2020 483s on data integrity

Data integrity continued to be a focus of the FDA, though the reduced inspections definitely led to fewer 483s.

Reference NumberShort DescriptionLong Description2020 Frequency2019 Frequency2018 Frequency
21 CFR 211.194(a)Complete test data included in recordsLaboratory records do not include complete data derived from all tests, examinations and assay necessary to assure compliance with established specifications and standards.  Specifically, , ***153833
21 CFR 211.194(a)(4)Complete Test DataLaboratory records are deficient in that they do not include a complete record of all data obtained during testing.  Specifically, ***102428
21 CFR 211.68(b)Backup data not assured as exact and completeBackup data is not assured as [exact] [complete] [secure from alteration, erasure or loss] through keeping hard copy or alternate systems.  Specifically, ***6671
21 CFR 211.194(a)(4)Data secured in course of each testLaboratory records do not include a complete record of all data secured in the course of each test, including all [graphs] [charts] [spectra] from laboratory instrumentation, properly identified to show the [specific component] [drug product container] [closure] [in-process material] [lot tested] [drug product tested].  Specifically, ***4128
21 CFR 211.68(b)Written record not kept of program and validation dataA written record of the program along with appropriate validation data has not been maintained in situations where backup data is eliminated by computerization or other automated processes.  Specifically, ***1671
483s related to data integrity

Human Performance and Data Integrity

Gilbert’s Behavior Engineering Model (BEM) presents a concise way to consider both the environmental and the individual influences on a person’s behavior. The model suggests that a person’s environment supports impact to one’s behavior through information, instrumentation, and motivation. Examples include feedback, tools, and financial incentives (respectively), to name a few. The model also suggests that an individual’s behavior is influenced by their knowledge, capacity, and motives. Examples include training/education, physical or emotional limitations, and what drives them (respectively), to name a few. Let’s look at some further examples to better understand the variability of individual behavioral influences to see how they may negatively impact data integrity.

Kip Wolf “People: The Most Persistent Risk To Data Integrity

Good article in Pharmaceutical Online last week. It cannot be stated enough, and it is good that folks like Kip keep saying it — to understand data integrity we need to understand behavior — what people do and say — and realize it is a means to an end. It is very easy to focus on the behaviors which are observable acts that can be seen and heard by management and auditors and other stakeholders but what is more critical is to design systems to drive the behaviors we want. To recognize that behavior and its causes are extremely valuable as the signal for improvement efforts to anticipate, prevent, catch, or recover from errors.

By realizing that error-provoking aspects of design, procedures, processes, and human nature exist throughout our organizations. And people cannot perform better than the organization supporting them.

Design Consideration

Human Error Considerations

Manage Controls

Define the Scope of Work

·       Identify the critical steps

·       Consider the possible errors associated with each critical step and the likely consequences.

·       Ponder the "worst that could happen."

·       Consider the appropriate human performance tool(s) to use.

·       Identify other controls, contingencies, and relevant operating experience.

When tasks are identified and prioritized, and resources

are properly allocated (e.g., supervision, tools, equipment, work control, engineering support, training), human performance can flourish.


These organizational factors create a unique array of job-site conditions – a good work environment – that sets people up for success. Human error increases when expectations are not set, tasks are not clearly identified, and resources are not available to carry out the job.

The error precursors – conditions that provoke error – are reduced. This includes things such as:

·       Unexpected conditions

·       Workarounds

·       Departures from the routine

·       Unclear standards

·       Need to interpret requirements


Properly managing controls is

dependent on the elimination of error precursors that challenge the integrity of controls and allow human error to become consequential.

Apply proactive Risk Management

When risk is properly analyzed we can take appropriate action to mitigate the risks. Include the criteria in risk assessments:

·       Adverse environmental conditions (e.g. impact of gowning, noise, temperature, etc)

·       Unclear roles/responsibilities

·       Time pressures

·       High workload

·       Confusing displays or controls

Addressing risk through engineering and administrative controls are a cornerstone of a quality system.


Strong administrative and cultural controls can withstand human error. Controls are weakened when conditions are present that provoke error.


Eliminating error precursors

in the workplace reduces

the incidences of active errors.

Perform Work


Utilizing error reduction tools as part of all work. Examples include:

·       Self-checking

o   Questioning attitude

o   Stop when unsure

o   Effective communication

o   Procedure use and adherence

o   Peer-checking

o   Second-person verifications

o   Turnovers


Engineering Controls can often take the place of some of these, for example second-person verifications can be replaced by automation.

Appropriate process and tools in place to ensure that the organizational processes and values are in place to adequately support performance.

Because people err and make mistakes, it is all the more important that controls are implemented and properly maintained.

Feedback and Improvement


Continuous improvement is critical. Topics should include:

·       Surprises or unexpected outcomes.

·       Usability and quality of work documents

·       Knowledge and skill shortcomings

·       Minor errors during the activity

·       Unanticipated workplace conditions

·       Adequacy of tools and Resources

·       Quality of work planning/scheduling

·       Adequacy of supervision

Errors during work are inevitable. If we strive to understand and address even inconsequential acts we can strengthen controls and make future performance better.

Vulnerabilities with controls can be found and corrected when management decides it is important enough to devote resources to the effort


The fundamental aim of oversight is to improve resilience to significant events triggered by active errors in the workplace—that is, to minimize the severity of events.


Oversight controls provide opportunities to see what is happening, to identify specific vulnerabilities or performance gaps, to take action to address those vulnerabilities and performance gaps, and to verify that they have been resolved.


