The previous article (“Databases in Industry”) noted that a database serves as the foundation of industrial systems, enabling the integration, analysis, and use of data in decision-making processes.
In practice, however, simply implementing a database does not guarantee the effectiveness of the entire solution. The key lies in selecting the right database—one tailored to the nature of the data and how it will be used within the system. Industrial environments generate information with highly varied structures and dynamics, which directly influences how it is stored and processed.
Data diversity as a starting point
To choose the right approach to data storage, it is essential to understand the nature of the data. In industrial systems, we most often deal with several basic categories:
- data (e.g., temperature, vibration, pressure),
- data from automation systems (states, control signals, process variables),
- event data (alarms, failures, state changes),
- structural and configuration data (system description, relationships between components).
Each of these types serves a different function in the system. Measurement data is responsible for observing reality, automation data for control, event data for recording significant moments, and structural data for providing context.
In practice, this means that the data architecture must account for different methods of storing and processing data, rather than attempting to unify everything into a single model.
Basic approaches to data storage
In response to the diversity of data in industrial systems, several main approaches are used to organize it:
- relational – used where data consistency and clearly defined relationships are essential,
- time-series – used for measurement data recorded continuously,
- event-driven – focused on recording and processing events in a specific order,
- flexible – designed for data with a variable structure.
Each of these approaches addresses different needs. Structured data requires precision and integrity checks, while measurement data requires performance and the ability to handle large volumes. Event data, on the other hand, requires maintaining sequence and temporal context, and data with a variable structure requires flexibility.
Applying a single approach to all data types typically leads to limitations—either in terms of performance or analytical capabilities.
Hybrid architecture as a practical approach
In practice, modern industrial systems are based on a hybrid architecture in which different approaches coexist and complement one another.
This means that data is not stored in a single location in a uniform manner, but is distributed according to its nature. Measurement data goes to structures optimized for time and volume, configuration data to structures ensuring consistency, and event data to mechanisms enabling real-time analysis.
This approach not only increases system performance but also better reflects the company’s operational reality. As a result, it is possible to simultaneously conduct monitoring, historical analysis, and respond to current events.
The Importance of Industrial Automation Data
Data from automation systems plays a particularly important role in the overall architecture. This data directly reflects the logic of technological processes and how equipment operates.
Measurement data alone only shows the system’s state, whereas only when combined with control data can we answer the question of why that state changed. In practice, this means that different types of data must be correlated and analyzed in the context of time and the relationships between system components.
This approach enables:
- the identification of the causes of events,
- the analysis of dependencies between control and the state of devices,
- the reconstruction of the course of technological processes.
Without taking this layer into account, it is impossible to move from simple monitoring to a true understanding of processes.
Challenges in choosing the right approach
Data storage in industrial systems is a complex and multifaceted process. It is not merely a technological decision but requires consideration of the specific nature of operational processes, the nature of the data, and how it is used within the organization.
One of the main challenges is the need to reconcile conflicting requirements. On the one hand, the system must efficiently handle large volumes of measurement data generated in real time; on the other, it must ensure the consistency and integrity of structured data. Additionally, there is a need for rapid response to events and the ability to perform historical analyses.
Another significant challenge is the integration of data from various sources. Automation systems, sensors, and business applications inherently operate in separate environments, using different data models. Integrating them requires not only the right architecture but also an understanding of the relationships between the data and their operational context.
In practice, the decision-making process is influenced by, among other things:
- the volume and frequency of data generation,
- requirements regarding access and processing time,
- the need for real-time and historical analysis,
- the complexity of technological processes,
- integration with existing system infrastructure.
Industrial systems evolve alongside the organization—the number of devices, measurement points, and the scope of analysis all increase. The data architecture should therefore allow for gradual expansion without the need to rebuild the entire system.
As a result, selecting the right approach requires viewing the system as a whole—not only through the lens of technology, but above all through the lens of the processes it is designed to support.
Summary
Selecting the right approach to data storage is one of the key elements of industrial system design. It does not involve choosing a single solution, but rather consciously combining different models into a coherent architecture.
It is precisely this architecture that allows for the effective processing of diverse data, which can then be used for analysis, optimization, and decision-making.
The next article in this series will explain how a properly designed data layer enables the creation of digital twins and predictive systems in modern industry.
