIs, such as customer counts or sales figures, presenting an inflated view of performance.

• Wasted resources: Marketing campaigns may target the same individual multiple times, wasting budget and potentially annoying customers.

• Confused communications: Customer support teams may struggle to manage inquiries, as duplicates could lead to inconsistent responses or multiple accounts for the same person.

Fix:

• De-duplication scripts: Automate de-duplication processes using scripts to merge duplicate records based on matching or fuzzy identifiers.

• Fuzzy matching: Use fuzzy matching algorithms to detect minor variations and merge records accordingly.

• Canonical ID assignment: Assign a unique ID to each entity to link all records under one primary ID.

• Data merging tools: Use platforms like Dedupe.io or Talend to identify and merge duplicates during data migrations or integrations.

• Data entry guidelines: Set clear guidelines for consistent data entry to prevent accidental duplicates.

4. Inconsistent formats

Inconsistent formats occur when data is represented in different structures, units, or notations across systems or datasets. This lack of uniformity can cause issues with data integration, sorting, analysis, and reporting.

Common causes:

• No standardization: Many organizations lack clear standards for data formatting, leading to mixed formats for similar data points (e.g., dates, currencies, phone numbers).

• Multiple data sources: Data from different systems or departments may use different formats, making it difficult to integrate and analyze.

• User-generated input: When users input data manually, they may use varying formats based on their preferences or regional conventions.

• Lack of governance: Without proper data governance, formatting rules are often ignored, resulting in a mishmash of styles that complicate analysis.

Business impact:

• Errors in sorting/filtering: Inconsistent formats can cause issues when sorting or filtering data, especially if certain formats aren’t recognized correctly by databases or reporting tools.

• Broken analytics pipelines: Inconsistent data can break analytics tools, leading to missed insights or delayed reporting.

• Inefficient operations: Teams spend extra time cleaning and reformatting data, diverting resources from more critical tasks like analysis or decision-making.

Fix:

• Define format rules: Set clear, organization-wide rules for data formatting (e.g., date format, phone number structure, currency symbols) and ensure uniform adherence.

• Apply format standardization tools: Use tools like DataRobot or Alteryx to automate the conversion of data into a standardized format before analysis.

• Use schema validators: Implement validation rules to ensure data adheres to predefined formats before entering the system.

• Integrate data cleaning tools: Leverage platforms like Trifacta or Data Ladder to detect and correct formatting inconsistencies during data integration or transformation.

5. Outdated data

Outdated data refers to information that has changed over time but has not been updated in your system. This could be due to outdated customer information, old product details, or anything that has changed but was not captured in a timely manner.

Common causes:

• Lack of update triggers: Many systems don’t automatically flag outdated information, leaving it up to manual updates that often don’t occur.

• Manual sync processes: When data updates are done manually (e.g., periodic updates or data migrations), it’s easy for records to become stale.

• Failure to implement real-time data validation: Without continuous monitoring, outdated information can go unnoticed.

• Infrequent refresh cycles: Without regular refresh cycles, data can become increasingly inaccurate over time.

Business impact:

• Wrong outreach: Outdated contact details (e.g., emails or phone numbers) lead to misdirected communications, missed opportunities, and damaged customer relationships.

• Incorrect reporting: Outdated data distorts reports, leading to inaccurate decision-making and poor strategic planning.

• Regulatory risk: Using outdated data, particularly in regulated industries like healthcare or finance, can result in compliance violations and legal consequences.

Fix:

• Regular data refresh cycles: Set automatic refresh intervals (e.g., monthly or quarterly) to keep data current.

• Use external validation APIs: Leverage third-party APIs like LinkedIn, Clearbit, or Experian to validate and update data in real-time.

• Implement real-time data validation: Use automated systems to validate and update information as it’s entered, such as email validation tools during form submission.

• Data governance policies: Establish clear policies for data maintenance, ensuring teams regularly review and update records, especially when employees leave or customer details change.

6. Data silos

Data silos occur when information is stored in isolated systems that don’t communicate with each other. These silos prevent seamless data sharing across teams, making it difficult to get a comprehensive view of business operations.

Common causes:

• Departmental tools: Different departments use separate software systems that lack integration with one another.

• Legacy systems: Outdated technology that doesn’t easily connect with modern platforms.

• No central data architecture: Lack of a unified data storage system to centralize all company data.

Business impact:

• Incomplete insights: Teams work with partial data, making decisions based on an incomplete picture.

• Manual workarounds: Employees waste time compiling data from multiple sources.

• Poor collaboration: Silos lead to inefficiencies and errors when teams can’t access each other’s data.

Fix:

• Data integration platforms: Use tools like Fivetran or Talend to connect disparate systems.

• Centralized data lake: Implement a Customer Data Platform (CDP) or data lake to unify data from all departments.

7. Validation failures

Validation failures occur when data doesn’t meet predefined business rules or criteria. This can lead to corrupted records or incorrect data being entered into the system.

