GDPR Accuracy Principle: What Article 5(1)(d) Requires in 2026

---

title: “The Principle of Data Accuracy in the GDPR” author: Thiebaut Devergranne layout: post permalink: data-accuracy-gdpr description: “The GDPR requires organizations to ensure the accuracy of personal data they collect and process. Learn what Article 5 demands.” image: “excel-sheet.jpg” categories:

• Personal data lastmod: “2026-03-25” lang: en niche: gdpr cta_type: tofu focus_keyword: “principle data accuracy”

---

Last updated: March 2026

The GDPR accuracy principle, enshrined in Article 5(1)(d) of the General Data Protection Regulation (GDPR), establishes personal data accuracy as a core obligation for every organization that processes personal data. The provision states verbatim:

“Personal data shall be accurate and, where necessary, kept up to date; every reasonable step must be taken to ensure that personal data that are inaccurate, having regard to the purposes for which they are processed, are erased or rectified without delay.” — EUR-Lex, Regulation (EU) 2016/679

This data accuracy GDPR requirement is one of the key data protection principles and carries significant enforcement weight: non-compliance with general data processing principles, which include the accuracy principle, was cited as the reason for 74 GDPR fines (Scrut.io, 2025). In this article, we explore what Article 5(1)(d) demands in practice, its implications for organizations, and the steps they can take to ensure personal data accuracy throughout the data lifecycle.

This article is for informational purposes only and does not constitute legal advice.

I - Understanding the Principle of Data Accuracy

The principle of data accuracy is a fundamental requirement of the GDPR. It mandates that personal data must be accurate and, where necessary, kept up to date. This principle aims to ensure that the data used by organizations for various purposes is reliable, correct, and reflects the current reality of the individuals concerned. While the term data quality principle is sometimes used as a synonym, the GDPR specifically frames this as an accuracy and currency obligation under GDPR Article 5 accuracy — requiring data to be correct and kept up to date rather than addressing broader dimensions of data quality.

A - The Importance of Data Accuracy

Maintaining accurate personal data is crucial for several reasons:

1. Decision-making: Organizations often rely on personal data to make important decisions, such as assessing creditworthiness, providing services, or offering employment. Inaccurate data can lead to incorrect or unfair decisions that negatively impact individuals. This concern is heightened by the rise of AI: in late 2025, the EDPB published its Guidelines on AI and Data Protection, emphasizing that organizations using AI systems for automated decision-making bear a reinforced obligation under the accuracy principle to verify and correct training data, given the risk that inaccurate inputs produce systematically biased outputs.

2. Individual rights: The GDPR grants individuals certain rights, such as the right to access their personal data and the right to rectification. If the data held by organizations is inaccurate, individuals may be unable to exercise these rights effectively.

3. Data integrity: Accurate data is essential for maintaining the integrity and reliability of data processing activities. Inaccurate data can undermine the effectiveness of data analysis, lead to flawed insights, and compromise the overall quality of data-driven processes.

B - The Scope of Data Accuracy

The principle of data accuracy applies to all personal data collected and processed by organizations. This includes data obtained directly from individuals, as well as data obtained from third parties or publicly available sources.

Organizations must take reasonable steps to ensure the accuracy of personal data at the time of collection and throughout the data lifecycle. This may involve implementing data validation processes, conducting regular data audits, and providing mechanisms for individuals to update or correct their personal data.

It is worth noting that historical records — data that was accurate at the time it was recorded — do not necessarily need to be updated if they are held for historical or archival purposes. The “where necessary, kept up to date” qualifier in Article 5(1)(d) applies contextually, meaning the obligation to update depends on the purposes for which the data is processed.

C - Accuracy of Opinions Under GDPR

The accuracy of opinions recorded as personal data requires particular consideration. The accuracy principle does not require that opinions themselves be “accurate” in an objective sense; rather, it requires that the record accurately reflects the opinion that was actually expressed, by whom it was expressed, and when it was recorded. For example, a performance appraisal, a medical opinion, or a risk assessment recorded about an individual constitutes personal data, but the controller’s obligation is to ensure the record faithfully captures what was stated — not to guarantee the correctness of the underlying judgment.

If a data subject disputes a recorded opinion, the controller is not obliged to delete it. Instead, the controller should note that the opinion is disputed, ensuring the record presents a complete and fair picture. The ICO’s guidance on this point confirms that adding a note of dispute, rather than erasure or alteration, is generally the appropriate response when a data subject challenges the substance of an opinion held about them.

