Digital Twins are gaining ground as an essential tool to transform the way organizations analyze their projects. These solutions offer significant advances in process prediction, optimization and evaluation. Following this point, PUE DATA experts have investigated how to maximize the use of this technology.
The importance of data quality
Digital Twins can virtually recreate and replicate products, infrastructure and systems. These tools monitor data in real time, providing a precise analysis of the behavior of the realities they represent.
The data is analyzed using machine learning and Artificial Intelligence techniques, with the aim of anticipating possible problems before they occur in the real world and obtaining virtual representations prior to development.
In this context, it is crucial to train the data that feeds the twin so that the representation of reality is as accurate as possible. However, lack of access to quality data is one of the main barriers to implementing a digital twin, according to McKinsey analysts, along with other factors such as employee training.
Access to quality data is one of the main issues that must be addressed to develop a Digital Twin according to Mckinsey
Sergio Rodríguez de Guzmán, CTO and co-founder of PUE DATA, states that “the efficiency of a Digital Twin depends largely on the data that feeds it. If the tool is based on incorrect data, there will be a distortion between the twin and the reality it represents, similar to hallucinations in LLMs. That is why it is important to have relevant and quality data, as well as an appropriate data management methodology.”
Maximize the value of data from a digital twin
To make the most of innovations like this, in addition to having quality data, companies or institutions must clearly define their purpose. As experts in data management, PUE DATA highlights the aspects to consider to obtain true value:
- Refresh Rate: Determining the appropriate frequency to update data is key depending on the use case. Real-time oriented digital twins require constant updates to reflect immediate changes in the physical environment.
- Data validation and verification: Ensure data quality through controls that validate its accuracy, consistency and relevance. This step minimizes errors and ensures that the simulations are reliable and representative.
- Continuous update: Digital twins must adapt to new conditions and learning. Incorporating advanced tools such as machine learning can improve their accuracy and usefulness over time.
- Security: Protecting data from unauthorized access and ensuring its integrity is essential. Implementing robust cybersecurity measures avoids vulnerabilities that could compromise the models.
In conclusion, data is the foundation that defines the success of a digital twin. Its quality, safety and adequate management allow the creation of precise models that improve strategic decision making. In Spain there are already many projects, such as the Mar Menor Digital Twin or the Vigo Council, which help take care of the environment and improve the urban environment.