Common causes:

• Weak data validation logic: Insufficient or absent rules to check the data for accuracy, completeness, or format before it enters the system.

• Inconsistent rule enforcement: Sometimes validation rules aren’t consistently applied across all data entry points, allowing flawed data to pass through.

Business impact:

• Corrupt records: Invalid data gets stored, which can break workflows, reports, or analytics.

• Failed workflows: When validation fails, downstream systems or processes may fail as they rely on accurate inputs.

• Unreliable dashboards: Invalid or corrupted data skews reports, reducing trust in analytics.

Fix:

• Implement schema constraints: Define validation rules at the database level to enforce correct data formats and business logic.

• Real-time validation: Apply checks during data entry, API ingestion, or ETL processes to catch issues early.

• Alert systems: Set up automated alerts for failed validations to quickly identify and correct issues.

8. Schema drift

Schema drift occurs when there are unexpected changes in data structure over time. These changes can disrupt data pipelines and analytics if they are not properly managed or documented.

Common causes:

• Unannounced updates to APIs: Changes in the structure of external APIs (e.g., new fields, removed fields) that aren’t communicated to downstream teams.

• Pipelines or schema updates: Modifications to data ingestion pipelines or internal schemas without versioning or proper documentation.

• Lack of data governance: Without a formal process for managing schema changes, alterations can go unnoticed, leading to inconsistencies in the data.

Business impact:

• ETL failures: Data extraction, transformation, and loading (ETL) processes fail when the structure of incoming data changes unexpectedly.

• Broken reports: Schema changes can cause dashboards or reports to break, resulting in misleading insights or a lack of reporting.

• Loss of historical comparability: Schema drift can make it impossible to compare historical data accurately, leading to confusion or incorrect trend analysis.

Fix:

• Schema monitoring tools: Use tools like Monte Carlo or Databand to track and alert teams to any changes in schema that could affect data pipelines.

• Versioning protocols: Establish versioning and clear documentation for all schema changes to ensure they are communicated and managed.

• Automated checks: Set up automated schema validation checks to ensure any new data structure is compatible with your existing systems.

9. Human errors

Human errors are mistakes made during manual data entry or updates. These can range from simple typos to incorrect data formatting or misinterpretation of fields.

Common causes:

• Typos: Simple typing mistakes, like misspelled names or incorrect numerical entries.

• Copy-paste errors: Copying data from one system or document to another can introduce errors, especially when data formatting or structure isn’t properly considered.

• Misunderstanding field intent: Data entry staff might misinterpret the purpose of a field, leading to incorrect data being input.

Business impact:

• Mismatched records: Incorrect data entry can cause records to be misaligned, making it hard to identify and manage entities like customers, products, or orders.

• Failed automation: Incorrect data can cause automated systems to malfunction or fail, leading to delays in processes like order fulfillment or customer communications.

• Poor customer experience: If customer data is entered incorrectly, it can result in issues like billing errors, missed communications, or wrong shipments.

Fix:

• UI enhancements: Implement user-friendly input forms with dropdowns, auto-complete features, and real-time validation to reduce human error.

• User training: Provide comprehensive training to data entry teams to ensure they understand the field requirements and the importance of accuracy.

• Audit logs: Maintain detailed records of who enters or modifies data, allowing for quick identification and correction of any errors.

Why do data quality issues arise? (Root causes)

Data quality problems often don't arise overnight. They’re the result of systemic issues that build up over time, often originating from legacy systems, poor governance, and a lack of real-time monitoring.

1. Manual data entry and human errors

A significant portion of data issues originates from manual entry. Typos, inconsistent formatting, and incorrect field selections are common, especially during repetitive tasks or under time pressure. 

Without strong validation rules or employee training, these errors become embedded in systems, eventually impacting reports, analytics, and even customer experiences.

2. Poor integration between tools and systems

When tools don’t integrate smoothly, data often becomes siloed, duplicated, or lost during transfers. Legacy systems may be incompatible with modern platforms, and manual migration only increases the risk of inconsistencies. 

Poor integration creates fragmented datasets, forcing teams to work with incomplete or mismatched information and slowing down operational decision-making.

3. Absence of data governance policies

Without clear governance policies, organisations lack defined standards for data accuracy, ownership, and validation. Teams may follow different rules, or none at all, resulting in inconsistent definitions of “clean” data.

 Over time, errors go unnoticed, leading to unreliable insights and increased operational or compliance risks.

4. Lack of real-time monitoring

Systems that rely on batch updates delay the detection of issues. Without real-time monitoring, errors can accumulate for months before being discovered, affecting dashboards, forecasts, and customer interactions. 

Real-time validation helps catch mistakes early and prevents downstream impact.

5. Buried issues in legacy source systems

Data problems often originate in legacy source systems where issues like undocumented defaults, inconsistent coding, or misused columns are hidden. These problems are difficult to spot unless there’s proper data profiling or metadata inspection.

