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The logistics sector’s migration to cloud computing has revolutionized the way data is stored and managed. With data lakes and integrated systems, the challenge of data silos is largely addressed. However, ensuring the integrity of this data is now more critical than ever, especially when considering the environmental, social, and governance (ESG) implications and the security landscape.  Data Integrity as a Driver for Sustainability and ESG Initiatives  In a sector where environmental impact and sustainability are increasingly under the spotlight, data integrity ensures that the reported metrics on fuel usage, emissions, and resource utilization are accurate and reliable. This transparency allows logistics companies to make informed decisions that align with their ESG goals, such as:  Additional Regulatory Compliance  Regulatory frameworks like the General Data Protection Regulation (GDPR) are catalyzing the adoption of sound data practices. These regulations mandate stringent data handling procedures, pushing companies towards adopting data integrity measures. A few examples:  The Shift from Current Data Management and Security The current data management approach in many organizations can be reactive and fragmented, leading to potential risks like data breaches or non-compliance with regulations. Data integrity emphasizes a proactive, holistic approach to data management, ensuring its security, privacy, and reliability from the outset.  Data integrity is not simply a technical requirement in the cloud-era logistics sector; it’s a strategic asset that empowers organizations to uphold sustainability, fortify security, and derive the most accurate insights from AI technologies. It’s a commitment to excellence that resonates with every stakeholder and is a vital component of the industry’s future.  The Role of Data Integrity in AI and Machine Learning  The reliability of artificial intelligence outputs in logistics, from optimizing delivery routes to forecasting demand, is contingent upon the integrity of the underlying data. Immutable and accurate data becomes the fuel for AI algorithms, enabling them to provide more precise and actionable insights.  Use Case: Tracking Enhancement   In one of our projects at Armilis, we empowered an energy company to improve their high-valued biomass transport. By enhancing GPS with blockchain technology, we enabled the creation of immutable and traceable data, which was crucial in spotting illegal stops and suspicious behavior.  Actionable Steps Towards Data Integrity  For logistics companies leveraging cloud capabilities, fortifying data integrity involves:  Incorporating these technologies into the data management strategy not only fortifies the data against external threats but also enhances internal confidence in the data’s reliability, which is essential for making informed decisions in the logistics industry.  Explore further on how data integrity can transform your logistics operations by visiting Industries – Armilis. 

Starting in January 2024, substantial enterprises within the European Union will be required to disclose data regarding their environmental, social, and sustainability impacts. This new mandate comes as the Corporate Sustainability Reporting Directive (CSRD) received approval from the members of the European Parliament in November 2022 with 525 votes in favor, 60 against, and 28 abstentions. These newly agreed-upon regulations will mandate companies to routinely make public data concerning their societal and ecological impacts. As a result, greenwashing could be minimized, the social market economy within the EU may be strengthened, and a foundation for worldwide sustainability transparency standards might be established. The regulations will be enforced in stages from 2024 to 2028 as follows: by the beginning of 2024 for large public interest corporations (having over 500 employees) that are already subject to the Non-Financial Reporting Directive, with data publication deadline in 2025; by the start of 2025 for large corporations not yet under the Non-Financial Reporting Directive (with over 250 employees or having a turnover of €40 million or total assets of €20 million), with a deadline in 2026; and by January 1, 2026 for SMEs and other listed corporations, with a deadline in 2027. SMEs can opt-out of participation until 2028. New EU sustainability standards The standards rectify the weaknesses in the existing Non-Financial Reporting Directive (NFRD) legislation, considered largely inadequate. NFRD introduces stricter transparency requirements on companies’ environmental impacts, human rights, and societal standards, based on criteria aligned with EU climate targets. The first set of such standards is projected to be adopted by the Commission by June 2023. To ensure reliability, the provided data will be independently monitored and certified, with the sustainability report being on par with the financial report. This will enable investors to have access to dependable and comparable data. Moreover, companies must guarantee digital access to this sustainability information. With these requirements, data integrity becomes and central point of interest for companies. The ability to prove transparency and accountability in business processes will result in better economical returns as well as higher market reputation, leading to new business opportunities. Extension of application criteria The revised EU sustainability transparency mandates will apply to all large corporations, publicly traded or not, including non-EU corporations that have a turnover exceeding €150 million within the EU. Listed SMEs will also be included, but will have an extended time for adaptation. The new mandate increases the number of companies required to disclose sustainability information from about 11,700 to nearly 50,000. “Europe is showing the world that it really is possible to ensure that finance, in the strict sense of the word, does not rule the entire global economy” said rapporteur Pascal Durand (Renew, FR) during the plenary debate. Compliance creating new opportunities These new requirements represent far more than just another obligation for companies. In fact, sustainability reports can offer an opportunity for companies to enhance their performance in areas that are not financial but have social impact, engage with new stakeholders, and create long-term value by aligning the company with a more socially and environmentally sustainable economy, with positive implications for the capital market.

