DATA INTEGRITY
Data integrity: the center of attention
DATA INTEGRITY
Data integrity: the center of attention
DATA INTEGRITY
Data integrity: the center of attention
In the era of Big Data, where digitalization and industrie 4.0 significantly increase the production of data, there are more and more companies who want to control the way they produce, collect, distribute, share and analyze their critical data. First and foremost is the pharmaceutical industry which needs data control due to the severe constraints issued by regulatory bodies such as the FDA, EMA, MHRA, and ANSM, etc.
As data is considered « fuel » for a company with a 4.0 vision of industry, it adds value. The problem of data integrity is therefore becoming a sensitive subject involving, at the highest level, the organization of companies. This leads to an extensive readjustment of the Quality Systems and so puts data at the heart of the debate.
ALCOA and the data life-cycle
The general concept supporting the principles which affect data integrity is today well-known because of the acronym ALCOA (Attributable, Legible, Contemporaneous, Original, Accurate). Each data, which is itself made up of a collection of coherent and indissociable information (raw data, metadata and associated audit trails, etc.), must therefore address the ALCOA requirements i.e. to be attributable (clearly identified author and source(s)), legible (can be visualised over a long period), contemporary (recorded at the same moment as the connection), original (coming from the first recording or a certified copy) and reliable (conforms to a realistic situation and is processed correctly).
ALCOA and the data life-cycle
The general concept supporting the principles which affect data integrity is today well-known because of the acronym ALCOA (Attributable, Legible, Contemporaneous, Original, Accurate). Each data, which is itself made up of a collection of coherent and indissociable information (raw data, metadata and associated audit trails, etc.), must therefore address the ALCOA requirements i.e. to be attributable (clearly identified author and source(s)), legible (can be visualised over a long period), contemporary (recorded at the same moment as the connection), original (coming from the first recording or a certified copy) and reliable (conforms to a realistic situation and is processed correctly).
ALCOA and the data life-cycle
The general concept supporting the principles which affect data integrity is today well-known because of the acronym ALCOA (Attributable, Legible, Contemporaneous, Original, Accurate). Each data, which is itself made up of a collection of coherent and indissociable information (raw data, metadata and associated audit trails, etc.), must therefore address the ALCOA requirements i.e. to be attributable (clearly identified author and source(s)), legible (can be visualised over a long period), contemporary (recorded at the same moment as the connection), original (coming from the first recording or a certified copy) and reliable (conforms to a realistic situation and is processed correctly).
As indicated by its name, data integrity is associated with the data and not just with the application or system which generated them. It is certain that the latter application or system must always be validated but it is the data life-cycle which is at the heart of data integrity: creation, calculation, reporting, verification, assistance in decision making, conservation and deletion, etc. These data can be transferred from one application to another which is thus very important for data integrity.
According to the above principles, we expect to see and be shown that there is no data alteration or change in data accessibility. It is therefore clear that these expectations lead to the existence of secure systems to generate data, archive or even restore them. The vulnerability criteria and the risk management associated with the data life-cycle will have an impact on the systems as much as the organization.
Data integrity and controlling the quality
Data integrity is a major requirement in system quality. This approach requires management which is in phase with the sensitivity of the subject, i.e. management which acts on the behavior of the people involved with respect to the problems of data integrity, which accompany the human means (data management, reviews, etc.) and technical means (ensuring the security of the different environments, etc.) and which raise global awareness with respect to the risks involved. There is therefore the need for a policy for all the personnel involved. Management sponsoring, training of the employees concerned, periodic reviews, and corrective and preventive actions will be implemented within a real company-wide project.
Another aspect of quality with respect to the problem of data integrity is risk management. Risk anticipation and mitigation play a key role in data integrity. By depending notably on the principles of Data Flow Charts, this risk management addresses the data exploitation conditions in the firm, all along the whole life-cycle of the said data from creation to archiving or deletion. The business processes which support this life cycle must not be forgotten.
To sum-up, quality and risk management and respect of the regulations therefore constitute the three principal bases on which a good command of data integrity must be founded.
Data integrity and controlling the quality
Data integrity is a major requirement in system quality. This approach requires management which is in phase with the sensitivity of the subject, i.e. management which acts on the behavior of the people involved with respect to the problems of data integrity, which accompany the human means (data management, reviews, etc.) and technical means (ensuring the security of the different environments, etc.) and which raise global awareness with respect to the risks involved. There is therefore the need for a policy for all the personnel involved. Management sponsoring, training of the employees concerned, periodic reviews, and corrective and preventive actions will be implemented within a real company-wide project.
