Why Data Integrity Remains Challenging for Pharmaceutical Companies

Growing Interest and Persistent Challenges

Interest in data integrity continues to grow within the pharmaceutical industry. However, the reality is that many companies continue to struggle with implementing effective data integrity measures. This challenge is not unique to any single organization; it represents a widespread industry concern that requires sustained attention and systematic approaches.

Understanding why data integrity implementation remains difficult for enterprises is essential for developing effective solutions. The challenges can be attributed to several interconnected factors that must be addressed holistically.

Four Core Challenges Facing Organizations

Performance and Business Pressures

Pharmaceutical companies operate in a highly competitive environment where they must balance drug development, manufacturing operations, and market demands. As for-profit entities, the pursuit of revenue and profitability is an inherent business necessity. However, when business priorities overwhelm quality considerations, data integrity measures often become deprioritized or implemented superficially.

This tension between operational efficiency and compliance requirements creates a challenging dynamic. Companies may view data integrity initiatives as impediments to productivity rather than as fundamental quality requirements. This mindset can lead to corner-cutting, inadequate resource allocation, and insufficient time devoted to proper implementation.

The pressure to meet production schedules, market timelines, and financial targets can create an environment where data integrity controls are viewed as obstacles rather than safeguards. Organizations must recognize that robust data integrity is not merely a compliance checkbox but a critical foundation for sustainable business success and patient safety.

Lack of Awareness and Capability Deficiencies

Many employees lack fundamental awareness of how their daily activities impact data integrity. They may not fully comprehend the extent to which their work touches upon data integrity requirements or the potential consequences of data quality failures.

More critically, many personnel fail to consider the downstream impacts of compromised data integrity. When data reliability is undermined, whether through inadvertent errors or systemic failures, the consequences can be severe: patients and clinical trial subjects may be exposed to health risks, and the reliability of regulatory submission data may be called into question. These potential impacts often remain abstract concepts rather than concrete concerns that influence daily decision-making.

The first critical step is ensuring that all employees gain awareness of data integrity’s fundamental importance. This awareness must extend beyond quality departments to encompass all personnel whose activities generate, process, or utilize GxP data.

However, awareness alone proves insufficient. Even when employees recognize data integrity’s importance, practical implementation challenges persist. Organizations may lack sufficient personnel to implement proper verification controls such as independent review processes. More fundamentally, staff may lack the necessary knowledge, technical skills, or experience to effectively identify data errors, anomalies, or integrity issues.

According to recent research, studies have demonstrated that 40% of quality professionals report being unable to find the true root causes of issues, and 80% of manufacturing investigators do not identify definitive root causes, instead concluding with probable causes—often defaulting to “human error” as an explanation. This pattern suggests that capability gaps extend beyond operational personnel to include those responsible for quality oversight and investigation.

Absence of Quality Culture Integration

The current imperative is the cultivation of a “Quality Culture”—an organizational environment where data integrity is valued and prioritized by everyone from executive leadership through all levels of the organization. This culture must be embedded in daily operations rather than existing merely as stated policy.

Through extensive consultation work, I have reviewed Standard Operating Procedures (SOPs) from numerous companies. When questioning why certain processes are structured in particular ways, responses frequently reveal a troubling pattern: “I don’t know the reason, but our company has been conducting operations this way for a very long time.” This represents a fundamental failure of quality culture—procedures are followed by rote without understanding the underlying rationale or recognizing when processes may be outdated or inadequate.

Such an approach makes effective data integrity implementation impossible. What is required instead is a commitment to investing time and effort in continuously improving SOPs, systematically identifying and mitigating all relevant risks, and fostering an environment where questioning and improvement are encouraged rather than discouraged.

The mindset that “following established procedures is sufficient” must be reformed. Organizations must move beyond blind adherence to legacy practices and develop critical thinking capabilities that enable continuous assessment and improvement of data integrity controls.

Understanding Current Data Integrity Standards: ALCOA to ALCOA++

It is important to note that data integrity expectations have evolved significantly beyond the original principles. The industry has progressed from ALCOA to ALCOA+ and now to ALCOA++, reflecting increasingly sophisticated regulatory expectations.