Data integrity plays a pivotal role in building trust and making informed decisions. Blockchain ensures data integrity and offers unprecedented levels of security. This article delves into the importance of blockchain in maintaining data integrity and explores the inherent relationship between blockchain and data security. SECURING DATA INTEGRITY THROUGH BLOCKCHAIN Blockchain technology provides a robust framework for safeguarding data integrity in various industries. Its decentralized nature and distributed consensus mechanism ensure that each transaction undergoes validation and verification by a network of nodes. This decentralized validation prevents unauthorized manipulation of data, making it highly reliable and tamper-resistant. This feature is particularly valuable when dealing with sensitive data, such as financial or commercial records, where the integrity of information is paramount. THE ROLE OF BLOCKCHAIN IN DATA SECURITY The relationship between blockchain and data integrity is deeply intertwined. Blockchain enhances data security and integrity by creating an inclusive environment where everyone can add and verify data. Transactions within a blockchain are logged and timestamped, making it impossible for anyone to duplicate or tamper with the same transaction. This characteristic ensures the maintenance of an auditable and immutable record of all transactions ever conducted on the blockchain. The historical integrity of this information perfectly aligns with the core principles of data integrity. BENEFITS OF IMMUTABILITY The immutability of data on the blockchain is a pivotal aspect that reinforces the concept of data integrity. Any alteration or manipulation of data would require the consensus of the majority of the network participants, making it virtually impossible to compromise the integrity of stored information. This characteristic establishes a solid foundation for businesses and individuals alike to rely on accurate and unaltered data for critical decision-making processes. Blockchain technology has emerged as a game-changer. Its decentralized structure, advanced cryptographic techniques, and immutability of data provide a secure and trustworthy platform for storing sensitive information. By leveraging blockchain, organizations can enhance data integrity, minimize the risk of unauthorized manipulation, and establish an auditable record of transactions.

Successful implementation of this new technology requires an immense amount of data, but to manage it properly, it is critical to ensure that it is reliable, accurate, secure and trustworthy. ENSURING TRUSTED DATA Trust in data comes from the ability to demonstrate with complete confidence how it has been prepared, trace the provenance of the data back to its origin, and provide rights management and auditability. This involves ensuring that the data is accurate, consistent and contextually relevant. Without strong data integrity, companies run the risk of making decisions based on faulty information, which can have disastrous consequences for their business performance. IMPLICATIONS FOR ML AND IA MODELS Data integrity takes on even greater importance when it comes to ML and AI machine learning models. If these models are trained on datasets that lack integrity, they will not achieve their intended results and may even generate incorrect and faulty predictions, which would negatively affect business outcomes. A holistic approach to data integrity includes accuracy, consistency and context. When data is accurate and consistent, a company can build a long-term competitive advantage over its competitors. BENEFITS OF DATA INTEGRITY Data integrity offers a number of key benefits for companies looking to take full advantage of AI and ML. First, it ensures the quality and reliability of the data used in models, resulting in more accurate predictions and more informed business decisions. In addition, it enables organizations to track the origin of data and perform audits, which is crucial in regulated environments or when it comes to data privacy and security. Data integrity is a critical component to business success in the age of AI. Companies that prioritize the integrity of their data are better positioned to leverage the full potential of AI and ML, getting more accurate results and making more informed decisions.

