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8 Data Quality Problems in 2025 & 8 Ways to Fix Them

Updated June 12th, 2025

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Quick Answer: What are data quality problems? #


Data quality problems are the issues and discrepancies within datasets that hinder their accuracy, completeness, consistency, and reliability. So, they can disrupt operations, compromise decision-making, and erode customer trust.

The eight most common data quality problems are:

  1. Incomplete data
  2. Inaccurate data
  3. Misclassified or mislabeled data
  4. Duplicate data
  5. Inconsistent data
  6. Outdated data
  7. Data integrity issues across systems
  8. Data security and privacy gaps

Up next, we will explore these common data quality problems in detail, how to fix them, and factors affecting the data quality.

Table of contents #

  1. Data quality problems explained
  2. What are the eight most common data quality problems?
  3. What are the key factors affecting data quality?
  4. How can you fix data quality problems? 8 ways to get around data quality problems
  5. Data quality problems: Final thoughts
  6. Data quality problems: Frequently asked questions (FAQs)
  7. Data quality problems: Related reads

Data quality problems explained #

Data quality problems refer to flaws in the structure, content, or context of data that prevent it from serving its intended purpose. Whether you’re trying to segment customers, forecast revenue, or comply with regulations, poor data quality slows you down and leads you in the wrong direction.

Data quality problems come at a cost. For instance, Gartner’s Data Quality Market Survey showed that the average annual financial cost of poor data is to the tune of $15M.

Often, these issues result from:

  • Missing or outdated metadata that makes it hard to validate or trace data
  • A lack of governance over how data is created, modified, and consumed
  • Siloed systems without integrated quality monitoring or collaboration
  • Manual error-prone processes without automation or accountability

Addressing these challenges calls for a layered, metadata-driven approach, which includes:

  • Clear data governance policies
  • Using sophisticated data cleaning tools
  • Continuous quality audits and monitoring
  • Context-aware remediation workflows
  • Fostering a culture of data awareness and responsibility within the organization

These steps help ensure that the data is as accurate, complete, consistent, reliable, relevant, timely, usable, and has integrity as possible, thereby enhancing its value and effectiveness for its intended use. And, now let’s look at some typical data quality problems.

What are the eight most common data quality problems? #

Data quality issues often stem from fragmented data ecosystems, unclear ownership, and lack of context. Here are the eight most common problems organizations face:

The seven common data quality problems are:

  1. Incomplete data
  2. Inaccurate data
  3. Misclassified / mislabeled date
  4. Duplicate data
  5. Inconsistent data
  6. Outdated data
  7. Data integrity issues
  8. Data security and privacy concerns

Let’s understand each problem in detail.

1. Incomplete data #


Incomplete data refers to the presence of missing or incomplete information within a dataset. This can occur for various reasons, such as data entry errors, system limitations, or data sources not providing certain required details.

Incomplete data can lead to broken workflows, faulty analysis, and delays in operational processes.

Addressing this issue involves data validation processes, data collection improvements, and ensuring that all necessary information, especially the underlying metadata, is consistently and accurately recorded.

2. Inaccurate data #


Inaccurate data encompasses errors, discrepancies, or inconsistencies within a dataset. These inaccuracies can originate from various sources, including human errors during data entry, system malfunctions, or issues with data integration.

Inaccurate data misleads analytics, affects customer communication, and can result in regulatory penalties.

Resolving this issue often requires rigorous data validation and cleansing procedures, data quality monitoring, and implementing data entry validation rules to prevent errors at the source.

Metadata again has an important role to play. Metadata documents data sources, formats, and verification rules, providing transparency into when and how data was last updated.

3. Misclassified or mislabeled data #


Data is misclassified or mislabeled when it’s tagged with incorrect definitions or business terms, or inconsistent category values. This leads to incorrect KPIs, broken dashboards, and flawed machine learning models.

Resolving this issue requires establishing semantic context through glossaries, tags, and lineage to ensure shared understanding across the organization.

4. Duplicate data #


Duplicate data is when you have multiple entries for the same entity, often across systems (e.g., two records for the same customer).

Duplicate data can lead to redundancy, increased storage costs, and misinterpretation of information if not properly identified and managed.

De-duplication processes, data cleansing, and the implementation of unique identifiers can help address this issue. The underlying metadata, if carefully collected and documented, helps by identifying matching records and duplicate-generating transformations.

5. Inconsistent data #


Inconsistent data is when there are conflicting values for the same field across systems (e.g., different shipping addresses in CRM vs. ERP). It often arises due to data entry variations, evolving data sources, or a lack of standardized data governance practices.