This distinction is important for organizations in sectors such as healthcare, education, finance, and human resources, where subjective assessments form a routine part of personal data processing. Controllers should ensure that staff recording opinions do so clearly, attributing the opinion to its source and dating it, so that the accuracy of the record itself can be maintained and verified over time.

II - Implementing Data Accuracy in Practice

To comply with the principle of data accuracy, organizations should adopt a proactive approach and implement appropriate measures to ensure the accuracy of personal data. So what does the principle of data accuracy mean in practice? It means embedding data accuracy in practice into every stage of the data lifecycle — from collection and data validation to ongoing data verification and correction. According to the IAPP-EY Privacy Governance Report 2026, 68% of European businesses have updated their processing records in the past 12 months, indicating that organizations are actively working toward maintaining accurate data practices (as cited by Searchlab, 2026).

A - Data Collection and Validation

The journey towards data accuracy begins at the point of data collection. Organizations should design their data collection processes to capture accurate and complete information from individuals.

This can be achieved through:

1. Clear instructions: Providing clear and concise instructions to individuals on how to provide accurate data, including specifying the format and any necessary details.

2. Data validation: Implementing data validation mechanisms, such as input validation, to ensure that the data entered by individuals meets the required criteria (e.g., valid email format, date range, etc.).

3. Data verification: Verifying the accuracy of collected data through cross-referencing with reliable sources or requesting additional documentation from individuals when necessary.

B - Data Maintenance and Updates

Ensuring data accuracy is an ongoing process that requires regular maintenance and updates. Organizations should establish procedures to keep personal data up to date and reflect any changes in individuals’ circumstances. Regular reviews also support storage limitation by identifying data that is no longer necessary.

This can involve:

1. Periodic data reviews: Conducting regular reviews of personal data to identify and correct any inaccuracies or outdated information.

2. Self-service portals: Providing individuals with self-service portals or mechanisms to review and update their personal data directly.

3. Data integration: Integrating data from various sources and systems to maintain a consistent and up-to-date view of individuals’ data across the organization.

C - Data Quality Monitoring and Audits

Organizations should implement data quality monitoring and auditing processes to proactively identify and address data accuracy issues.

This can include:

1. Data profiling: Analyzing data to identify patterns, anomalies, or inconsistencies that may indicate data accuracy problems.

2. Data cleansing: Implementing data cleansing techniques to identify and correct inaccurate, incomplete, or inconsistent data.

3. Data quality metrics: Establishing data quality metrics and key performance indicators (KPIs) to measure and monitor the accuracy of personal data over time.

D - Handling Data Inaccuracies

Despite best efforts, data inaccuracies may still occur. Organizations must have processes in place to handle and rectify data inaccuracies when they are identified or reported by individuals.

This involves:

1. Rectification procedures: Establishing clear procedures for individuals to request the rectification of inaccurate personal data and ensuring prompt action is taken to correct the data.

2. Notification of rectification: Informing individuals about the rectification of their personal data and any third parties to whom the inaccurate data may have been disclosed.

3. Documentation: Maintaining records of data rectification requests and actions taken to demonstrate compliance with the principle of data accuracy.

III - Challenges and Considerations

Implementing the principle of data accuracy presents certain challenges and considerations for organizations.

A - Legacy Data and Systems

Many organizations have legacy systems and databases that contain personal data collected before the GDPR came into effect. Ensuring the accuracy of this historical data can be challenging, as it may have been collected under different standards or may lack the necessary documentation.

Organizations should prioritize the review and remediation of legacy data to identify and address any accuracy issues. This may involve data cleansing, data enrichment, or even the deletion of data that cannot be verified as accurate.

B - Third-Party Data

Organizations often rely on personal data obtained from third parties, such as data brokers or public sources. Ensuring the accuracy of this data can be more complex, as the organization may have limited control over the data collection and maintenance processes of the third party.

In such cases, organizations should conduct due diligence on the third-party data providers, establish contractual obligations for data accuracy, and implement additional verification processes to validate the accuracy of the data received.

C - Balancing Accuracy and Data Minimization

The principle of data accuracy should be balanced with the principle of data minimization, which requires organizations to collect and process only the personal data that is necessary for the specified purposes.

