January 1, 2022

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