The GDPR Accuracy Principle explained

What is the GDPR Accuracy Principle?

The GDPR Accuracy Principle requires organisations to take reasonable steps to ensure that all personal data they collect and use is accurate, complete and kept up to date.

This principle protects individuals from the risks associated with incorrect or outdated information, such as unfair decisions, financial loss or reputational damage

It also supports lawful, fair and transparent data processing by ensuring that organisations base their actions, assessments and automated decisions on reliable information across all systems, databases and business processes.

The legal definition of accuracy                                 

Under UK GDPR, accuracy means personal data must be correct and, where necessary, kept current. Organisations must take reasonable steps to erase or rectify inaccurate data without delay to prevent misleading outcomes or unlawful processing.

Key points:

• Accuracy is a legal requirement under the UK GDPR and must be actively upheld by all data controllers and processors

  
  

• Applies to all personal data, regardless of format, system, or whether it is held digitally or on paper

  
  

• Includes keeping data up to date where changes could affect how the information is used or interpreted

  
  

• Errors must be corrected promptly to prevent harm, unfair decisions, or unlawful processing

Accuracy is assessed based on what is reasonable in the circumstances, taking into account the purpose of processing, the reliability of the data source, and how frequently information changes, so organisations can apply proportionate, risk-based controls.

Why accuracy matters for individuals’ rights

Accurate data ensures people can exercise their rights fairly, including access, rectification and objection.

Inaccuracies may lead to incorrect decisions, reputational damage or financial loss, undermining trust and breaching data protection obligations.

Key points:

• Supports fair treatment by ensuring individuals are assessed and treated based on correct and relevant personal information

  
  

• Prevents harmful decisions that could result from errors, outdated records, or misleading data being used in important determinations

  
  

• Protects reputations by reducing the risk of false or damaging information being recorded or shared about an individual

  
  

• Enables effective rights by allowing people to meaningfully access, correct, and challenge the data held about them

When personal data accurately reflects reality, individuals can trust organisations to make lawful, fair and proportionate decisions. This strengthens confidence in digital services, public bodies and private organisations, and supports transparency, accountability and respect for data protection rights.

How accuracy supports fairness and transparency

Accuracy underpins fairness by ensuring decisions are based on truthful information, and transparency by allowing individuals to understand how their data is used.

Errors distort outcomes and obscure accountability across automated and manual processing activities.

Key points:

• Improves decision quality by ensuring actions and outcomes are based on reliable, complete and relevant personal information

  
  

• Enhances trust between organisations and individuals by showing data is handled carefully and responsibly

  
  

• Supports explainability by making it easier to justify decisions and show how personal data was used

  
  

• Reduces disputes by minimising errors that lead to complaints, challenges, or legal disagreements

Maintaining accurate records enables organisations to provide clear and consistent explanations for their decisions, demonstrate regulatory compliance, and reduce the likelihood of complaints or disputes, reinforcing ethical data handling and strengthening confidence among individuals, regulators and stakeholders.

What Counts as Accurate Personal Data?

Accurate personal data is information that correctly represents an individual’s circumstances, identity and activities. It must be complete enough for its purpose and not misleading, ensuring organisations can rely on it for lawful, effective processing.

In practice, accuracy has both factual and contextual aspects

Factual accuracy concerns objective correctness, while contextual accuracy reflects whether data is appropriate for its use. A fact can be true yet misleading if used outside its original context, creating unfair or unlawful outcomes.

Key points:

• Facts must be correct so that personal data reflects objective reality and can be relied upon for lawful decision-making

  
  

• Context must be appropriate to ensure information is not used in a way that creates a misleading or unfair impression

  
  

• Purpose matters because data that is accurate for one use may be inappropriate for another

  
  

• Misuse creates inaccuracy when information is applied outside its original or intended context

Organisations should assess both factual and contextual accuracy to avoid harmful or misleading interpretations, especially in high-impact activities such as profiling, credit assessments and eligibility decisions, where errors or misuse of data can significantly affect individuals.