Another aspect of quality with respect to the problem of data integrity is risk management. Risk anticipation and mitigation play a key role in data integrity. By depending notably on the principles of Data Flow Charts, this risk management addresses the data exploitation conditions in the firm, all along the whole life-cycle of the said data from creation to archiving or deletion. The business processes which support this life cycle must not be forgotten.
To sum-up, quality and risk management and respect of the regulations therefore constitute the three principal bases on which a good command of data integrity must be founded.
Data integrity and controlling the quality
Data integrity is a major requirement in system quality. This approach requires management which is in phase with the sensitivity of the subject, i.e. management which acts on the behavior of the people involved with respect to the problems of data integrity, which accompany the human means (data management, reviews, etc.) and technical means (ensuring the security of the different environments, etc.) and which raise global awareness with respect to the risks involved. There is therefore the need for a policy for all the personnel involved. Management sponsoring, training of the employees concerned, periodic reviews, and corrective and preventive actions will be implemented within a real company-wide project.
Another aspect of quality with respect to the problem of data integrity is risk management. Risk anticipation and mitigation play a key role in data integrity. By depending notably on the principles of Data Flow Charts, this risk management addresses the data exploitation conditions in the firm, all along the whole life-cycle of the said data from creation to archiving or deletion. The business processes which support this life cycle must not be forgotten.
To sum-up, quality and risk management and respect of the regulations therefore constitute the three principal bases on which a good command of data integrity must be founded.
Data integrity: what challenges for industry?
Over the last few years, the authorities have sent out a number of warning letters indicating the problem of data integrity. For example, 75% of those sent by the FDA in 2018 addressed this problem and highlighted the occasional but serious shortcomings such as the non-respect of ALCOA rules, lack of traceability, suppressed data, and even data falsification in some cases.
The above issues directly affect the image and performance of the companies concerned (product recall, loss of customer confidence, etc.). Beyond that, they may also involve the safety and quality of products (notably medicines and medical devices) and therefore, the health of patients or more widely the consumers. These observations are signs of the challenges faced by regulated industries which are either already affected by the problem of data integrity or who will be in the near future if they take into account ever-stricter legislation.
Data integrity: what challenges for industry?
Over the last few years, the authorities have sent out a number of warning letters indicating the problem of data integrity. For example, 75% of those sent by the FDA in 2018 addressed this problem and highlighted the occasional but serious shortcomings such as the non-respect of ALCOA rules, lack of traceability, suppressed data, and even data falsification in some cases.
The above issues directly affect the image and performance of the companies concerned (product recall, loss of customer confidence, etc.). Beyond that, they may also involve the safety and quality of products (notably medicines and medical devices) and therefore, the health of patients or more widely the consumers. These observations are signs of the challenges faced by regulated industries which are either already affected by the problem of data integrity or who will be in the near future if they take into account ever-stricter legislation.
Data integrity: what challenges for industry?
Over the last few years, the authorities have sent out a number of warning letters indicating the problem of data integrity. For example, 75% of those sent by the FDA in 2018 addressed this problem and highlighted the occasional but serious shortcomings such as the non-respect of ALCOA rules, lack of traceability, suppressed data, and even data falsification in some cases.
The above issues directly affect the image and performance of the companies concerned (product recall, loss of customer confidence, etc.). Beyond that, they may also involve the safety and quality of products (notably medicines and medical devices) and therefore, the health of patients or more widely the consumers. These observations are signs of the challenges faced by regulated industries which are either already affected by the problem of data integrity or who will be in the near future if they take into account ever-stricter legislation.
However, due to our significant presence in regulated industries over the last 30 years, our Group can help you control your data integrity. Leverage the versatility of our consultants between Quality approach, regulatory skills, business skills and expertise in industrial information systems. They will be able to accompany you in your project (partially or totally depending on your needs) with the setting up a complete management policy for data integrity within your company.
Our priority?
The success of your digitalization project, from the needs analysis to the definition of the project right through to the implementation of solutions to enable continued operation.
Do you have a problem with data integrity? Do you want an objective evaluation and be accompanied by experts in this domain?
