10 common data quality issues (and how to fix them for good)

You launch your most anticipated marketing campaign of the year. The messaging is perfect, the creative is compelling, and the budget is approved. But weeks later, the results are bafflingly poor. Engagement is low, conversion rates are flat, and a significant portion of your emails have bounced.

The post-mortem reveals the culprit wasn’t the strategy, but the data fueling it: duplicate records received multiple messages, key contacts were assigned to the wrong industry segment, and many email addresses were simply out of date.

This scenario isn’t a fluke; it’s a daily reality in businesses worldwide. It’s a symptom of poor data quality, a silent saboteur that undermines strategy, erodes customer trust, and quietly drains resources. Understanding this problem is the first step toward solving it.

This guide provides a definitive overview of the 10 most common data quality issues that businesses face. We will go beyond definitions to explore each problem with real-world examples, break down their direct impact on your business, and provide a clear framework on how to fix them for good.

The staggering business cost of poor data Quality

Ignoring data quality isn’t a shortcut; it’s an enormous, often hidden, expense. The scale of this cost is difficult to overstate.

According to a landmark article in the Harvard Business Review, bad data costs the U.S. economy up to $3.1 trillion per year. This isn’t just an abstract number; it materializes in tangible, everyday inefficiencies. The same study reveals that knowledge workers can waste up to 50% of their time hunting for data, confirming sources, and correcting errors that should have been prevented.

This isn’t just an operational headache; it’s a strategic risk that has captured the attention of the boardroom. Top consulting firms consistently find that a majority of CEOs are deeply concerned about the integrity of the data informing their most critical decisions. When leaders can’t trust their data, they can’t confidently lead.

This erosion of trust manifests in several critical business risks:

• Flawed business strategy: decisions based on an inaccurate understanding of the market, customer behavior, or internal performance lead to misallocated resources and failed initiatives.
• Damaged customer trust: sending a customer an offer for a product they already own, addressing them by the wrong name, or failing to acknowledge their history with your brand creates a poor experience and damages loyalty.
• Operational inefficiency: from supply chain logistics to financial reporting, bad data creates friction, requires manual rework, and grinds processes to a halt.
• Growing compliance risk: with regulations like GDPR and CCPA, maintaining inaccurate, outdated, or duplicate personal data is no longer just sloppy – it’s a significant legal and financial liability.

In today’s economy, as Gartner asserts, high-quality data is a crucial competitive advantage. To avoid these costs and build that advantage, you must first diagnose the specific issues that create them.

Let’s dive into the 10 most common culprits.

A deep dive into the 10 most common data quality issues

To effectively treat a problem, you must first be able to name it. While the symptoms of poor data quality – like a failed marketing campaign – are easy to spot, the underlying causes are often more complex.

The following are the most common and damaging data quality issues that organizations face every day.

For each one, we will define the problem, provide a real-life example, detail its consequences, and outline a clear solution.

1. Inaccurate data

The accuracy problem: when your data is factually wrong

• Definition: This issue, a direct failure of data accuracy, occurs when data is syntactically correct but does not reflect real-world truth. The goal is to ensure every user has access to correct data they can trust for decision-making. It’s a subtle but dangerous problem because systems will treat the data as if it’s correct.
• Real-life examples: Your CRM lists a key client’s headquarters in New York, but they relocated to Austin six months ago. A sales executive, relying on this data, plans an entire trip around an outdated fact.
• Business impact & consequences:
  • Flawed strategy: Leadership makes critical decisions – from sales territory planning to market analysis – based on a fundamentally incorrect understanding of their customers and the market.
  • Wasted resources: Time, money, and effort are spent on initiatives aimed at the wrong targets, like the sales trip planned for the wrong city.
  • Reputational damage: Continuously using outdated or incorrect information in communications with a client makes your organization appear disorganized and inattentive.
• Solution & how to fix it:
  • Implement data validation rules at the point of entry to cross-reference information where possible.
  • Utilize third-party data enrichment services to periodically verify and update customer and business information.
  • Establish a clear data governance framework using a tool like the Collibra Data Catalog to trace data lineage, identify the original source of inaccuracies, and assign ownership for correction.

2. Duplicate data

The redundancy problem: the hidden costs of duplicate data

• Definition: A single real-world entity – such as a customer, product, or partner – is represented by multiple records within or across your data systems.
• Real-life examples A customer named “Robert Smith” downloads a whitepaper and is entered into your marketing system. A few months later, a salesperson manually creates a new opportunity for “Bob Smith” at the same company in the CRM. The systems now see two different people, not one.
• Business impact & consequences:
  • Skewed analytics: Your reports show more customers than you actually have, leading to incorrect calculations for customer acquisition cost (CAC), lifetime value (LTV), and market penetration.
  • Wasted marketing spend: Your marketing budget is diluted by sending multiple mailers, running redundant ad campaigns, and paying for extra contacts in your automation platforms.
  • Poor customer experience: The same customer receives conflicting messages from different teams, leading to frustration and a fragmented view of your brand.
• Solution & how to fix it:
  • Employ fuzzy matching and other advanced algorithms to identify non-obvious duplicates (e.g., “Rob” vs. “Robert”).
  • Establish a Master Data Management (MDM) strategy to create and maintain a single, authoritative “golden record” for each entity.
  • Use a data catalog to discover and profile data across all systems, making it possible to identify where duplicate records are being created and by which processes.

