The FDA defines data integrity as “The completeness, consistency, and accuracy of data. Complete, consistent, and accurate data should be attributable, legible, contemporaneously recorded, original or a true copy, and accurate.” So the rules for good data integrity is through ALCOA, which stands for:
Attributable: you can trace the data to the person who recorded it.
Legible: it’s easy to read and understand.
Contemporaneous: data is recorded as things happen in near real-time, not later.
Original: it’s the first version of the data, not a copy.
Accurate: the data is correct and free from errors.
ALCOA+ extends these principles by emphasising on the attributes of being Complete, Consistent, Enduring and Available throughout the data life cycle.
Complete: data recorded including any additional entries.
Consistent: data maintained in consistent manner.
Enduring: data that's durable over a retention period.
Available: readily accessible for usage, review, audit and inspection.
These basic rules ensure that data stays clear, consistent, and trustworthy as part of good manufacturing practices.
cGMP (Current Good Manufacturing Practice) refers to regulations enforced by the FDA to ensure proper manufacturing processes and facilities for any product. These regulations emphasise quality control, including quality management systems, raw materials, operational procedures, and testing. cGMP allows flexibility for companies to apply modern technologies in product lifecycle management and approaches while meeting necessary quality standards. It ensures the products' identity, strength, and purity by preventing contamination and errors, and encourages continual improvement through up-to-date systems and innovative practices.
Incorrect data can create serious issues in any system. Erroneous information in the process could lead to selling unsafe products in the market. In the food industry, wrong data might lead to contamination or incorrect labelling. Incomplete ingredient's information could end up messing the system in the cosmetics formulation as well. Likewise, consumers in medical and pharmaceutical industries could thus be exposed to unsafe products, risking their health and wellbeing.
Incorrect data can lead to risking the health and wellbeing of people…
Having consistent data integrity practices means companies can easily prove that they’re following all the regulations. It ensures product safety, helps companies stay compliant with regulations, and keeps their brand reputation intact. It’s not just about avoiding mistakes; it’s about building trust amongst the customers and regulators.
The regulatory bodies expect companies to have accurate data. Several regulatory bodies have laid down strict guidelines for transparency, accuracy and compliance in record-keeping and quality control.
ISO 22000 has standards for food safety management systems, ISO 22716 for cosmetics and ISO 15378 for GMP in design, manufacture and supply of pharmaceutical packaging materials. All of these stress proper documentation, risk management and data integrity to prevent contamination and ensure product quality.
The FDA enforces data integrity in GMP to keep pharmaceutical and food, safe. Between 2019 and 2023 they conducted 56,913 inspections and issued 3,096 warning letters. So you know how important keeping accurate records and being compliant are.
WHO sets global guidelines for data integrity in GMP so medical products meet safety and efficacy standards. Companies must comply with WHO’s strict quality control requirements to stay approved.
The EMRN oversees pharmaceutical safety in Europe. In 2024 WHO designated the EMRN as a WHO Listed Authority (WLA) which means they meet global regulatory standards.
The EFSA safeguards food safety data across Europe. Also EFSA together with ECDC publishes annual One Health Summary Reports on zoonoses, antimicrobial resistance and foodborne outbreaks to show its commitment to high data integrity in food safety evaluations.
From 2019 to 2023, the FDA conducted 56,913 inspections and issued a total of 3,096 warning letters.
In India, quality control organisations (e.g., Food Safety and Standards Authority of India (FSSAI) are in charge to ensure that information about the product in respect of quality and integrity is of good quality. Hundreds of food safety inspections are done by both state and central regulators every day, notably in urban areas. Non-compliance can have more serious outcomes, such as the product recall and loss of consumer approval. One of the most evident examples of these spillover effects is the renowned instant noodle brand case in 2015.
Legacy systems: some companies still use legacy systems, outdated ways of tracking data, which are rigid, restrictive, and lack effectiveness in relation to new technologies and processes.
People & Process: overly reliant on manual human practices, lack of training in handling data & systems, incoherent processes - all of which leads to inappropriate and inadequate control of data flow.
Data Governance: failure to ensure that data is complete, consistent, and enduring throughout the lifecycle will result in data quality issues.
Data Security: lack of policy and enforcement of users data access and privileges based on the function and responsibilities of the people. Additional risk of cyber threats, hacking or unauthorised changes.
Here are some simple ways companies can keep their data on track:
Use ALCOA principles: This is a simple framework that helps make sure data is recorded properly.
Modernise systems: New age softwares can help automate, track and store data securely.
Regular audits: It’s important to check your records often to make sure everything is correct.
Train employees: Make sure everyone understands why data matters and how to handle it.
Secure your data: Protect both physical and digital records to prevent tampering.
Leaders have a meaningful role to play in effective data handling. They should ensure that employees are well-equipped with cutting edge systems, tools and training to do their work. Foresight into technological landscape, re-platforming the current systems, tech enabled processes ensures that new and innovative technologies are applied.
Outsourcing of activities and responsibilities of each party in new product initiatives should clearly emphasise data integrity requirements at all stages. Ultimately, companies are responsible for the integrity of data provided to them by outsourced contracts.
When leaders emphasise data integrity, everyone understands and appreciates its importance. There exists a strong agreement that good data practices should be driven from the top and permeate all departments. Data custodians will also have to check the systems for updates and perform regular audits.
The leaders must ensure that their data systems are fit during regular checking to see that nothing goes haywire.
Blockchain allows companies to create records that can’t be changed, which makes it easier to track data safely.
AI enables easy and faster identification of mistakes and inconsistencies with its analytical capabilities , hence making it easier to fix them before they become serious issues in research work.
Cloud based Product Lifecycle Management (PLM) systems will enable data integrity and accessibility in new product initiatives at the early and mid-stage of a product development.
Such technologies, however, can never completely replace the basics of data integrity. In addition to these technologies, companies still need to follow reliable practices and processes for data to be used in applications.
Thus, data integrity is a necessity for the safety and quality assurance of the product. By following ALCOA, investing in modern technologies, training employees, and strengthening systems, organisations can maintain the highest integrity of data. New technology will continue to advance; however, the essence of data integrity will always remain in keeping precise, accurate, and trustworthy information!