The IoT is a fundamental piece to convert real world conditions into data, allowing companies to make better, more informed decisions. As the IoT gains maturity, we’re seeing its impact across many industries and use cases.   In agriculture, smart sensors are in widespread use for real-time crop monitoring to help farmers track the state of their products.  Security cameras, alarm systems and smart locks are strengthening physical security and bringing residents peace of mind.  Manufacturers, pharmaceutical firms and utilities are increasingly switching to remote infrastructure monitoring. To manage and repair their assets without extended downtime and costly travel, these industries have begun connecting previously disconnected machines.  What do these diverse use cases have in common? Without trusted data from connected sensors and devices, all of them become vulnerable and the decisions they support become questionable. Establishing data trust should be top of mind for IoT device manufacturers, and it is imperative that they take steps to ensure that data is not being manipulated in transit or at rest. This protection applies not only to data packets exchanged by the devices and applications but firmware updates and supply chain processes.  To achieve this, one model that has been in use for a long time is that of the CIA triad. The key components of this model (see Figure 1) are based around confidentially, integrity and availability.  In the context of IoT, confidentiality caters for protecting privacy of IoT devices, integrity looks after the data contained within the device while availability covers accessibility of the device.   Focusing on integrity, it is helpful to consider three different states that data can exist, namely in motion, at rest and in process.   Any breach of data integrity will mean that an IoT device cannot operate correctly but it also potentially exposes the device to being exploited and become a compromised platform from which other attacks can be launched. The usual method of verifying the integrity of data is by a mathematical algorithm called a hash, of which the secure hash algorithms (SHA) is most popular.   Data-in-motion requires that data be protected from modification while on its journey from sensor to cloud application. While a hash technique can be used an attacker could make a change to the message and recalculate the hash. A stronger approach is by using a data integrity check with a shared private key as illustrated in Figure 3. This is called a keyed-hash message authentication code (HMAC), and since it needs a shared private key, it must be protected just like any other cryptographic key.  When it comes to data-at-rest there are several considerations. Firstly, the stored program data will need to be verified and that will be done at boot time, see secure boot in the next section of this article. Configuration data and any stored device data should always be verified prior to being processed by an IoT device. Periodic integrity checks can be made during operation and always at start-up and shutdown.  The positive business outcomes enabled by the IoT all hinge on working with accurate, timely and trusted data. With this, Artificial Intelligence (AI) systems and Advance Analytics applications can provide reliable results based on that data, thus leading to positive operational decisions and results.  Data integrity can bring trust to data by:  Ensuring its origin and providing a secure and private access through cryptography.  Enable a reliable data stream verification thanks to blockchain technology. Provide a more robust chain-of-trust, that is, taking the trust of the system as close as possible to the source of data, guaranteeing its authenticity across networks and servers without the need to rely on strong network security. 

In today’s data-driven world, data integrity has become essential for the proper functioning of businesses and for informed decision making. This is especially true in the Industry 4.0 sector, where data is at the core of its operations, driven mainly by digitization and the proliferation of technologies such as Internet of Things (IoT) and Artificial Intelligence (AI). By implementing data integrity techniques and methodologies, companies can significantly improve their data governance and protect against security breaches, which will make their businesses more efficient and reliable, allowing them to take better operational decisions, reduce risks, and leverage the advantages of Industry 4.0.  In this post, we will explore the importance of data integrity to Industry 4.0, and discuss its advantages, enabling technologies and how it relates to industry regulations and standards, such as GDPR or ISO 27001. WHAT IS DATA INTEGRITY?  Data integrity refers to the completeness, consistency, accuracy, and authenticity of data throughout its lifecycle. With the proliferation of IoT devices, big data, analytics and AI, data integrity has become an increasingly critical issue.  Data integrity is a prerequisite for data quality and plays a critical role in improving decision-making and operational efficiency. While data quality takes care of providing relevant information, data integrity ensures its transparency and authenticity.   Data integrity also contributes to more robust data security. While the goal of data security is to protect information from outside attacks, data integrity is concerned with keeping information intact and accurate for the entirety of its existence, avoiding unwanted manipulations or highlighting the existence of errors.  