ALCOA Principles (Original framework from the 1990s):

• Attributable: Every action can be traced to a specific individual
• Legible: Data is clear, readable, and understandable
• Contemporaneous: Data is recorded in real-time as actions occur
• Original: Documents are preserved in their first-recorded form
• Accurate: Data reflects exactly what was observed

ALCOA+ Principles (Expanded framework): In addition to ALCOA, the following criteria are added:

• Complete: Data is documented fully with nothing omitted
• Consistent: Documentation is chronological and orderly
• Enduring: Records are maintained for regulatory-specified durations
• Available: Documents are accessible when needed for audit or inspection

ALCOA++ Principles (Current best practice): Building upon ALCOA+, this framework adds:

• Traceable: Complete audit trails that track all data lifecycle activities
• Additional attributes including: Integrity, Robustness, Transparency, Accountability, and Reliability

These principles are endorsed by regulatory authorities including FDA, EMA, MHRA, WHO, and are incorporated into frameworks such as GAMP (Good Automated Manufacturing Practice) guidance. The evolution from ALCOA to ALCOA++ reflects the increasingly complex data environments in modern pharmaceutical operations, particularly with the integration of digital systems, artificial intelligence, and interconnected global operations.

Inadequate Processes and Technology

As previously discussed, SOPs must systematically address and mitigate all relevant risks. However, many organizations design procedures based on the assumption that operations will proceed normally, without accounting for potential failures or deviations.

A fundamental principle of effective data integrity implementation is anticipating scenarios where processes do not proceed as planned. Risk assessment must be embedded in process design.

Consider these common examples:

Process: Transcription

• Risk: Transcription errors, misreading source data, incorrect transfer
• Mitigation: Dual verification by independent reviewers, electronic data transfer where feasible

Process: Calculation

• Risk: Computational errors, formula mistakes, incorrect parameter entry
• Mitigation: Automated calculations with validation, independent recalculation for critical values

Process: Data Entry

• Risk: Input errors, typographical mistakes, field transposition
• Mitigation: Data validation rules, range checks, independent verification

To discover and prevent such errors, controls such as independent review (often called “double-checking” but more properly termed “independent verification”) should be implemented. However, the specific control must be appropriate to the risk level and nature of the data.

Current processes that fail to adequately consider and control risks must be urgently reviewed and revised. This applies equally to both paper-based and electronic systems.

Regarding technology, several specific concerns warrant attention:

MS-Excel and Similar Tools: Microsoft Excel and similar spreadsheet applications present significant security and audit trail risks when used for GxP-critical data. While widely used due to familiarity and flexibility, these tools often lack adequate controls for data integrity, including:

• Limited or absent audit trails
• Ease of undetected data manipulation
• Difficulty in controlling formula changes
• Insufficient access controls

Organizations must assess Excel usage risk-critically and implement compensating controls or migrate to validated systems where appropriate.

Simple Standalone Instruments: Equipment such as electronic balances, Total Organic Carbon (TOC) analyzers, and pH meters often are not directly interfaced with database systems. Consequently, they require human-mediated transcription of results, introducing integrity risks at the data transfer point. Where feasible, direct electronic data capture should replace manual transcription.

The use of inappropriate technology itself represents a form of data integrity risk. Organizations that fail to assess and upgrade their technology infrastructure accordingly will continue to lag in data integrity compliance.

The Primary Focus: Addressing Human Error

An important perspective emerges when we examine the actual causes of data integrity violations. Contrary to what some might assume based on regulatory emphasis, the data reveals a striking pattern.

The largest single cause of data integrity violations is unintentional error—human error. Remarkably, according to available industry data, unintentional mistakes account for the majority of data integrity issues. Recent studies indicate that over 80% of process deviations and approximately 25% of all quality faults—ranging from laboratory errors and complaints to inspection concerns—are attributed to human error.

The remaining portion consists of intentional data modification. However, it is crucial to understand that “intentional modification” does not necessarily imply malicious intent. In many cases, personnel believe they are correcting errors and improving data accuracy, but their corrections are themselves incorrect. These situations arise from misconceptions, misunderstandings, or inadequate training rather than deliberate fraud.

