What are the best practices for managing data in industrial automation?

1 Define data requirements

Before collecting and storing data, you need to define what data you need, why you need it, and how you will use it. This will help you avoid collecting unnecessary or irrelevant data, and focus on the data that matters for your objectives. You should also define the data sources, formats, standards, and protocols that you will use, and ensure that they are compatible and consistent across your devices, systems, and platforms.

2 Implement data validation

Data validation is the process of checking and verifying that the data you collect and store is accurate, complete, and reliable. Data validation can help you detect and correct errors, anomalies, and outliers that can compromise your data quality and integrity. You can implement data validation at different stages of your data pipeline, such as during data acquisition, transmission, processing, and storage. You can use various methods and tools for data validation, such as checksums, filters, rules, scripts, and software.

3 Secure data transmission and storage

Data security is crucial for protecting your data from unauthorized access, modification, or loss. Data security can help you prevent data breaches, cyberattacks, and sabotage that can damage your reputation, operations, and assets. You can secure your data transmission and storage by using encryption, authentication, authorization, firewalls, backups, and recovery systems. You should also follow the best practices and standards for data security, such as ISO 27001 and NIST 800-53.

4 Organize data structure and metadata

Data structure and metadata are the elements that describe and organize your data, such as data types, names, labels, categories, relationships, and descriptions. Data structure and metadata can help you improve your data accessibility and usability, as they make your data easier to find, understand, and analyze. You should organize your data structure and metadata according to your data requirements, and use common and clear conventions and terminology. You should also document your data structure and metadata, and keep them updated and consistent.

5 Optimize data processing and analysis

Data processing and analysis are the steps that transform your raw data into meaningful insights and actions. Data processing and analysis can help you improve your data value and utility, as they enable you to extract, filter, aggregate, visualize, and interpret your data. You should optimize your data processing and analysis by using appropriate methods and tools, such as databases, algorithms, software, dashboards, and reports. You should also automate your data processing and analysis as much as possible, and monitor and evaluate their performance and results.

6 Manage data lifecycle

Data lifecycle is the sequence of stages that your data goes through from creation to deletion. Data lifecycle can help you manage your data efficiently and effectively, as it helps you plan, execute, review, and improve your data activities and outcomes. You should manage your data lifecycle by following a systematic and iterative approach, such as the Plan-Do-Check-Act (PDCA) cycle. You should also align your data lifecycle with your business and operational lifecycle, and ensure that your data supports your decisions and actions.