3. Incomplete data

The void problem: making decisions with incomplete data

• Definition: This issue of incomplete data, often referred to as missing data, occurs when records are missing essential information in one or more critical fields, rendering them less useful or even unusable.”
• Real-life examples: A B2B company has 20,000 lead records, but only 60% have a job title, 40% have a phone number, and 20% have an industry classification.
• Business impact & consequences:
  • Ineffective segmentation: The marketing team cannot personalize campaigns or segment their audience effectively without complete demographic or firmographic data.
  • Paralyzed operations: The sales team is unable to act on a “hot lead” because the contact information is missing.
  • Biased reporting: Any analysis performed only on the records with complete data is likely biased and not representative of the entire dataset, leading to flawed conclusions.
• Solution & how to fix it:
  • Make key fields mandatory in data capture forms and systems where appropriate.
  • Regularly run data profiling jobs to identify the most common and impactful areas of incomplete data.
  • Invest in data enrichment services to append missing information to your existing records.

4. Inconsistent data

The consistency problem: when your data contradicts itself

• Definition: The same real-world entity is described differently in separate data systems, leading to direct contradictions.
• Real-life examples: A customer’s record in your CRM indicates they are in the “Technology” industry. However, in your billing system, the same customer is classified under the “Financial Services” industry. Both cannot be correct.
• Business impact & consequences:
  • Complete loss of trust: When faced with two conflicting “facts,” users lose all faith in the data. They revert to manual confirmation and tribal knowledge, abandoning the very systems meant to create efficiency.
  • Operational gridlock: It becomes impossible to automate decisions. Should the customer receive marketing relevant to tech or finance? Which department’s report is correct?
  • Failed integration: Attempts to sync or integrate systems fail when the data is fundamentally inconsistent, halting critical IT projects.
• Solution & how to fix it:
  • Develop a centralized business glossary using a data governance platform like Collibra. This creates an authoritative, shared definition for every critical data element (like “Industry” or “Customer Status”).
  • Implement data standardization processes that reconcile data as it moves between systems.
  • Establish clear data governance policies that dictate which system is the “source of truth” for specific data elements.

5. Outdated data (stale data)

The timeliness problem: when good data goes bad

• Definition: Data that was once accurate but has lost its relevance and correctness because it has not been updated over time.
• Real-life examples: Your contact database lists a key prospect’s title as “Senior Analyst.” In reality, she was promoted to “Director of Analytics” nine months ago and is now the key decision-maker you should have been targeting.
• Business impact & consequences:
  • Missed opportunities: Your sales and marketing teams are operating with an obsolete map of the market, failing to engage the right people at the right time.
  • Inefficient resource allocation: Teams waste valuable time and effort trying to contact people who have changed roles or left the company.
  • Compliance risk: Holding onto personal data long after it has ceased to be relevant for its original purpose can violate the data minimization principles of privacy regulations like GDPR.
• Solution & how to fix it:
  • Implement data decay workflows that automatically flag records that have not been updated or verified within a specific timeframe (e.g., 6 months).
  • Subscribe to data refresh services that periodically update your records with the latest information.
  • Establish clear data retention policies as part of your governance framework to define how long data should be kept and when it should be archived or deleted.

6. Invalid data (formatting issues)

The format problem: when data doesn’t follow the rules

• Definition: Data that is not stored in the required format, does not adhere to defined syntax, or fails to match its intended data types (e.g., a text string in a number field). This is a fundamental structural issue that can cause widespread technical failures.
• Real-life examples: A “start date” field in your employee database requires a YYYY-MM-DD format, but due to a faulty import, it contains entries like “Jan. 5, 2023,” “2023/01/05,” and “Not Applicable.”
• Business impact & consequences:
  • Application & integration errors: Automated processes and software applications expecting a specific format will fail, causing system crashes and halting data synchronization between tools.
  • Inaccurate sorting and filtering: It becomes impossible to correctly sort records by date or filter for a specific time period, making historical analysis unreliable.
  • Blocked analytics: Data cannot be properly loaded into business intelligence tools or data warehouses, creating a major roadblock for the analytics team.
• Solution & how to fix it:
  • Enforce strict formatting rules at the point of data entry using input masks and validation checks in forms.
  • Run regular data profiling scripts to detect and report on format anomalies across your databases.
  • Use a data governance platform like Collibra to centrally document, define, and enforce the authoritative format standards for all critical data elements across the organization.