Keeping data up to date

Data must be reviewed and updated when circumstances change. What was accurate yesterday may be wrong today, especially for contact details, employment status or financial information, requiring ongoing maintenance to remain compliant.

Key points:

• Regular updates are required to ensure personal data continues to reflect an individual’s current circumstances

  
  

• Changes affect accuracy because even small updates, such as contact details or employment status, can significantly alter how data should be used

  
  

• High-risk data needs frequent review where decisions have legal, financial or similarly significant effects on individuals

  
  

• Automation can help by triggering reminders, synchronising records and identifying inconsistencies

Keeping personal data up to date reduces the risk of errors, improves service quality and supports lawful decision-making, while also helping organisations avoid regulatory breaches that arise when outdated or obsolete records are relied upon.

Avoiding misleading or incomplete records

Incomplete or selectively recorded data can mislead, even if individual elements are true. Records should be sufficiently detailed to avoid false impressions, particularly where decisions affect rights, benefits or legal obligations.

Key points:

• Completeness matters because missing information can distort how an individual is understood or assessed

  
  

• Partial data misleads by creating an inaccurate or unfair picture, even when individual facts are technically correct

  
  

• High-impact records need care where decisions affect rights, finances or access to important services

  
  

• Quality controls are essential to detect errors, gaps and inconsistencies across records

Ensuring records tell the full story supports fair, balanced and lawful outcomes, while also helping organisations justify their decisions, respond to challenges and demonstrate compliance with data protection and accountability requirements.

When Personal Data Becomes Inaccurate

Personal data becomes inaccurate when it no longer reflects reality, is entered incorrectly, or is used for an inappropriate purpose. This can happen gradually or suddenly, creating hidden compliance risks across systems and workflows.

Common causes of inaccurate data

Errors arise from manual entry mistakes, outdated sources, poor integration between systems and lack of user verification. Without proper controls, these issues multiply, spreading incorrect information throughout an organisation’s data environment.

Key points:

• Human error during data entry, updating or handling can introduce mistakes that undermine the accuracy of personal data

  
  

• System mismatches occur when information is not properly synchronised between databases, leading to conflicting or duplicated records

  
  

• Old data sources may no longer reflect an individual’s current situation but continue to be relied upon

  
  

• Weak validation allows incorrect or incomplete data to be entered without being detected

Identifying the root causes of inaccurate data enables organisations to implement targeted safeguards, improve data handling processes and strengthen overall data quality, reducing compliance risks and helping ensure personal information remains reliable and fit for purpose.

The impact of incorrect data on individuals

Incorrect data can cause denial of services, wrongful accusations or financial loss. Individuals may suffer stress, reputational damage and reduced opportunities when organisations rely on errors instead of verified, current information.

Key points:

• Service denial can occur when incorrect data leads to individuals being wrongly refused access to products, benefits or essential services

  
  

• Emotional distress may result when people are forced to deal with errors, disputes or unfair treatment caused by inaccurate records

  
  

• Financial harm can arise from incorrect credit, billing or eligibility decisions based on faulty information

  
  

• Loss of trust develops when organisations repeatedly rely on inaccurate or outdated personal data

Protecting individuals from these harms is a central aim of the GDPR Accuracy Principle, ensuring people are treated fairly, decisions are based on reliable information, and organisations maintain trust, transparency and accountability in how they process personal data.

The Accuracy Principle in Practice

Applying the accuracy principle requires practical measures across the data lifecycle, from collection to deletion. Organisations must design processes that prevent errors, detect issues quickly and enable timely corrections to protect individuals and compliance.

Collecting data accurately at the point of entry

Accurate collection starts with clear forms, validation checks and user confirmation. Capturing correct data first time reduces downstream errors and minimises the need for later corrections, improving efficiency and regulatory compliance.

Key points:

• Clear data fields help users understand exactly what information is required, reducing confusion and incorrect entries

• Input validation checks prevent invalid, incomplete or inconsistent data from being submitted into systems

• User confirmation allows individuals to review and verify their information before it is recorded

• Standardised formats ensure data is captured consistently, making it easier to store, process and update

Good collection practices form the foundation of reliable data management by minimising errors at the point of entry, improving data quality across systems and supporting accurate, lawful and efficient use of personal information.