Organizations should carefully consider the data fields and attributes they collect, ensuring that they are relevant and necessary for the intended purposes while still maintaining data accuracy. Collecting excessive or irrelevant data can increase the risk of data inaccuracies and complicate data maintenance efforts.

FAQ

What is the GDPR accuracy principle?

Article 5(1)(d) requires personal data to be accurate and, where necessary, kept up to date. Inaccurate data must be erased or rectified without delay. This applies to all personal data you hold, not just data you actively use.

What does Article 5(1)(d) require?

Article 5(1)(d) requires personal data to be accurate and, where necessary, kept up to date. Inaccurate data must be erased or rectified without delay. This applies to all personal data you hold, not just data you actively use.

How often should organisations verify personal data accuracy?

There is no fixed frequency. It depends on how quickly data becomes stale. Employment records may need annual review. Customer contact data should be verified at key interactions. Medical data requires continuous accuracy checks.

How can organisations ensure data accuracy?

Article 16 GDPR gives individuals the right to rectification to have inaccurate personal data corrected without undue delay. Organisations must respond within one month and propagate corrections to any processors or third parties who received the data.

What happens if personal data is inaccurate?

Fines up to €20 million or 4% of global annual turnover, plus reputational damage. The ICO has fined organisations for maintaining outdated blacklists and incorrect credit data, causing harm to individuals.

Does the GDPR accuracy principle apply to opinions?

Opinions recorded as personal data (e.g., a manager’s performance assessment, a doctor’s clinical opinion) are subject to the accuracy principle, but the obligation is that the record accurately reflects what opinion was held, by whom, and when — not that the opinion itself must be objectively “correct.” If a data subject disputes a recorded opinion, the controller should note the contest rather than delete it. The Article 16 right to rectification provides the mechanism for adding a note of dispute.

Does the accuracy principle apply to opinions recorded about individuals under the GDPR?

The accuracy of opinions GDPR rules are nuanced. The accuracy principle does not require recorded opinions to be objectively correct. Instead, it requires that the record faithfully reflects what opinion was expressed, by whom, and when. If a data subject disputes an opinion, the controller should add a note of dispute rather than delete the record. Where a data subject believes the opinion itself is wrong, they may also exercise their right to rectification to request that the record be supplemented with their own statement. The ICO confirms that maintaining an accurate account of the opinion as given — rather than guaranteeing the opinion’s correctness — satisfies the obligation under Article 5(1)(d).

How does data accuracy interact with AI and automated decision-making under the GDPR?

Organisations using AI systems for profiling or automated decisions must pay particular attention to the accuracy of training and input data. The EDPB’s 2025 Guidelines on AI and Data Protection stresses that inaccurate source data can produce systematically biased outputs, reinforcing the obligation under Article 5(1)(d) to verify, correct, or erase inaccurate data before and during automated processing.

Does the accuracy principle require opinions recorded about individuals to be objectively correct?

No. The accuracy principle does not require that opinions themselves be objectively correct. It requires that the record accurately reflects what opinion was expressed, by whom, and when. If a data subject disputes a recorded opinion, the controller should note the dispute rather than delete the opinion. The ICO’s guidance confirms this approach: the record must be an accurate account of the opinion given, not a guarantee that the opinion itself is right.

What does the principle of data accuracy mean in practice for organisations?

In practice, it means embedding data validation and data verification processes at every stage of the data lifecycle. Organisations should validate data at the point of collection, conduct periodic reviews, provide self-service update mechanisms, and maintain documented rectification procedures. The EDPB’s guidance further reinforces that accuracy obligations extend to AI training data and automated processing inputs.

Conclusion

The gdpr accuracy principle under Article 5(1)(d) goes beyond simply storing correct data — it demands verifiable, documented processes for validation, maintenance, and rectification throughout the data lifecycle. As this article has shown, the obligation extends to nuanced areas: recorded opinions need not be objectively correct, but must faithfully reflect what was expressed, by whom, and when. With 74 enforcement actions already citing principle-level violations, regulators have made clear that accuracy is not a theoretical requirement but an actively audited one.

Organizations must embed accuracy controls at every processing stage — from collection and third-party due diligence to legacy data remediation — and maintain auditable records demonstrating compliance with the GDPR. Conducting a thorough GDPR audit remains essential for identifying gaps.

As the EDPB’s evolving guidance on AI and data protection takes shape through 2026, the accuracy obligation will only intensify — particularly where automated systems amplify the downstream consequences of inaccurate input data.