Overview:
In the life sciences, the concept of "data integrity" has evolved far beyond simple compliance expectations. Historically, organizations treated data integrity as a documentation or recordkeeping issue-something to audit, correct, or defend after the fact.
Today, regulators and industry leaders are shifting toward the principle of data integrity by design, an approach that embeds reliability, traceability, and scientific truth into the operational systems, technologies, and human behaviors that generate data. Instead of trying to police bad data, the goal is to create environments where inaccurate, falsified, or incomplete data cannot easily occur.
Regulatory expectations reflect this shift. FDA, EMA, MHRA, PIC/S, and WHO emphasize that data must meet ALCOA++ principles: attributable, legible, contemporaneous, original, accurate, complete, consistent, enduring, and available. They further expect organizations to manage the entire data lifecycle-from creation to archival to eventual destruction. Importantly, these expectations apply across all GxP domains, including pharmaceutical manufacturing, laboratory testing, clinical research, medical devices, pharmacovigilance, and biologics development. Modern guidance makes it clear: data integrity failures are not caused by dishonest workers; they are usually the result of poor processes, weak system design, and unhealthy corporate culture.
Process design plays a central role. When organizations rely heavily on manual transcription, duplicate data entry, retrospective documentation, or unnecessarily complex paper forms, they unintentionally create opportunities for error, omission, and manipulation. A poorly constructed workflow may make it impossible for employees to record data contemporaneously, forcing them to "recreate reality" later in the day. Similarly, systems that reward flawless metrics or punish variance in results encourage workers to alter or omit data to protect themselves or their team. Data integrity by design seeks to remove these vulnerabilities. It prioritizes simplification, real-time data capture, barcode or scanner verification, sequence locking, automation of calculations, and the elimination of redundant documentation. When well-designed, the workflow itself ensures that honest data is the easiest data to produce.
Technology also carries enormous responsibility. Software systems must enforce integrity through secure user access control, tamper-evident audit trails, time-stamped records, version management, and restricted storage. Regulations require not just system validation, but validation of data integrity controls themselves-testing whether a user can alter time stamps, delete data, save locally, or bypass an audit trail. Cloud platforms, SaaS tools, laboratory instruments, building management systems, and even spreadsheets must be governed under this principle.
Finally, data integrity by design depends on organizational culture. Leadership must value truthful data over "good news" or productivity metrics. Employees need psychological safety to report anomalies without fear of blame. Rewards should encourage transparency, not perfection. Accountability should be shared across quality, IT, operations, and management, reinforcing that data is not merely an output, but a regulated scientific asset.
Ultimately, data integrity by design recognizes that trustworthy information is not the result of vigilance-it is the outcome of systems engineered for truth.
Why you should Attend:
Data integrity failures are one of the most expensive and damaging risks facing life sciences companies today. They lead to warning letters, delayed approvals, batch rejections, failed inspections, and even criminal liability. Yet most integrity problems are not caused by careless or dishonest employees-they arise from poorly designed workflows, inadequate system controls, and organizational pressures that make it difficult to document reality as it happens. This webinar will help you move beyond reactive policy enforcement and understand how to build systems that naturally produce accurate, trustworthy, regulator-ready data.
Participants will learn how FDA, EMA, MHRA, and PIC/S now expect integrity to be engineered into processes, rather than policed through SOPs or post-event auditing. You will see how automation, system configuration, audit trails, barcoding, workflow sequencing, and role-based access controls eliminate opportunities for falsification and error. More importantly, you will learn how culture, metrics, and leadership expectations influence the quality of scientific data-even when technology is fully compliant.
Whether you work in manufacturing, QC/QA, clinical operations, R&D, IT/CSV, or regulatory affairs, this session will help you prevent integrity failures before they occur, protect your organization from costly compliance findings, and ensure that your data reflects scientific truth, not pressure or convenience.
Areas Covered in the Session:
- Introduction (3-5 min)
- Shift from inspection to prevention
- Integrity as a design choice, not a documentation burden
- Data integrity applies across GMP, GCP, GLP, PV, Devices
- Goal: make falsification and manipulation impossible or unattractive
- Regulatory Foundations (10 min)
- ALCOA - ALCOA++
- Data lifecycle requirements
- FDA (210/211, 820/QMSR, Part 11)
- EU Annex 11 & Annex 15 expectations
- MHRA/PIC/S focus on falsification risk
- Trend: regulators expect built-in system controls
- SOPs alone are insufficient
- Process Design for Integrity (12 min)
- Remove duplicate data entry
- Eliminate manual transcriptions
- Require contemporaneous entry
- Embed sequencing and step-locks
- Barcode/scanning to prevent errors
- Reduce cognitive load in forms
- Workarounds as red flags of poor design
- Prevent incentive-driven data manipulation
- System/Technology Design for Integrity (12 min)
- Role-based access restrictions
- Secure identity authentication
- Automated time-stamps
- Automatic audit trails
- Controlled storage (no local saves)
- Audit trail review built into workflow
- Validation of DI controls (not just system functions)
- Risk areas: spreadsheets, SaaS/cloud, hybrid paper/e-systems
- Cultural & Organizational Design (10 min)
- Integrity expectations = truthful data over "good results"
- Leadership pressure as a DI risk factor
- Accountability distribution: Quality / IT / Operations / Leadership
- Transparency rewarded; falsification discouraged
- Non-punitive reporting of anomalies
- Stop-work authority for DI concerns
- Metrics should not encourage data manipulation
- Mini-Scenarios (8 min)
- Backdated chromatography entries - access control failure
- "Corrected" spreadsheet data - uncontrolled calculation tool
- Hidden batch deviations to meet KPIs - leadership incentive failure
- Ask: "Which design control would have prevented this?"
- Summary & Q&A (3-5 min)
- DI is a product of process + technology + culture
- Good system design prevents bad behaviors
- SOPs cannot compensate for flawed workflows
- Key takeaway: build integrity into how data is generated
Who Will Benefit:
- Data integrity has long been viewed as a documentation requirement, managed through SOPs, training, and corrective action when issues are detected. However, recent regulatory actions show that this traditional approach is no longer sufficient. FDA, EMA, MHRA, and WHO now emphasize that integrity must be built into the design of systems, processes, and behaviors that create data. This reflects a growing realization that most data issues are not intentional violations; they are symptoms of poorly constructed workflows, unclear responsibilities, uncontrolled systems, and performance pressures that make it difficult to record information truthfully and contemporaneously
- Modern life sciences operations generate vast quantities of data across manufacturing, laboratory testing, clinical research, medical device development, pharmacovigilance, and electronic quality systems. As organizations adopt automated instruments, cloud platforms, SaaS applications, and integrated manufacturing execution systems, the integrity risks evolve. These technologies improve efficiency, but they also increase dependency on system configuration, access control, audit trails, and secure data lifecycle management. A spreadsheet with unlocked formulas or an instrument without audit trails can undermine regulatory compliance just as seriously as falsified paperwork
- Regulators therefore expect companies to demonstrate proactive control-where technology, workflow design, leadership expectations, and organizational culture collectively prevent integrity failures before they occur