When attending regulatory authority presentations, one frequently hears strong emphasis on fraud prevention. Consequently, many companies have developed the misconception that data integrity implementation is primarily about preventing deliberate misconduct.

However, this raises an important question: Is deliberate fraud actually a routine, everyday occurrence in pharmaceutical operations? The data suggests otherwise. The primary challenge that should be addressed is human error—unintentional mistakes that arise from various factors including inadequate training, poorly designed processes, unclear procedures, fatigue, distractions, and insufficient verification systems.

Furthermore, many data integrity seminars and training programs focus disproportionately on electronic record security—encryption, access controls, cybersecurity measures, and system validation. While these are important, data integrity requirements extend equally to paper records and handwritten documentation.

When considering the question from a patient safety perspective: Is the falsification of electronic records more serious than the falsification of paper records? The answer is unequivocal—both are equally serious because both equally impact patient safety, product quality, and regulatory compliance.

Recent statistics reinforce the human element’s critical importance: approximately 95% of data breaches are tied to human error, and manual processes in pharmaceutical manufacturing remain highly susceptible to mistakes. This reality underscores that data integrity is not solely a technology problem requiring technical solutions, but fundamentally a human factors challenge requiring cultural, procedural, and behavioral interventions.

Addressing Human Error Through Systematic Approaches

Given that human error represents the predominant cause of data integrity issues, organizations must implement systematic approaches to minimize its occurrence. Several evidence-based strategies have proven effective:

Human and Organizational Performance (HOP) Principles

Integrating Human and Organizational Performance methodologies into quality culture examines the interactions between people, systems, processes, and organizational culture. This approach builds resilience into systems and minimizes mistake likelihood by:

• Creating environments where employees feel empowered to report potential problems without fear of retaliation
• Enhancing transparency and communication across all organizational levels
• Understanding that most errors result from system failures rather than individual negligence
• Analyzing the context and conditions that made the error possible or likely

Error-Proofing Techniques (Poka-Yoke)

Implementing mistake-proofing mechanisms that force correct action execution, leaving no room for misunderstanding. Many effective solutions prove simple, economical, and can be integrated into product design or process steps. Examples include:

• Physical constraints that prevent incorrect assembly or processing
• Detection systems that identify errors before they propagate downstream
• Warning mechanisms that alert operators to potential mistakes
• Automated validation that verifies data entry before acceptance

Behavior Engineering Model

Utilizing systematic frameworks to assess both individual performance and working environment, identifying conditions that may contribute to errors. This model examines three critical categories shared between environment and individual:

• Information: Are clear, accessible procedures and references available?
• Instrumentation: Do personnel have adequate tools and resources?
• Motivation: Are appropriate incentives and consequences in place?

Advanced Technology Integration

Leveraging modern technology to reduce manual intervention and human error:

• Artificial Intelligence and Machine Learning: AI continuously monitors data entries, detecting anomalies that may signal human errors such as incorrect inputs or missing information
• Automated Data Capture: Replacing manual transcription with direct electronic interfaces reduces transcription errors
• Real-time Monitoring: Systems that provide immediate feedback when data falls outside expected parameters
• Predictive Analytics: Identifying patterns that may precede errors, enabling preventive intervention

Current Regulatory Landscape and Enforcement Trends

Understanding the current regulatory environment helps contextualize why data integrity remains such a critical focus for pharmaceutical companies:

Increased Regulatory Scrutiny

Recent data from 2024 indicates that approximately 40% of FDA warning letters cited data integrity issues. These citations commonly involve:

• Missing or incomplete audit trails
• Uncontrolled changes to data or records
• Inadequate investigation of data anomalies
• Failure to maintain original records
• Insufficient data review procedures

This pattern repeats across regulatory authorities including FDA, EMA (European Medicines Agency), and MHRA (UK Medicines and Healthcare products Regulatory Agency), demonstrating that data integrity remains a global regulatory priority.

