Your Oversight Responsibility for Data Integrity and Quality of Research


Doctors, physicians, and patients alike expect any drug that makes it to a shelf has complied with a rigorous regulatory path. However, beyond any regulatory responsivity, data integrity is not a checklist itself, rather, it’s a way of life in the lab. There are certain steps you can take to ensure a smooth workflow to honest, accurate, and reliable data.

Success in any industry stems from having quality standards and regulations in place and adhering to them routinely in all aspects of the work. The same goes for the world of pharma; doctors, physicians, and patients alike expect any drug that makes it to a shelf has complied with a rigorous regulatory path. The FDA has thoroughly reviewed the quality and accuracy of the data that supports the safety and efficacy of the drug being marketed. This all begins with a solid data integrity program.

What defines data integrity?

Data integrity is not a checklist itself, rather, it’s a way of life in the lab. That said, there are certain steps you can take to ensure a smooth workflow to honest, accurate, and reliable data.

At its core, data integrity is a process that uses policies, procedures, audit trails, and other documented and tested controls and regulatory factors to establish a foundation in the laboratory so that the information being put forth from the facility itself, scientists, and inspectors can be trusted. It solidifies the backbone of reliability of the data created in the lab.

The U.S. Food and Drug Administration defines data integrity as the completeness, consistency, and accuracy of all data.1

  • Complete: Including all aspects and details of data; the good and the bad.
  • Consistent: Instrument serial number, time stamps, tech name, and other contributing factors need to be consistent in how data are reported.
  • Accurate: All information or data should always be authentic in presentation.

All of this data should be attributable, legible, contemporaneously recorded, in its original copy, and accurate, also known as ALCOA.

Checklist: How to ensure data integrity and quality of research

A thorough checklist of process and procedures prioritizes the achievement of data integrity. Regulatory agencies that inspect the laboratory premises want to feel confident that any piece of data can be trusted to make a reliable conclusion about certain aspects of the lab or the product being produced. Establishing a checklist allows laboratories to create that strong foundation in the early stages of drug discovery and meet the expectations of the general public in the end result. To achieve a strong data integrity governance program, consider the following:

Remember ALCOA in data documentation
The data life cycle includes the creation, modification, processing, maintenance, archival, retrieval, transmission, and disposition of all information. In the industry, the acronym known as ALCOA describes data integrity as a whole, and establishes the standards to which the data life cycle should be held. ALCOA stands for:

  • Attributable: Who is performing during each stage of the data life cycle? All data should show documentation of the analyst, instrument, etc. to ensure data is reliable.
  • Legible: Is it easy to read and understand the data? It should be legible when written in a lab notebook; advisable to consider more digital technology choices instead.
  • Contemporaneous: Lab staff are required to record everything the moment it happens. For example, as soon as the temperature is checked, it needs to be recorded immediately. Waiting to document vital information could lead to misstep or human error.
  • Original: All data must be clear and concise to its original form; anything that comes off the instrument should not be mocked up or altered in any form.
  • Accurate: All information presented in the lab must always be authentic, never false.

The ALCOA acronym itself represents the interrogative questions of the laboratory; the who, what, where, when, and why used to analyze the audit trail.

Put access controls in place
Access controls are a critical component to protecting data integrity. Managing who is allowed to operate instrumentation or enter designated parts of lab space based on their prominence and level of training within the facility can be taken care of with a keycard or a unique login in with a secure password. After putting such access controls to work, they need to be tested to ensure the management system flows and meets requirements as it was designed. All access controls should routinely be updated for new additions to the personnel list or removal of those no longer in a position to access the environment or equipment.

ALCOA comes into play here as well; keeping data attributable to a particular scientist or instrument or lab space allows the audit trail to have information that may be needed for recreation of events. Additional controls should be considered to ensure there are never accidental modifications to or deletions of necessary information without proper access privileges.

Follow new FDA guidelines
The FDA has been working on administering rules and regulations on data integrity since the 2000s, but has focused on the subject in the last five or six years. (Figure 1)

Figure 1

The Data Integrity Compliance with Drug CGMP Questions and Answers, the guidance document issued in

December 2018 by the FDA, focuses on drugs, biologics, and positron emission tomography drugs.2 The guidance is made up of 18 questions covering data integrity and governance, with an end goal to ingrain quality culture and organizational value at the executive level that trickles down into the rest of the system. Topics addressed in the questions include:

  • How data integrity, metadata and audit trails apply to GMP records.
  • How GMP workflows are validated.
  • How electronic copies can be accurate representations of paper notes.

The full list of 18 questions provides details and examples of how firms can apply the guidance in the laboratory.3

Make audit trails necessary
The audit trail is defined as the secure, electronic record that details the course of action taken in the creation, modification, and deletion of data.4 This adds another element of trust and reliability that the laboratory has controls in place. The audit trail paints the picture of how data is generated. It’s necessary to achieve a robust data integrity program. This significant component includes details such as the user name of the scientists who managed the data, the date and time in which the operations occurred, the integration parameters used, processing details and any other specifics that can assist during data justification. In addition, the audit trail will document any changes to the data (intentional or otherwise), who made the change, an explanation of why the change was made and the original value being kept as a part of the trail.


Good business practices must be put in place to understand the importance of data integrity and quality research. When the CEO understands the importance of what enters and leaves the lab, it may be more distinct for the rest of the organization. In 2018, there were 95 warning letters issued by the FDA - 54 of which involved a component of data integrity. The FDA’s focus on data integrity will resonate at a global level. If more companies emphasize regulatory processes and policies as fundamental, quality and integrity in regulated environments becomes second nature.