The Importance of Regular Data Integrity Assessments

The Nature of Data Integrity Evaluation

Data integrity issues, by their very nature, possess characteristics that prevent them from surfacing without proactive investigation.

For instance, when the number of police officers increases, the number of speeding violations and parking violations detected also rises. This does not mean that the number of speeders has increased, nor does it indicate a rise in parking violators. What has increased is the rate of detection.

Similarly, the more Medical Representatives (MRs) are trained, the greater the number of adverse event reports submitted. Again, this does not reflect an actual increase in adverse events; rather, it represents an enhancement in awareness and recognition.

In the same way, strengthening investigative activities related to data integrity inevitably leads to the discovery of more data integrity violation cases. This phenomenon is well-documented in regulatory guidance, including FDA’s “Data Integrity and Compliance With Drug CGMP Questions and Answers” (revised in May 2023) and the EU GMP Annex 11 (Computerised Systems), which emphasize the need for proactive monitoring and regular audits.

Short-term Approach and Implementation

From a short-term perspective, the highest priority is to recognize and clearly identify data integrity issues that exist within the organization. Through concrete investigative activities, it is necessary to identify and document problems such as data inconsistencies, input errors, and system defects. The number of issues discovered during this process often exceeds initial expectations. Organizations are required to implement swift and effective countermeasures against these problems.

Key short-term activities should include:

Initial Gap Assessment: Conducting a comprehensive review of current practices against regulatory requirements, including FDA’s 21 CFR Part 11 (Electronic Records; Electronic Signatures), EU GMP Annex 11, and the ALCOA+ principles (Attributable, Legible, Contemporaneous, Original, Accurate, plus Complete, Consistent, Enduring, and Available).

Risk-based Prioritization: Not all data integrity issues carry the same level of risk. Organizations should employ a risk-based approach to prioritize remediation efforts, focusing first on areas that directly impact product quality, patient safety, or regulatory compliance. This approach aligns with ICH Q9 (Quality Risk Management) principles.

Immediate Containment: For critical findings, immediate containment measures must be implemented to prevent further issues while long-term solutions are developed.

Medium to Long-term Improvement Strategy

From a medium to long-term perspective, implementing systematic improvement activities for identified issues is indispensable. First, it is necessary to optimize the entire system, from data input through processing to storage. Next, business workflows should be reviewed and standardized to improve process efficiency. Furthermore, through enhancing the skills of personnel and sharing best practices, the organization aims to improve overall capability. In parallel with these activities, establishing mechanisms for continuous monitoring and evaluation builds the foundation that enables sustained improvement.

System Optimization and Modernization: Organizations should invest in modern data management systems that incorporate built-in data integrity controls. This includes audit trail functionality, electronic signatures compliant with 21 CFR Part 11, access controls based on the principle of least privilege, and automated validation protocols. The implementation of cloud-based Laboratory Information Management Systems (LIMS) or Electronic Laboratory Notebooks (ELN) can significantly reduce human error and improve traceability.

Process Standardization: Developing and implementing Standard Operating Procedures (SOPs) that specifically address data integrity requirements is crucial. These SOPs should cover data lifecycle management, including data creation, modification, review, approval, archival, and destruction. The GAMP 5 guidelines (Good Automated Manufacturing Practice) provide valuable frameworks for validation and compliance in computerized systems.

Training and Competency Development: A comprehensive training program should be established that covers not only what data integrity is, but why it matters and how to maintain it in daily operations. Training should be role-specific and include practical scenarios. Competency assessments should be conducted regularly to ensure understanding and compliance.

Quality Culture Enhancement: Building a quality culture where employees understand that data integrity is not just about compliance but about ensuring patient safety and product quality is fundamental. This involves leadership commitment, open communication channels, and a non-punitive approach to error reporting that encourages transparency.

Realistic Goal Setting

Completely eliminating data integrity violations to zero is extremely difficult in reality. Rather, it is important to pursue continuous improvement “like wringing water from a dry towel.” This signifies a steady effort to identify even the smallest opportunities for improvement and steadily accumulate results. Organizations can achieve sustainable improvement activities by setting realistic goals and progressing through incremental improvements.

It is essential to recognize that human error is inevitable, and systems should be designed with this understanding. The goal is not perfection but rather the establishment of robust systems that detect, prevent, and correct errors before they impact product quality or patient safety. Metrics should focus on leading indicators (preventive measures) as well as lagging indicators (actual violations detected).

