## 1 Introduction  
Over the last few decades, the main ambition of the pharmaceutical industry has been to provide optimum quality products that adhere to ever-updated good manufacturing practice (GMP) regulations which need to be applied throughout all stages of drug manufacturing [1]. Data integrity, which refers to the accuracy and consistency of data over its lifecycle, is an essential part of GMP regulations, and an issue of paramount importance in any pharmaceutical industry quality system, which must ensure that medicines are of the required quality [2]. In particular, data integrity is critical in pharmaceutical manufacturing because it directly impacts patient safety and product quality. Pharmaceutical manufacturing processes are highly regulated, with strict guidelines set by regulatory agencies such as the FDA (Food and Drug Administration) [3] and the EMA (European Medicines Agency) [4]. These guidelines require that pharmaceutical production companies must ensure the accuracy, completeness, and consistency of their data, including production and quality control data. Ensuring data integrity is crucial for maintaining the quality of pharmaceutical products, as any errors or inconsistencies in data could result in drug product defects, contamination, or even harm to patients. For example, if production data are not properly recorded, it could lead to incorrect dosages or missing ingredients in a specific drug, which consequently could have serious consequences for patients taking this drug. Inaddition to patient safety, data integrity is also important for regulatory compliance. Regulatory agencies expect pharmaceutical companies to maintain accurate and complete product records for demonstrating that their products are safe, effective, and manufactured in compliance with applicable regulations. Ensuring data integrity requires a systematic combination of certain technical aspects, such as secure data storage and access controls, as well as organizational aspects, such as training policies and procedures. Furthermore, pharmaceutical production companies must conduct regular audits and reviews of their data to identify and resolve any problematic issues that may arise due to data integrity violations. To this purpose, the ALCOA+ principles have been defined by the U.S. Food and Drug Administration to provide accurate, complete, consistent, enduring, and available data. These data qualities are indispensable for data and metadata integrity in pharmaceutical manufacturing [2]. However, until recently [5], the ALCOA+ principles referred to non-quantifiable properties that data should possess and the evaluation of whether the data adhered to the ALCOA+ principles was a purely qualitative task. Various data processing and management methods have so far been proposed in aiming to ensure the observance of the ALCOA+ data integrity rules, in line with current pharmaceutical industry regulatory frameworks [6]. Regulatory frameworks such as ALCOA+ generally include well-defined structured rules, reflecting applicable laws and regulations, imposed by international bodies. Failure to comply with the respective regulations can be disastrous for pharmaceutical manufacturing, as it may cause long delays and increase costs. In this respect, achieving data integrity through satisfaction of the ALCOA+ requirements is a fundamental step towards ensuring compliance to the respective pharmaceutical regulations, which further results in more efficient processing and increased product quality. The ALCOA+ principles are the following [7]:  
1. Attributable: This means that all collected data should include information about the individuals who collected the data, the individuals who took action, and the time when the action was carried out. 2. Legible: This refers to the requirement for data to be accurate and comprehensible. However, legibility encompasses more than just reading the written content, it also involves understanding the surrounding context of the information. 3.