Verifying data before use

Before data is used for decisions, it should be verified against reliable sources. Verification ensures information is still correct and suitable for its purpose, particularly where outcomes significantly affect individuals.

Key points:

• Cross-checking sources helps confirm that personal data matches information held in other reliable systems or documents

• Risk-based verification ensures higher levels of checking where decisions have greater legal, financial or personal impact

• Human oversight provides judgement and context that automated systems alone may not detect

• Automated alerts flag inconsistencies, missing data or changes that require review

Verification protects against outdated, inconsistent or incorrect data being used in decision-making, helping organisations avoid unfair outcomes, reduce compliance risks and ensure personal information remains accurate, relevant and suitable for its intended purpose.

Regular review and maintenance of records

Ongoing reviews identify inaccuracies and trigger updates or deletions. Scheduled audits and automated reminders help ensure records remain accurate, relevant and compliant with retention and data protection requirements.

Key points:

• Periodic audits review records to identify inaccuracies, inconsistencies or outdated information that needs correction

  
  

• Automated reminders prompt staff or systems to review and update data at appropriate intervals

  
  

• Data cleansing removes duplicates, errors and obsolete records to improve overall data quality

  
  

• Retention controls ensure personal data is not kept longer than necessary

Ongoing maintenance keeps personal data trustworthy and accurate, helping organisations reduce long-term compliance risks, improve operational efficiency and ensure records remain relevant, lawful and fit for their intended purpose throughout the data lifecycle.

Rights of Individuals Under the Accuracy Principle

The GDPR grants individuals strong rights to ensure their data is accurate. These rights empower people to correct errors, challenge misuse and hold organisations accountable for maintaining high standards of data quality.

The right to rectification

Individuals can request correction of inaccurate or incomplete data. Organisations must respond promptly, ensuring records reflect the truth and any recipients of the data are informed of the updates where required.

Key points:

• Legal right gives individuals the ability to require organisations to correct inaccurate personal data under UK GDPR

  
  

• Applies to all data, regardless of format, system or how the information was collected

  
  

• Includes completion where data is incomplete and needs additional information to be accurate

  
  

• Third parties notified when corrected data has been shared with other organisations

  
  

Rectification restores fairness by ensuring personal data reflects the truth and prevents ongoing harm caused by incorrect or incomplete records, while also supporting transparency, accountability and compliance with data protection obligations.

How individuals can challenge incorrect data

People may challenge data through subject access requests, complaints or direct contact. Organisations must provide accessible channels and clear explanations, ensuring disputes are resolved transparently and in line with GDPR requirements.

Key points:

• Multiple channels allow individuals to raise concerns through email, online forms, phone or in writing

  
  

• Clear procedures explain how challenges are handled, assessed and resolved in a consistent and lawful way

  
  

• Transparent responses ensure people understand what action has been taken and why

  
  

• Documented outcomes provide a record of decisions and any corrections made

  
  

Effective challenge mechanisms build trust by showing that organisations take concerns seriously, while also reducing complaints, disputes and the likelihood of issues being escalated to regulators or supervisory authorities.

Timeframes for correcting inaccurate records

UK GDPR generally requires responses within one month. Where data is clearly wrong, corrections should be made without undue delay to minimise harm and demonstrate organisational accountability.

Key points:

• One-month deadline applies to most rectification requests under UK GDPR, starting from the date the request is received

  
  

• Faster where obvious means clear errors should be corrected without delay, even before the full deadline expires

  
  

• Extensions limited and only allowed in complex cases, with proper justification

  
  

• Communication required so individuals are kept informed about progress and outcomes

Meeting these timeframes is essential for lawful and fair data handling, ensuring individuals are not left with incorrect information on record and that organisations demonstrate accountability, responsiveness and compliance with their data protection obligations.

Organisational Responsibilities for Accuracy

Organisations must embed accuracy into governance, training and technical controls. This ensures personal data remains reliable throughout its lifecycle, supporting lawful processing and protecting individuals from the consequences of errors.