Risk Based Data Integrity Assessment

A quick overview. The risk-based approach will utilize three factors, data criticality, existing controls, and level of detection.

When assessing current controls, technical controls (properly implemented) are stronger than operational or organizational controls as they can eliminate the potential for data falsification or human error rather than simply reducing/detecting it. 

For criticality, it helps to build a table based on what the data is used for. For example:

For controls, use a table like the one below. Rank each column and then multiply the numbers together to get a final control ranking.  For example, if a process has Esign (1), no access control (3), and paper archival (2) then the control ranking would be 6 (1 x 3 x 2). 

Determine detectibility on the table below, rank each column and then multiply the numbers together to get a final detectability ranking. 

Another way to look at these scores:

Multiple above to determine a risk ranking and move ahead with mitigations. Mitigations should be to drive risk as low as possible, though the following table can be used to help determine priority.

Risk Rating Action Mitigation
>25 High Risk-Potential Impact to Patient Safety or Product Quality Mandatory
12-25 Moderate Risk-No Impact to Patient Safety or Product Quality but Potential Regulatory Risk Recommended
<12 Negligible DI Risk Not Required

In the case of long-term risk remediation actions, risk reducing short-term actions shall be implemented to reduce risk and provide an acceptable level of governance until the long-term remediation actions are completed.

Relevant site procedures (e.g., change control, validation policy) should outline the scope of additional testing through the change management process.

Reassessment of the system may be completed following the completion of remediation activities. The reassessment may be done at any time during the remediation process to document the impact of the remediation actions.

Once final remediation is complete, a reassessment of the equipment/system should be completed to demonstrate that the risk rating has been mitigated by the remediation actions taken. Think living risk assessment.

Data Process Mapping

In a presentation on practical applications of data integrity for laboratories at the March 2019 MHRA Laboratories Symposium held in London, UK, MHRA Lead GCP and GLP Inspector Jason Wakelin-Smith highlighted the important role data process mapping plays in understanding these challenges and moving down the DI pathway.

He pointed out that understanding of processes and systems, which data maps facilitate, is a key theme in MHRA’s GxP data integrity guidance, finalized in March of 2018. The guidance is intended to be broadly applicable across the regulated practices, but excluding the medical device arena, which is regulated in Europe by third-party notified bodies.

IPQ. MHRA Inspectors are Advocating Data Mapping as a Key First Step on the Data Integrity Pilgrimage

Data process maps look at the entire data life-cycle from creation through storage (covering key components of create, modify and delete) and include all operations with both paper and electronic records.   Data maps are cross-functional diagrams (swim-lanes) and have the following sections:

  • Prep/Input
  • Data Creation
  • Data Manipulation (include delete)
  • Data  Use
  • Data Storage

Use a standard symbol for paper record, computer data and process step.

For computer data denote (usually by color) the level of controls:

  • Fully aligned with Part 11 and Data Integrity guidances
  • Gaps in compliance but remediation plan in place (this includes places where paper is considered “true copy”
  • Not compliant, no remediation plan

Data operations are depicted utilizing arrows.  The following data operations are probably most common, and are recommended for consistency:

  • Data Entry – input of process, meta data (e.g. lot ID, operator)
  • Data Store – archival location
  • Data Copy – transcription from another system or paper, transfer of data from one system to another, printing (Indicate if it is a manual process).
  • Data Edit – calculations, processing, reviews, unit changes  (Indicate if it is a manual process)
  • Data Move – movement of paper or electronic records

Data operation arrows should denote (again by color) the current controls in place:

  • Technical Controls – Validated Automated Process
  • Operational Controls – Manual Process with Review/Verified/Witness Requirements
  • No Controls – Automated process that is not validated or Manual process with no Review/Verified/Witness Considerations
Example data map

The role of a data steward

With data integrity on everyone’s mind the last few years, the role of a data steward is being more and more discussed. Putting aside my amusement on the proliferation of stewards and champions across our quality systems, the idea of data stewards is a good one.

Data steward is someone from the business who handle master data. It is not an IT role, as a good data steward will truly be invested in how the data is being used, managed and groomed. The data steward is responsible and accountable for how data enters the system and ensure it adds value to the process.

The job revolves around, but is not limited to, the following questions:

  • Why is this particular data important to the organization?
  • How long should the particular records (data) be stored or kept?
  • Measurements to improve the quality of that analysis

Data stewards do this by providing:

  • Operational Oversight by overseeing the life cycle through defining and implementing policies and procedures for the day-to-day operational and administrative management of systems and data — including the intake, storage, processing, and transmission of data to internal and external systems. They are accountable to define and document data and terminology in a relevant glossary. This includes ensuring that each critical data element has a clear definition and is still in use.
  • Data quality, including evaluation and root cause analysis
  • Risk management, including retention, archival, and disposal requirements and ensuring compliance with internal policy and regulations.

With systems being made up of people, process and technology, the line between data steward and system owner is pretty vague. When a technology is linked to a single system or process it makes sense for them to be the same person (or team), for example a document management system. However, most technology platforms are across multiple systems or processes (for example an ERP or Quality Management System) and it is critical to look at the technology holistically as the data steward. I think we are all familiar with the problems that can be created by the same piece of data being treated differently between workflows in a technology platform.

As organizations evolve their data governance I think we will see the role of the data steward become more and more part of the standard quality toolbox, as the competencies are pretty similar.