Inconsistent data erodes trust, causes decision paralysis, and leads to audit issues.

To mitigate this issue, organizations must establish clear data standards, enforce data quality guidelines, and use data transformation and cleansing techniques to ensure consistency.

6. Outdated data #


Outdated data consists of information that is no longer current or relevant. This can occur over time as data ages and becomes obsolete. Decisions based on outdated data can lead to lost revenue or compliance gaps.

To address this issue, organizations should implement data update and refresh procedures, data aging policies, and regular data maintenance routines to ensure that data remains current and relevant.

Bi-directional tag syncing is especially useful in such situations, as it ensures that metadata across your data estate is current, consistent, and updated.

7. Data integrity issues #


Data integrity issues refer to broken relationships between data entities, such as missing foreign keys, orphan records, or type mismatches. This breaks joins, produces misleading aggregations, and leads to downstream pipeline errors.

Data integrity problems can harm data quality and trustworthiness, and they often require strong data validation, constraints, and access controls to maintain the integrity of data.

Additionally, ingesting and managing the right metadata can help by describing schema relationships, data types, constraints, and interdependencies.

8. Data security and privacy concerns #


Data security and privacy concerns involve issues related to the protection of data against unauthorized access, breaches, or improper handling. Unprotected sensitive data and unclear access policies often lead to the risk of fines, breaches, and reputational damage.

Addressing data security and privacy issues involves implementing robust security measures, access controls, encryption, and compliance with privacy regulations to safeguard data from unauthorized access and maintain data quality and trustworthiness.

Metadata plays a crucial role here by enabling the proper classification of PII/PHI, tracking granular access control policies, and showing where (and how) sensitive data flows.

What are the key factors affecting data quality? #

Before we fix data quality issues, it’s important to understand what causes them. Poor data quality often stems from a mix of human, technical, and organizational issues – many of which are invisible without metadata context.

Here are the most common factors affecting data quality:

  1. Lack of metadata context: Without metadata, data assets are just tables and fields — no definitions, no lineage, no ownership. This makes it hard to assess quality, trace errors, or apply consistent standards.
  2. Inconsistent data standards and definitions: When teams define key terms differently — like what counts as a customer or when a deal is “closed” — data becomes fragmented and untrustworthy across systems.
  3. Poor data governance practices: Without data policies, roles, and escalation paths, no one is accountable for maintaining data quality — and inconsistencies often go unresolved.
  4. Data integration and migration errors: When moving or merging data across systems, schema mismatches, format differences, and loss of metadata can all degrade quality.
  5. Unreliable or unvetted data sources: Using third-party data or scraping tools without verifying source accuracy introduces noise into your systems — from outdated entries to completely fabricated data.
  6. Human error during data entry or handling: Manual entry mistakes — typos, skipped fields, misclassifications — are one of the oldest and most persistent sources of bad data.
  7. Data decay and aging: Even the best-quality data loses value over time. Contact info, prices, inventory status — all decay, becoming misleading or obsolete.
  8. Siloed systems and fragmented access: When teams don’t have shared access or context, they often replicate efforts, misinterpret fields, or miss critical lineage. This leads to inconsistent or duplicative work.
  9. Lack of clear data ownership: Without named stewards, there’s no one to answer quality questions, investigate issues, or enforce policies. Accountability gaps are a major blocker to long-term data quality.

How can you fix data quality problems? 8 ways to get around data quality problems #

Addressing data quality problems involves putting proper systems in place to ensure these issues don’t recur. That’s where metadata, data governance, and automation play crucial roles.

Here are seven strategies to fix and prevent common data quality problems:

  1. Data validation and cleaning
  2. Standardization and consistency
  3. De-duplication and entity resolution
  4. Regular data audits and updates
  5. Automated data quality rules and monitoring
  6. Governance and ownership
  7. Data security and privacy measures
  8. Data quality studio offering a single, trusted view of data health

Let’s look at them in detail:

1. Data validation and cleaning #


Use rule-based and statistical checks to catch and correct errors in structure, format, or logic.

Validation checks can include:

  • Format validation (e.g., ensuring valid email addresses)
  • Range validation (e.g., verifying that a value falls within an expected range)
  • Presence validation (e.g., ensuring required fields are filled)

Data cleansing procedures involve identifying and rectifying errors within the data, such as correcting misspelled names or eliminating inconsistent data formats.

2. Standardization and consistency #


Clear data standards define how data should be structured, formatted, and labeled. Data quality guidelines ensure that data is maintained consistently according to these standards.