Processes for updating personal data

Clear procedures should exist for receiving updates, verifying changes and applying them across systems. Consistent processes prevent discrepancies and ensure all records reflect the most current, correct information.

Key points:

• Update workflows define how changes to personal data are requested, reviewed and applied across the organisation

  
  

• Verification steps ensure updates are accurate, authorised and supported by reliable evidence

  
  

• System synchronisation keeps records consistent across different databases and platforms

  
  

• User notifications inform individuals when their data has been updated or corrected

Well-designed processes reduce confusion, prevent inconsistencies and lower compliance risks by ensuring personal data is handled in a controlled, transparent and accurate way throughout all systems and business activities.

Training staff on data accuracy

Staff must understand the importance of accurate data and how to handle corrections. Regular training reduces human error, improves data quality and ensures everyone knows their role in maintaining GDPR compliance.

Key points:

• Induction training introduces new staff to data accuracy requirements and their responsibilities under data protection law

  
  

• Refresher courses keep employees up to date with policies, risks and best practices

  
  

• Role-specific guidance ensures staff understand how accuracy applies to their particular duties

  
  

• Error reporting encourages prompt identification and correction of mistakes

  
  

• Skilled and informed staff are critical to sustaining accurate records, reducing human error and ensuring that personal data is handled correctly, consistently and in line with GDPR obligations across the organisation.

Accountability and audit trails

Organisations should record who made changes, when and why. Audit trails provide evidence of compliance, support investigations and help demonstrate that reasonable steps were taken to maintain accuracy.

Key points:

• Change logs record what data was updated, when it was changed and who made the amendment

  
  

• User accountability ensures staff actions can be traced and reviewed where issues arise

  
  

• Compliance evidence demonstrates that accuracy requirements are being followed in practice

• Incident investigation is supported by clear records showing how errors occurred and were resolved

Strong records protect organisations during audits, complaints and legal disputes by providing clear evidence of responsible data handling, accountability and ongoing compliance with GDPR accuracy and governance requirements.

Accuracy and Automated Decision-Making

Automated systems rely heavily on data quality. Inaccurate inputs can lead to biased, unfair or unlawful outcomes, making the accuracy principle especially critical when algorithms and profiling are used to make significant decisions.

Why inaccurate data is especially risky in automated systems

Automated decisions scale errors quickly. A single inaccuracy can affect thousands of people, producing systemic unfairness and regulatory breaches that are harder to detect and correct without robust monitoring.

Key points:

• Errors multiply because a single inaccuracy can be replicated across thousands of automated decisions

  
  

• Harder to detect as problems may remain hidden within complex algorithms and large datasets

  
  

• High impact since automated decisions often affect large numbers of individuals at once

  
  

• Regulatory exposure increases when widespread errors lead to unfair or unlawful outcomes

High-quality data is essential for responsible automation, ensuring automated systems produce fair, reliable and lawful results while reducing the risk of systemic errors, regulatory action and harm to individuals.

Preventing bias caused by incorrect records

Incorrect or outdated data can introduce bias into models, skewing results against certain groups. Regular data quality checks and diverse testing help ensure automated systems remain fair and compliant.

Key points:

• Data quality checks identify errors, gaps and inconsistencies that could distort automated decision-making

• Bias testing assesses whether certain groups are unfairly affected by the data or outcomes

• Diverse datasets help ensure systems are trained on representative and balanced information

• Ongoing monitoring detects emerging issues as data and circumstances change

Preventing bias protects individuals from unfair treatment and discrimination, while also safeguarding organisational reputation, regulatory compliance and public trust in the use of automated systems and data-driven decision-making.

Reviewing and correcting data used in profiling

Data used for profiling must be accurate and relevant. Regular reviews and correction mechanisms ensure profiles reflect reality, reducing the risk of unfair treatment or unlawful automated decisions.