Once buried in older systems, these issues often carry over into modern data pipelines, causing corruption, inconsistencies, and trust gaps that affect data quality downstream.

How to fix common data quality issues (Step-by-step)

Fixing data quality issues requires a structured, step-by-step approach to ensure long-term improvements. Here’s a proven framework to help you tackle common data problems.

Step 1: Define clear data standards & ownership

Start by setting clear definitions, rules, and accountability. Without standards and owners, data becomes inconsistent and unreliable. Create a data dictionary, define accepted formats, and assign responsibility for each data domain.

Actionable steps:

1. Create a data dictionary that includes all data definitions, formats, and standards for your organization.

2. Assign data ownership to specific individuals or teams for different types of data (e.g., sales data, marketing data).

3. Establish data governance protocols with a clear RACI model (Responsible, Accountable, Consulted, Informed) to define roles and responsibilities.

Step 2: Implement automated validation rules

Automated validation catches errors early, preventing bad data from entering your systems. This reduces manual checks and ensures accuracy across the pipeline.

Actionable steps:

1. Implement real-time validation during form entry or data import to ensure that fields meet specific criteria (e.g., required fields, proper formats).

2. Set up automated checks during ETL (Extract, Transform, Load) processes to validate incoming data before it’s ingested into your system.

3. Integrate automated alerts for failed validations, so teams are immediately notified when invalid data is detected.

Step 3: Regular data cleansing & enrichment

Ongoing cleansing ensures data remains accurate, complete, and standardized. Enrichment fills in missing details and strengthens downstream analytics.

Actionable steps:

1. Schedule regular data cleansing jobs to identify and remove duplicates, correct formatting issues, and standardize data across systems.

2. Use enrichment APIs to fill in missing or incomplete data, such as customer emails, phone numbers, or company information.

3. Monitor data quality metrics like completeness and consistency on a monthly or quarterly basis to prioritize high-value datasets for cleaning.

Step 4: Establish a data quality dashboard

A data quality dashboard provides real-time visibility into the health of your data. A well-designed dashboard can highlight areas that need attention and provide a quick overview of data quality health across the organization.

Actionable steps:

1. Define key data quality metrics (e.g., completeness, accuracy, uniqueness) that matter most to your business.

2. Build a dashboard using tools like Power BI, Tableau, or Superset to track and visualize these metrics in real-time.

3. Set up alerts for thresholds (e.g., if data completeness drops below 95%) to quickly address potential issues before they escalate.

Step 5: Automate remediation & feedback loops

Once data issues are detected, it’s important to have automated systems in place for remediation. In some cases, automation can fix issues on its own, such as correcting data formatting or merging duplicate records. For more complex issues, create workflows that notify data owners or stakeholders to take corrective action.

Actionable steps:

1. Implement automated remediation for common issues, such as normalizing formats, auto-merging records, or filling in missing fields.

2. Create workflows for manual remediation, integrating them with ticketing or operations tools (e.g., Jira, ServiceNow) for seamless issue tracking.

3. Enable user feedback to refine rules and improve processes continuously.

Conclusion

Data quality isn’t just an IT problem; it directly impacts your decisions, customer experience, and bottom line. When your data is inaccurate, inconsistent, or outdated, every insight becomes weaker, and every process slows down. But with clear standards, automated validation, and continuous monitoring, you can turn chaotic data into a reliable asset that drives growth.

Improving data quality doesn’t have to be overwhelming. Start small, fix the highest-impact issues, and build a culture where clean data becomes the default.

If you want to streamline data governance and automate quality checks at scale, OvalEdge gives you the tools to do it efficiently and confidently. 

Ready to take control of your data? Book a demo and explore OvalEdge today.

FAQs

1. What are the signs of poor data quality?

Common signs of poor data quality include inconsistent reports, frequent manual corrections, missing or duplicate records, and declining trust in analytics or dashboards. These issues typically indicate underlying problems in data collection, integration, or governance.

2. How does poor data quality affect AI and analytics?

Poor data quality skews AI models, leading to inaccurate predictions and unreliable insights. When the data fed into AI systems is flawed, it results in reduced model performance, slow decision-making, and a loss of confidence in the AI outputs.

3. What is considered good data quality in an organization?

Good data quality means the data is accurate, complete, consistent, and timely. Organizations often set benchmarks such as 95% completeness and less than 2% duplication to ensure reliable and actionable data.

4. Who is responsible for ensuring data quality?

Data quality is a shared responsibility among data owners, stewards, and IT teams. Collaboration across departments is essential to define standards, maintain accuracy, and monitor data quality over time.

5. How long does it take to improve data quality?

Organizations can see early improvements within weeks by implementing quick fixes like deduplication and validation. However, long-term results, such as automation and cultural alignment, typically take 6 to 12 months to fully implement.

6. What is the ROI of improving data quality?

High-quality data reduces errors, improves decision-making speed, enhances compliance, and boosts customer experiences. Many companies see measurable ROI within months through reduced operational costs and more accurate analytics.