Sie bewegen sich in der Welt der automatisierten Systeme. Wie stellen Sie die Datenintegrität zwischen ihnen sicher?

Data integrity in today's world is no brainer requirement, many companies are investing hugely in 21 CFR Part 11 compliant systems yet they lack data integrity. Majority of the times Data loss, Data recording error, breach of Data integrity due to password exchange, Due to password complexity and many more issues. All of these problems can be solved by two critical things, 1. Understanding Why of Automated systems- is it to replace the human error or just as fancy system ? are we are really solving design issue as it might arise when there is deviation due to data loss 2. Robust Compliance system - have seen majority of the cases, the system lacks continues verification. Though its automated systems, it requires continuous verification.

To ensure data integrity in automated workflows, consider the following strategies: - Data validation and standardization - Regular quality monitoring and audits - Data lineage tracking and clear documentation - Automated testing and backup/recovery processes - Access controls, authentication, and data encryption - Continuous improvement and training Implementing these measures helps maintain accurate, complete, and reliable data, supporting informed decision-making and operational efficiency.

To ensure data integrity in automated workflows, I start with thorough data validation and cleansing, removing duplicates, enforcing data type consistency, and standardizing formats. I incorporate data integrity checks such as range validation, referential integrity, and checksums. Exception handling captures errors and logs them for quick resolution. Versioning and audit trails allow tracking of changes, while post-processing validation confirms accuracy. Continuous monitoring and periodic audits help maintain data integrity over time, ensuring that automation processes run reliably and consistently.

In some occasions we implement routines to validate the data in the source device, at the time it is generated and recorded. Here is a step by step example of what we've done in some batch control systems: 1) the PLC stores the data to be registered in a data structure and triggers data collection, 2) the data collection system(external) registers the data, 3) the data collection system recovers the registered data and writes it in a data structure in the PLC, 4) the PLC compares the delivered and the recovered data, and if it is ok, the process is released for the next task. If not, the PLC provides the feedback, a rollback is performed, and the operation is repeated.

Data integrity refers to the accuracy, consistency, and reliability of data during various processes like storage, retrieval, and processing. In automation testing, the data used for testing needs to remain accurate and consistent to ensure that the tests are reliable and the results are valid. Data integrity is a broad concept that includes aspects of data quality and security. Data quality refers to how well data meets a company's expectations for accuracy, completeness, validity, and consistency. Data security is the infrastructure, tools, and rules that ensure data is only accessible to authorized users and applications, is used in a business-compliant manner, and is protected from loss, theft, or other issues.