Apply consistent formats, codes, and naming conventions across sources and systems. Define a “single source of truth” for shared data.

A metadata-powered control plane can help by cataloging schemas, code sets, and format rules. This makes it easier to align and harmonize disparate data assets.

3. De-duplication and entity resolution #


De-duplication processes involve identifying and eliminating duplicate records within datasets.

You can identify and merge duplicate records using fuzzy matching, rule-based matching, or ML models. This is critical in customer, product, and transaction data.

Plus, having unique identifiers (such as customer IDs) helps prevent the creation of new duplicates by ensuring that each data entry has a distinct identifier.

4. Regular data audits and updates #


Schedule regular data audits to detect stale, incomplete, or incorrect data. Ensure outdated records are flagged and cleaned periodically.

Establish data aging policies to define when data becomes outdated and should be updated or archived. Regular data updates ensure that information remains current and relevant.

5. Automated data quality rules and monitoring #


Define and enforce rules for each data quality dimension (e.g., completeness ≥ 95%, no invalid formats). You can set up alerts and dashboards using a data quality studio like Atlan, which helps by automatically tracking violations and triggering real-time alerts.

6. Governance and ownership #


Assign clear owners to critical data assets. Define roles (like data stewards), escalation paths, and policies to enforce accountability.

You can record ownership, approval workflows, and escalation contacts by embedding governance into day-to-day workflows.

7. Data security and privacy measures #


Data security measures, including encryption and access controls, protect data from unauthorized access or breaches. Encryption ensures that data is scrambled and can only be deciphered with the correct decryption key.

Compliance with privacy regulations (e.g., GDPR or HIPAA) ensures that sensitive data is handled according to legal and ethical standards, reducing the risk of privacy breaches and data security issues.

8. Data quality studio offering a single, trusted view of data health #


Most data-quality tooling stops at pipeline health. A data quality studio like Atlan empowers business and data teams to collaborate in one workspace to keep analytics and AI running on purpose-built, trustworthy data.

With Atlan, you can define, automate, and monitor rules that mirror real-world, business-defined expectations.

Such a studio also serves as a unified control plane, integrating with upstream data quality tools (Anomalo, Soda, Monte Carlo), and combining discovery, governance, and quality in a single control plane for one trusted view of data health.

Moreover, Atlan’s Reporting Center provides a single dashboard to track coverage, failures, and business impact in one glance.

Data quality problems: Final thoughts #

In a nutshell, incomplete data, inaccurate data, duplicate data, inconsistent data, outdated data, mislabeled data, data integrity issues, data security and privacy concerns are some of the typical data quality problems.

They are pervasive and can significantly hinder an organization’s operations and decision-making. Recognizing and addressing these issues is crucial for maintaining data integrity.

By implementing the above best practices and using a data quality studio like Atlan, organizations can secure their data’s reliability and ensure its value in data-driven environments.

Data quality problems: Frequently asked questions (FAQs) #

1. What are data quality problems? #


Data quality problems are issues like missing, inaccurate, inconsistent, or duplicated data that compromise the usability, reliability, and trustworthiness of datasets. They lead to flawed analysis, operational inefficiencies, and compliance risks.

2. Why do data quality problems keep recurring? #


They often recur due to lack of data governance, unclear ownership, inconsistent standards, poor tooling, or system fragmentation. Without root-cause visibility (often provided by metadata) and automation, the same issues reappear across pipelines.

3. What’s the cost of poor data quality? #


According to Gartner, the average annual cost of poor data quality is $15 million. This includes missed opportunities, inefficiencies, reputational damage, and regulatory exposure.

4. How does metadata help solve data quality problems? #


Metadata provides context — such as definitions, lineage, owners, and usage — that helps detect, trace, and resolve data quality issues. Without metadata, it’s hard to understand where bad data comes from or who should fix it.

5. What is the role of data governance in fixing quality problems? #


Governance defines roles, rules, and escalation paths. It ensures accountability for maintaining quality, enforcing standards, and resolving issues before they impact decisions or compliance.

6. Can data quality be fixed with just data cleaning tools? #


Not entirely. Cleaning is reactive. Long-term quality depends on proactive governance, metadata management, standardization, and continuous monitoring — ideally supported by a data quality studio like Atlan.

7. How can Atlan help with data quality problems? #


Atlan acts as a metadata-powered control plane that unifies quality signals, automates alerts, maps issues to owners, and centralizes lineage. It integrates with tools like Great Expectations or Soda to turn metadata into trust workflows.

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