Since data security and data quality are essential to a successful digital transformation effort, the implementation of data integrity is crucial in any industry. To know more about this read our blog “What is data integrity and why is it important”. WHY DATA INTEGRITY IS IMPORTANT IN INDUSTRY 4.0  HARMONIZATION OF DECENTRALIZED DATA FLOWS Digitization is a catalyst for change. For many industries, digital transformation brings the massive productivity, resilience and sustainability benefits. Companies and industries are implementing multiple solutions and technologies to advance to Industry 4.0, like installation of multiple IoT devices to monitor the real world or applications based on AI to perform predictive maintenance or process optimization. With the proliferation of various applications creating and using vast amount of data, maintaining the integrity of information across interconnected systems has become a major challenge. This has led to the creation of decentralized data silos that must communicate and exchange information effectively.  While the traditional approach to solving this issue is through the qualification of individual systems, this often overlooks the overall context and fails to manage data properly throughout the end-to-end process. To guarantee secure and reliable intercommunication among systems and applications, it is essential to maintain the integrity of data. By doing so, companies can focus on processes and ensure that facilities mature in relation to recorded information.  Ultimately, processes and records (along with their data) must be the starting point for engineering and qualification of any system. ASSURANCE AND COMPLIANCE Ensuring data integrity is an essential part of the quality assurance procedure, which enables companies to maintain product quality, efficacy, and safety. The pharmaceutical sector, for example, relies on data integrity standards such as the FDA and ALCOA+ guidelines to assess and mitigate data integrity risks and avoid regulatory product rejections. You can learn more about data integrity for Pharma in our post Data Integrity in Pharma.  Apart from complying with industry-specific regulations, companies must also pay attention to general data protection regulations such as GDPR. By maintaining data integrity, companies can prevent the misuse of personal data.  Cybersecurity is another crucial area that companies need to address to ensure data integrity. Regulations such as NIST SP 800-171 establish security requirements for U.S. government contractors to protect unclassified information. Under this regulation, data integrity involves guarding against improper information modification or destruction, and ensuring information non-repudiation and authenticity.  Data integrity also support the compliance of standards such as ISO27001 (Information Security) or ISO27037 (Digital evidence). ENABLING TECHNOLOGIES TO ACHIEVE DATA INTEGRITY To ensure data integrity, it is crucial to leverage the right technologies. Blockchain technology is an essential tool in this regard, providing secure and immutable records of process operations and product information. This results in reliable data validation and verification, preventing the collection and processing of fraudulent data. This, in turn, maximizes efficiency and process trustworthiness.  Cryptography is another critical technology that can help secure data against unwanted access. It reinforces the consistency of information and reduces the possibility of data breaches.  By combining the power of blockchain and cryptography, data can be guaranteed to be trusted, authentic, and accurate. This leads to improved decision-making, increased operational efficiency, and reduced risk of security breaches. SUMMARY The success of Industry 4.0 in any business relies heavily on data integrity. Precise, trusted, and secure data is crucial for enhancing operational efficiency and making intelligent decisions. To ensure data integrity, companies can incorporate advanced technologies such as cryptography and blockchain solutions, which guarantee the immutability of the collected data. This in turn, helps to comply with some regulations such as GDPR, NIST SP 800-171 or standards like ISO 27001, to avoid penalties and fines. In this aspect, data integrity plays a pivotal role in generating reliable audit trails by enhancing data transparency and authenticity.  By implementing data integrity solutions, companies can fully capitalize on the benefits of Industry 4.0. They can mitigate the risk of data breaches, enhance decision-making, augment operational efficiency, and gain a competitive edge.  3 important steps to ensure data integrity:  ABOUT US Armilis is a leading deep tech company that specializes in data integrity, providing solutions to ensure the consistency, authenticity, and accuracy of data throughout its life cycle.  Our software layer is based on cryptography and blockchain, enabling seamless integration with any system, resulting in enhanced trust, efficiency, and security for existing or new digital infrastructures.  We cater to industry 4.0 and the SMART world, where data is vital to daily operations, focusing on delivering unparalleled

