Previous articles have pointed out that databases form the foundation of industrial systems and that their effectiveness depends on being properly tailored to the nature of the data and processes.
The next step in the development of such systems is to use data not only for monitoring or analysis, but also to create digital representations of reality and support decision-making processes.
In this context, it is important to distinguish between the concepts of Digital Shadow and Digital Twin, which are often used interchangeably, even though they describe different levels of maturity for data-driven systems.
Digital Shadow vs. Digital Twin – A Key Distinction
In industrial practice, a digital representation of a system can take various forms, depending on the degree of data integration and the possibilities for its use.
Digital Shadow refers to a situation in which data from a physical system is mapped to the digital world, but without actively influencing its operation. This means that:
- data flows from the physical system to the digital system,
- analysis and visualization of the system’s state are possible,
- there is no feedback mechanism influencing the process.
This approach is characteristic of monitoring and reporting systems, where the goal is to understand the status and history of processes.
In contrast, a Digital Twin represents a more advanced form of integration, in which there is a two-way relationship between the physical system and its digital representation. In this case:
- data is not only analyzed but also used to model the system’s behavior,
- it is possible to simulate scenarios and predict events,
- the digital system can influence operational decisions or directly affect control.
In practice, this means a shift from observation to actively supporting or optimizing processes.
The role of the data layer in both approaches
Regardless of the level of sophistication, both Digital Shadow and Digital Twin are based on the same foundation: a coherent and well-designed data architecture.
The data layer is responsible for:
- integrating information from sensors, automation systems, and master systems,
- ensuring its timeliness and availability,
- storing the history necessary for analysis and modeling.
The difference lies not in the data itself, but in how it is used. In the case of Digital Shadow, data is used primarily for observation and analysis, whereas in Digital Twin, it serves as the basis for building models and autonomous decision-making.
This distinction is not merely conceptual but is also reflected in standards for digital twins, such as ISO 23247, which outline different levels of integration between the physical system and its digital representation—ranging from one-way data flow to feedback loops that enable influence over the process
From monitoring to decision support
The development of industrial systems does not end with the creation of digital representations. Solutions that help users make decisions based on available data are becoming increasingly important.
In this context, the concept of the Digital Advisor emerges—an analytical system positioned between Digital Shadow and Digital Twin solutions that:
processes data from multiple sources,
identifies relationships and patterns,
suggests possible actions or recommendations.
A key element of this approach is the presence of a human in the decision-making process (the so-called “human in the loop”). This means that the system does not make decisions autonomously, as in the case of a Digital Twin, but supports the operator or engineer by providing recommendations and key information for decision-making.
This approach is particularly important in industrial settings, where decisions have a direct impact on safety, operational continuity, and operating costs.
Consistency with the normative approach and the direction of development of industrial systems
The development of data-driven industrial systems is neither random nor purely technological. It reflects a broader normative approach, as described, for example, in standards on digital twins, such as ISO 23247. These standards indicate that the construction of modern systems should be based on consistent data integration, the ability to map the system’s structure and behavior, and the ability to use models for decision-making.
In practice, this means that the development of such solutions should be treated as a gradual process, rather than a one-time implementation of a specific technology. Industrial systems mature alongside the organization, and subsequent functional layers are built upon the existing data infrastructure.
This approach is directly reflected in the observed direction of system development:
- from monitoring systems (Digital Shadow), which enable data observation and analysis,
- through decision-support systems (Digital Advisor), in which data and predictions are transformed into recommendations,
- to modeling and predictive systems with feedback (Digital Twin), capable of automatically making decisions and controlling the physical system.
The transition between these levels is not abrupt. Organizations develop their systems evolutionarily, expanding their capabilities as the maturity of data, models, and operational processes increases.
As a result, data architecture becomes not only an element of infrastructure but a long-term foundation for the development of systems that support and automate decision-making in industry.
Summary
Data-driven industrial systems are evolving toward greater integration and the use of information in decision-making processes. In this context, it is crucial to distinguish between maturity levels—from Digital Shadow, through Digital Advisor, to Digital Twin systems.
Regardless of the stage of development, the foundation remains a properly designed data layer that enables the integration, analysis, and practical application of information in operations.
It is this layer that determines whether data will remain merely a record of events or become a real tool supporting the functioning of modern industry.