Key points:

• Profile audits regularly review profiling data to identify inaccuracies, outdated information or inappropriate assumptions

  
  

• User challenges allow individuals to question and correct the data used to create their profiles

  
  

• Data refreshes ensure profiling information remains current and relevant

  
  

• Documentation records how profiling data is created, reviewed and updated

  
  

Responsible profiling depends on continuous data accuracy, ensuring profiles reflect real circumstances, support fair and lawful decisions, and allow organisations to demonstrate transparency, accountability and compliance with data protection requirements.

How to Demonstrate Compliance with the Accuracy Principle

Demonstrating compliance requires documented policies, evidence of corrections and clear governance. Regulators expect organisations to show how they ensure accuracy in practice, not just in theory.

Policies and procedures for data accuracy

Written policies should define standards, responsibilities and workflows for maintaining accurate data. These documents guide staff, support training and provide a benchmark for internal and external audits.

Key points:

• Clear standards set out what accurate data looks like and how it should be maintained across the organisation

  
  

• Defined roles assign responsibility for collecting, checking and updating personal data

  
  

• Approved procedures provide consistent methods for handling accuracy and corrections

  
  

• Regular reviews ensure policies remain effective and up to date

  
  

Strong policies create consistent and defensible practices by giving staff clear guidance, supporting accountability and helping organisations demonstrate that data accuracy is managed in a structured, lawful and reliable way.

Documenting corrections and updates

Every correction should be recorded, including the reason and date. Documentation provides an audit trail, supports accountability and helps demonstrate that the organisation takes accuracy and individuals’ rights seriously.

Key points:

• Change records show what personal data was altered and what the previous information was

  
  

• Reasons noted explain why the correction or update was made

  
  

• Dates logged provide a timeline of when changes occurred

  
  

• User confirmation verifies that individuals have reviewed or requested the update

  
  

Good documentation is vital for compliance evidence, allowing organisations to demonstrate accountability, track how data has been managed over time and prove that corrections were made promptly and in line with GDPR requirements.

Evidence regulators may expect

Regulators may request policies, logs, training records and examples of corrected data. Providing clear, organised evidence shows that accuracy is embedded into governance and day-to-day operations.

Key points:

• Policies show how the organisation intends to meet its data accuracy obligations

  
  

• Audit trails provide evidence of how personal data has been updated and maintained

  
  

• Training materials demonstrate that staff are taught how to handle data accurately

  
  

• Case examples illustrate how accuracy issues have been identified and resolved
  

Preparedness reduces enforcement risk by ensuring organisations can quickly provide clear, credible evidence of compliance, show regulators how accuracy is managed in practice and demonstrate that individuals’ rights are taken seriously.

Key Takeaways for GDPR Accuracy

The accuracy principle is central to trustworthy data processing. By keeping data correct, current and complete, organisations protect individuals, improve decision-making and meet their legal obligations under UK GDPR.

Practical steps organisations should take

Organisations should validate data at collection, review it regularly and enable easy corrections. Training, technology and governance must work together to maintain high data quality across all systems.

Key points:

• Validate inputs to ensure personal data is accurate, complete and in the correct format when first collected

  
  

• Review regularly so records remain current and continue to reflect individuals’ real circumstances

  
  

• Enable rectification by making it easy for people to request corrections and updates

• Train staff so everyone understands their role in maintaining data accuracy

  
  

Practical controls turn legal requirements into everyday compliance by embedding accuracy into routine processes, reducing errors, supporting individuals’ rights and ensuring personal data is handled in a reliable, lawful and consistent way across the organisation.

How accuracy supports wider GDPR compliance

Accurate data supports fairness, transparency and accountability, strengthening compliance across all GDPR principles. When data is reliable, organisations can lawfully process information, respect rights and build lasting trust with customers and citizens.

Key points:

• Supports fairness by ensuring decisions about individuals are based on correct, relevant and reliable personal data

  
  

• Enables transparency by allowing organisations to clearly explain how and why personal data is used

  
  

• Reduces risk of errors, complaints and regulatory breaches arising from inaccurate information

  
  

• Builds trust between organisations and individuals through responsible data handling

  
  

Accuracy is the foundation of effective and ethical data protection, ensuring personal data is handled lawfully, decisions are fair and transparent, and organisations maintain the confidence of individuals, regulators and wider stakeholders.