7. Ambiguous data

The ambiguity problem: when data has multiple meanings

• Definition: Data that is technically valid but lacks the necessary context (metadata) to be understood correctly. Without this context, different people can interpret the same data in vastly different ways.
• Real-life examples: A financial report spreadsheet has a column labeled “Sales,” but it doesn’t specify the currency (USD, EUR, GBP?). A logistics database has a “Weight” column with no unit specified (Lbs or Kg?).
• Business impact & consequences:
  • Catastrophic miscalculations: A simple currency or unit mistake can lead to massive errors in financial projections, revenue reporting, and scientific analysis.
  • Flawed strategic conclusions: Leadership may believe revenue is skyrocketing or shipping costs are low, when in fact they are simply misinterpreting the data.
  • Erosion of trust: When an analyst discovers that a key metric is ambiguous, they immediately (and rightly) question the validity of every other report generated from that system.
• Solution & how to fix it:
  • Never assume context is understood. Always include descriptive metadata alongside data.
  • Create and enforce a company-wide business glossary that provides clear, unambiguous definitions for all key business terms and metrics.
  • Leverage a data catalog to formally link business context (like definitions, units, and currency) directly to the technical data assets in your databases and reports.

8. Data silos (hidden data)

The silo problem: when you can’t get a complete picture

• Definition: Valuable, high-quality data exists within the organization but is trapped within a specific department, application, or system, making it inaccessible to others who could benefit from it.
• Real-life examples: Your marketing team has rich data on how customers interact with your website and campaigns, stored in their marketing automation platform. Your customer service team has detailed data on product issues and customer complaints in their support ticketing system. Neither team has access to the other’s data, so neither has a true 360-degree view of the customer’s journey and sentiment.
• Business impact & consequences:
  • Missed opportunities: The sales team is unaware of a customer’s recent support issues and makes an ill-timed pitch, or marketing is unaware of a cross-sell opportunity identified by the service team.
  • Inconsistent customer experience: A customer has to repeat their entire history to every new person they speak with because the institutional knowledge is not shared.
  • Redundant work & cost: Different departments spend money and time to acquire or generate the same data that already exists elsewhere in the organization.
• Solution & how to fix it:
  • Promote a culture of data sharing, driven by executive leadership that emphasizes cross-departmental collaboration.
  • Implement a modern data integration or data virtualization strategy to connect disparate systems.
  • Deploy a data catalog like Collibra to create an inventory of all organizational data assets. This makes hidden data discoverable, allowing users to see what data exists, where it lives, what it means, and how to request access.

9. Orphaned data

The orphan problem: data without a home

• Definition: A record in one data table that is supposed to be linked to a record in another table, but the “parent” record has been deleted. This breaks the relationship between the data.
• Real-life examples: Your database has a table of “Sales Orders” where an order record is linked to “CustomerID: 789.” However, due to improper data cleanup, the record for customer 789 has been deleted from the main “Customers” table. The sales order is now an orphan.
• Business impact & consequences:
  • Broken referential integrity: This is a fundamental violation of database principles, leading to unpredictable application behavior and errors.
  • Incomplete reporting: It becomes impossible to run a report on “Sales by Customer” because the orphaned order cannot be linked to a customer name or region.
  • Compromised analysis: The orphaned record is essentially useless for any analysis that requires its full context, skewing historical trends.
• Solution & how to fix it:
  • Enforce referential integrity constraints at the database level to prevent the deletion of parent records when child records exist.
  • Establish proper data archiving and deletion protocols within your data governance policy instead of allowing ad-hoc deletions.
  • Run regular data audits to identify and remediate orphaned records, a process whose rules and results should be managed within your governance platform.

10. Unstructured data

The chaos problem: taming unstructured data

• Definition: Data that does not have a predefined data model or is not organized in a pre-defined manner. It accounts for an estimated 80% of all enterprise data.
• Real-life examples: The immense volume of valuable information contained in customer support emails, call center transcripts, social media comments, online reviews, legal contracts in PDFs, and doctor’s notes.
• Business impact & consequences:
  • Massive untapped value: The authentic “voice of the customer” – their complaints, desires, and opinions – is locked away and unused, leaving a huge gap in business intelligence.
  • Increased risk & inefficiency: It is nearly impossible to know if sensitive information is present in these documents or to find specific information quickly (e.g., locating all contracts with a specific clause).
  • Inability to govern: Standard data quality rules cannot be easily applied to free text, making governance a significant challenge.
• Solution & how to fix it:
  • Invest in tools that use Natural Language Processing (NLP) and text analytics to extract structured entities, topics, and sentiment from text.
  • Leverage a modern data catalog that can scan and index unstructured data sources.
  • Use the catalog to apply classification tags (e.g., “Contains PII,” “Legal Contract”) to unstructured documents, making them discoverable, governable, and linkable to your structured data.