The importance of data integrity in the pharmaceutical industry is a topic that has received increasing attention in recent years.  Data integrity is an important aspect of current Good Manufacturing Practice (cGMP) compliance, according to a draft guidance issued by the U.S. Food and Drug Administration (FDA). In addition, the European Medicines Agency (EMA) and the UK’s Medical and Healthcare Products Regulatory Agency (MHRA) also emphasize the importance of data integrity in their publications. Although these guidelines are not “non-binding recommendations”, data integrity is often an important issue during pharmaceutical audits and inspections. In this article, we will examine in detail the importance of data integrity and how pharmaceutical companies can ensure compliance with these regulations.  WHAT IS DATA INTEGRITY? Data integrity refers to the completeness, consistency and accuracy of the data. It is important that the data is Attributable, Legible, Contemporaneously Recorded, Original or a True Copy and Accurate (ALCOA). All of these attributes ensure transparency and reliability of data within companies and towards third parties, such as regulatory and compliance entities. In the pharmaceutical industry, compliance with these regulations is crucial to ensure patient safety and drug efficacy. IMPORTANCE OF DATA ACCURACY IN THE PHARMA SECTOR Digitization has brought several advantages to pharmaceutical processes, including the ability to search data faster, make backups, and bring together data from different sources to provide new insights with advanced analytics or artificial intelligence. However, as the amount of data increases exponentially, the risk of integrity also increases. Human error is more likely when entering data into electronic systems, access breaches can occur if robust permissions methods are not in place, and automation of data collection through IoT / IIoT can increase the amount of erroneous data. As many documents are emailed, downloaded, uploaded and shared, and data is used multiple times to run simulations or scenarios, it becomes difficult to determine which ones are the originals. This new paradigm of digitization requires an innovative approach to data integrity in the pharmaceutical industry. COMPLIANCE WITH DATA INTEGRITY REGULATIONS Data integrity is crucial for regulatory compliance in the pharmaceutical industry. Regulatory agencies and pharmaceutical companies need accurate and reliable data to ensure the safety, efficacy and quality of a product. Current regulations, such as ALCOA+ and guidelines issued by the EMA, MHRA and FDA, highlight the importance of data integrity and set out the requirements needed to ensure it. Data integrity is also essential to build a relationship of trust between the pharmaceutical company and the regulatory agencies. Agencies cannot evaluate every detail of processes and production, so they must rely on the integrity of the data provided by companies. In case of non-compliance, the consequences can be serious, such as product recalls, company image problems and even harm to patients.  For example, the ALCOA+ data attributability requirement requires that all data collected be attributed to the person or team that generated it. An example of a data attributability failure was highlighted in FDA warning letter 320-20-10. The letter indicates that the company failed to exercise appropriate controls over computer systems to ensure that only authorized personnel can make changes to master production and control records or other records. Pharmaceutical companies must implement a data integrity management system to ensure regulatory compliance and avoid potential regulatory issues and reputational damage. DATA INTEGRITY SOLUTIONS Data integrity is crucial in the pharmaceutical industry to ensure the safety and quality of the products marketed and their production processes. In this sense, it is important to have a set of measures in place to prevent data manipulation and to have a complete and traceable history of data throughout the organization.  To this end, it is important to identify and assess vulnerabilities in the performance and security of computer systems, such as configurations, administrative permissions, password controls, auditing capabilities, network requirements, among others. In addition, it is essential to have an adequate backup system, ensure that hardware and software are suitable for their intended use, and conduct proper change management and management oversight. Data integrity is not just about network security, but involves the entire organization, from back-office processes to production. ALCOA PRINCIPLES Because of its importance, we have decided to dedicate a space to it in this blog. ALCOA is an essential framework used by regulated industries, particularly the pharmaceutical sector, to ensure data integrity and maintain Good Documentation Practices (GDP). The acronym was coined in the 1990s by Stan W. Woollen of the FDA’s Office of Compliance. The ALCOA principles apply to both paper and electronic data and are crucial to effective data management and governance. ALCOA represents five key principles. The European Medicines Agency (EMA) expanded upon the ALCOA framework in 2010 by adding four additional principles to ensure data integrity in the pharmaceutical industry.  These principles include completeness, consistency, enduringness, and availability.  By adhering to these guidelines, companies can prevent errors and data manipulations and ensure that their products meet FDA data requirements. RECOMMENDATIONS In today’s world, data is one of the most valuable resources. Therefore, it is essential that companies take measures to ensure its integrity.  Data integrity implies the accuracy, consistency and security of information. To implement it in your organization, it is essential to identify the key players and critical applications that rely on data. Also, consider incorporating technologies that ensure data integrity into your processes, devices and applications.  It is important to establish a long-term plan that will enable critical systems and applications to comply with data integrity guidelines in your organization. Make sure you take steps to protect data integrity in your organization and optimize your decision making! SUMMARY The pharmaceutical industry is becoming increasingly data-driven, so maintaining data integrity is essential. This blog aims to help industry players understand the basics of data integrity, as well as the guidelines and regulations applicable to it. In future posts, we will focus on specific use cases and technologies that can accelerate digital transformation in the pharmaceutical sector through trusted data. If you would like to learn more about data