Focus on Data Governance Systems

Regulatory authorities have evolved their approach from merely checking technical compliance toward evaluating comprehensive data governance systems. The FDA’s 2018 Data Integrity Guidance, MHRA’s GxP Data Integrity Guidance (updated 2021), and PIC/S Guidance on Good Data and Record Management Practices emphasize holistic approaches to data integrity rather than checklist-style compliance.

Risk-Based Approaches

Regulators increasingly encourage manufacturers to apply critical thinking and implement risk-based decisions about data governance. Rather than focusing solely on collecting required documentation, the emphasis has shifted toward:

• Identifying the most critical data and associated vulnerabilities
• Implementing appropriate controls proportionate to risk
• Demonstrating effective data lifecycle management
• Proving that data governance systems function as designed

Building a Comprehensive Data Integrity Framework

Effective data integrity implementation requires a multi-layered approach that addresses technical, procedural, and cultural dimensions:

Establish Robust Data Governance

A comprehensive data governance framework should:

Governance Element	Key Components
Policies and Procedures	Clear, risk-based procedures covering entire data lifecycle; regular review and update cycles; procedures that account for what happens when things go wrong
Roles and Responsibilities	Defined accountability at all organizational levels; clear ownership of data throughout lifecycle; segregation of duties for critical functions
Data Lifecycle Management	Controls appropriate to each lifecycle phase (creation, processing, review, approval, storage, retrieval, archival); metadata management; retention and disposition procedures
Training and Competency	Initial and ongoing training on data integrity principles; role-specific training on procedures and systems; assessment and documentation of competency
Quality Culture	Leadership commitment and modeling; open communication regarding errors and near-misses; focus on system improvement rather than individual blame

Implement Appropriate Technology Controls

Technology should support, not replace, good data management practices:

• Validated Systems: All computerized systems used for GxP data must be validated according to risk-based approaches consistent with GAMP guidance
• Audit Trails: Electronic systems must generate complete, time-stamped audit trails that capture who, what, when, and why for all data modifications
• Access Controls: Role-based access restrictions; prohibition of shared login credentials; regular review of user accounts and privileges
• Data Backup and Recovery: Regular backup procedures; tested recovery processes; business continuity planning
• System Integration: Where feasible, implement direct data capture and transfer to eliminate transcription steps

Conduct Regular Assessments and Continuous Improvement

Proactive monitoring identifies issues before they escalate:

• Self-Inspection Programs: Regular internal audits examining both technical systems and human processes
• Data Integrity Metrics: Key performance indicators tracking data quality, deviation rates, and control effectiveness
• Trending and Analysis: Systematic review of patterns that might indicate emerging issues
• External Audits: Periodic third-party assessment providing independent validation
• Corrective and Preventive Action (CAPA): Robust systems for investigating issues and implementing sustainable improvements

Conclusion

Data integrity implementation remains challenging for pharmaceutical companies due to interconnected factors including business pressures, awareness and capability gaps, cultural deficiencies, and inadequate processes and technology. However, these challenges are not insurmountable.

The key insight is that data integrity violations predominantly result from human error rather than deliberate fraud. This understanding should fundamentally shape implementation strategies, with emphasis placed on:

• Creating robust systems that prevent and detect errors
• Building quality cultures that encourage transparency and continuous improvement
• Implementing appropriate technology to reduce manual intervention
• Providing comprehensive training that develops both awareness and competency
• Applying risk-based approaches that focus resources on the most critical data

Data integrity is not merely a compliance obligation—it is a fundamental requirement for ensuring that pharmaceutical products are safe, effective, and of consistent quality. When data integrity is compromised, whether through paper records or electronic systems, patient safety is equally jeopardized.

Organizations that approach data integrity comprehensively, addressing technical, procedural, and cultural dimensions while focusing practical efforts on preventing human error, will be best positioned to meet evolving regulatory expectations and fulfill their fundamental obligation to protect public health.

As the industry continues to evolve with increasing digitalization, artificial intelligence, global supply chains, and complex manufacturing processes, the importance of robust data integrity frameworks will only grow. Companies that invest now in building strong foundations will be better prepared for future challenges and better able to demonstrate the quality, safety, and efficacy of their products to regulators, healthcare providers, and ultimately, to the patients who depend on them.