Feeling overwhelmed? You’re not alone. Manually finding and fixing these 10 issues across millions of data points and dozens of systems is impossible.

Learn how Murdio uses the Collibra Data Catalog to help businesses automatically discover, profile, and diagnose the full spectrum of data quality issues at their source.

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The impact: why every data quality issue erodes data integrity

As we’ve just seen, each of the ten issues – from a simple typo in a single record to a complex, organization-wide data silo – creates its own unique drag on resources and efficiency.

But their true danger lies in their cumulative effect. These are not isolated problems; they are cracks in your organization’s data foundation.

Individually, each error might cause a localized problem. Collectively, they lead to a much more dangerous, systemic outcome: the complete erosion of data integrity.

Data integrity is the measure of the overall accuracy, completeness, consistency, and trustworthiness of your data throughout its entire lifecycle.

It’s the confidence you are using reliable data to run your business is a true and reliable reflection of reality.

When duplicate records skew your sales reports, when inconsistent data brings operations to a halt, and when outdated information leads to missed opportunities, you don’t just have a data problem; you have an integrity problem.

Without integrity, trust evaporates, and without trust, data-driven decision-making is impossible.

The goal we’re all striving for: defining high-quality data

To rebuild that trust, we need a clear destination. The ultimate goal of resolving these issues is to achieve a state of high-quality data, which is defined as data that is “fit for its intended purpose.”

This means it must be able to reliably answer key business questions.

Think of the formal dimensions of data quality as a checklist for achieving this state of trustworthiness:

Achieving this state of high-quality data – where every dimension is actively managed and trusted – requires moving beyond ad-hoc, reactive cleanups to implementing a systematic framework.

A proactive framework to fix data quality issues

Firefighting individual data errors is an exhausting, inefficient, and never-ending battle. You fix one problem, and two more appear elsewhere.

The only way to win is to change the game entirely – from being reactive to becoming proactive. This shift is accomplished by implementing a Data Quality Framework.

A formal data quality management framework combines people, processes, and technology to manage data quality across your entire organization.

Instead of treating data quality as a one-time project, it embeds it into the fabric of your daily operations.

At a high level, this framework is built on three core pillars:

1. People & governance

Establishing clear ownership and accountability for data. This involves defining roles like Data Stewards, creating a data governance council, and fostering a culture where every employee understands their role in maintaining data quality.

2.Standardized processes

Creating repeatable processes for discovering issues by running automated data quality checks that track key data quality metrics (like error rates or null counts), remediating errors, and certifying data sources.

This includes standardizing definitions in a business glossary and setting up clear workflows for when an issue is found.

3. Enabling technology

Leveraging modern data quality tools to automate the work. These solutions are often applied throughout your data pipelines to validate, clean, and monitor data as it moves from source to analytics.

A unified data governance and data quality solution, like the Collibra Data Intelligence Platform, is the engine that powers the framework, enabling automated data discovery, profiling, continuous data quality monitoring, and collaboration.

These platforms give you the power to actively monitor data health rather than waiting for errors to be reported by users.

Adopting such a framework allows you to prevent errors at the source, drastically reducing the time and money spent on cleanup and building a sustainable foundation of trusted data.

Ready to build your own framework?

Implementing a robust data quality framework is a critical project with many moving parts. To give this topic the deep dive it deserves, we’ve created a comprehensive, step-by-step guide.

Continue Reading: [How to build a data quality framework: a step-by-step guide]

From data chaos to data confidence: your journey starts now

We began this guide by identifying the ten most common data quality issues that silently drain resources, erode trust, and hinder growth.

From inaccurate records and costly duplicates to the chaos of unstructured data, we have seen how these individual problems create a systemic drag on your entire organization.

The path forward is clear: moving beyond a reactive, fire-fighting approach to data errors is not just an option, it is a business necessity.

As we’ve discussed, the only sustainable solution is to adopt a proactive framework that combines governance, standardized processes, and enabling technology to build quality into the very fabric of your operations.

This is how you transform data from a source of frustration into your most reliable strategic asset.

Understanding the problems and knowing the solution are the first critical steps. The next is taking action.

Ready to move from theory to transformation?

Every organization’s data landscape is unique. A generic plan is not enough. To build a truly effective data quality and governance strategy, you need a partner with deep expertise.

Schedule a complimentary consultation with a Murdio Collibra expert today. We will help you assess your specific data quality challenges, identify the highest-impact areas for improvement, and map out a clear, actionable path to achieving lasting data integrity.