In this era of big data, when more pieces of information are processed and stored than ever, data health has become a pressing issue — and implementing measures that preserve the integrity of the data that’s collected is increasingly important. Understanding the fundamentals of data integrity and how it works is the first step in keeping data safe. In our society, data has become the foundation for a modern economy, driving everything from smart devices to artificial intelligence to a wide range of ‘smart’ industries. Data integrity refers to the accuracy, completeness, and consistency of data over its entire lifecycle. It is a critical aspect of information management and is essential for ensuring the reliability and trustworthiness of data. Data integrity also refers to the safety of data with regard to regulatory compliance — such as GDPR compliance — and security. Another important aspect of data integrity is ensuring that data is properly organized and structured. This includes using consistent naming conventions and formatting, as well as defining clear rules for data entry and handling. By adhering to these standards, organizations can ensure that data is easy to understand and interpret, and that it can be used effectively for decision-making and analysis. WHAT DATA INTEGRITY IS NOT With so much talk about data integrity, it’s easy for its true meaning to be muddled. Often data security and data quality are incorrectly substituted for data integrity, but each term has a distinct meaning. DATA INTEGRITY IS NOT DATA SECURITY Data security is the collection of measures taken to keep data from getting corrupted. It incorporates the use of systems, processes, and procedures that restrict unauthorized access and keep data inaccessible to others who may use it in harmful or unintended ways. Breaches in data security may be small and easy to contain or large and capable of causing significant damage. While data integrity is concerned with keeping information intact and accurate for the entirety of its existence, the goal of data security is to protect information from outside attacks. Data security is but one of the many facets of data integrity. Data security is not broad enough to include the many processes necessary for keeping data unchanged over time. DATA INTEGRITY IS NOT DATA QUALITY Does the data in your database meet company-defined standards and the needs of your business? Data quality answers these questions with an assortment of processes that measure your data’s age, relevance, accuracy, completeness, and reliability. Much like data security, data quality is only a part of data integrity, but a crucial one. Data integrity encompasses every aspect of data quality and goes further by implementing an assortment of rules and processes that govern how data is entered, stored, transferred, and much more. THREATS TO DATA INTEGRITY GETTING STARTED WITH DATA INTEGRITY Ensuring data integrity is essential for any organization that relies on data for daily processes, decision-making or analysis operations. Without accurate and reliable data, organizations risk making incorrect or misleading decisions, which can have serious consequences. By implementing robust integrity measures, establishing clear processes and procedures, and organizing and structuring data effectively, organizations can ensure the integrity of their data and maintain the trust of their stakeholders. STEPS TOWARDS DATA INTEGRITY Because data integrity is such a broad concept, it useful to consider a few examples: Data integrity is absolutely critical in the pharmaceutical industry to make sure that the end products meet all the required quality standards. FDA’S REQUIREMENTS FOR DATA INTEGRITY If data integrity is not met then: With the raise of renewable energies and electric vehicles, decentralization of the energy grid has increased exponentially. It is fundamental to ensure energy consumption/production readings coming from a vast distributed IoT network is reliable and accurate to avoid market or grid disruptions as well as protecting the smart infrastructure from unwanted data accesses and manipulations. Some new emerging energy models such as energy communities will require data integrity to assure market exchange transparency. Industry 4.0 revolves around automation of data collection and intelligent processing, providing advanced insights on plant performance as well as optimization recommendations to increase efficiency and product quality. Data becomes a critical asset to ensure reliable performance of all systems, so maintaining its integrity throughout the entire data lifecycle is an essential task. New business models such as Machines as a Service are enabled through machine’s performance and production data and collaborative models such Circular Economy require maximum data trust to maximize materials lifecycle and reduce environmental impact. As we move toward a smart future where critical decisions and processes are dependent on massive amounts of data, it is essential that we design data integrity into new processes and retrofit them into legacy systems. A smart future depends on trusted data. Emanuel Agnelli – Armilis CEO