Establishing Meaningful Metrics: Organizations should develop Key Performance Indicators (KPIs) that measure data integrity maturity. These might include:

Metric Category	Example Indicators
Preventive Measures	Number of employees trained, System validation status, Audit trail review completion rate
Detection Capabilities	Self-inspection frequency, Deviation detection rate, Time to detect anomalies
Corrective Actions	CAPA closure time, Repeat violation rate, Effectiveness of corrective measures
Cultural Indicators	Employee reporting rate, Quality culture survey scores, Management review frequency

Benchmarking and Continuous Improvement: Organizations should engage in industry benchmarking to understand their performance relative to peers and identify areas for improvement. Participation in industry forums, such as those organized by the Pharmaceutical Inspection Co-operation Scheme (PIC/S) or the International Society for Pharmaceutical Engineering (ISPE), can provide valuable insights and best practices.

Building a Sustainable Improvement Framework

To achieve long-term success, it is necessary to build a sustainable improvement framework. The construction of an efficient data management system, the implementation of automated checking functions, and the adoption of scalable architecture form the technical foundation. In addition to this, raising awareness throughout the organization regarding the importance of data integrity and building a highly transparent organizational culture where problems can be reported without concealment are indispensable.

Furthermore, by establishing mechanisms for regular evaluation and feedback and measuring and reviewing the effectiveness of improvement activities, it is possible to maintain a continuous improvement cycle. In this process, developing personnel, such as improving specialized knowledge regarding data integrity and strengthening problem-solving capabilities, also becomes an important element. Through cross-organizational skill development, it becomes possible to build a more robust improvement framework.

Technology Enablers: Modern technology offers significant opportunities to enhance data integrity. Artificial Intelligence (AI) and Machine Learning (ML) can be employed to detect anomalies in data patterns, predict potential integrity issues before they occur, and automate routine compliance checks. Blockchain technology is being explored for its potential to create immutable audit trails, particularly in supply chain management and clinical trial data management.

Regulatory Technology (RegTech): The adoption of RegTech solutions can help organizations stay current with evolving regulatory requirements and automate compliance monitoring. These systems can track regulatory changes across multiple jurisdictions and assess their impact on organizational processes.

Data Governance Framework: A comprehensive data governance framework should define:

• Data ownership and stewardship responsibilities
• Data classification and criticality assessment
• Data retention and archival policies
• Access rights and privilege management
• Change control procedures
• Incident response protocols

Third-party Management: Organizations must extend data integrity requirements to their suppliers, contractors, and service providers. This includes conducting data integrity audits of third parties, ensuring contractual obligations include data integrity requirements, and providing training to external partners on organizational data integrity standards.

Regulatory Landscape: As of 2025, regulatory authorities worldwide continue to increase their focus on data integrity. The FDA’s warning letters consistently cite data integrity violations as significant findings. The Medicines and Healthcare products Regulatory Agency (MHRA) in the UK and the European Medicines Agency (EMA) have published comprehensive guidance documents. In Asia, regulatory bodies such as the Pharmaceuticals and Medical Devices Agency (PMDA) in Japan and China’s National Medical Products Administration (NMPA) have aligned their expectations with international standards, emphasizing the global nature of data integrity requirements.

The World Health Organization (WHO) has also published guidance on data integrity in its Technical Report Series, recognizing the critical role of data integrity in ensuring the quality of medicines globally, particularly in emerging markets.

Conclusion

Maintaining and improving data integrity is a continuous effort that cannot be achieved overnight. It is required to recognize it as an organizational challenge and steadily move forward while balancing short-term results with long-term improvements. In particular, comprehensively promoting technical measures, personnel development, and the cultivation of organizational culture becomes the key to sustained success. Ensuring data integrity is an important element that enhances organizational trustworthiness and competitiveness, and it should be positioned as an issue that is part of management strategy.

In an era of increasing regulatory scrutiny, digital transformation, and globalization of pharmaceutical operations, data integrity has evolved from a compliance checkbox to a fundamental business imperative. Organizations that embed data integrity into their DNA—through robust systems, trained personnel, and a strong quality culture—will not only meet regulatory expectations but also gain competitive advantage through operational excellence and enhanced stakeholder trust.

The journey toward data integrity excellence is ongoing, requiring sustained commitment, adequate resources, and continuous adaptation to new challenges and technologies. By viewing data integrity not as a burden but as an opportunity to strengthen quality systems and protect patients, organizations can transform compliance into a strategic asset that drives long-term success in the pharmaceutical industry.