Introduction
Since Industry 4.0, companies have been implementing digital technologies to digitize their operations based on cyber-physical models.
Ten years after the beginning of the transformation generated by Industry 4.0, these companies are often struggling with the difficulty of generating value through the use of the data generated in their operations.
We can say that we are no longer in an age of data, but in an age in which it is necessary to understand, organize, and qualify this data in order to effectively generate value.
In this article, we will address some of the main concepts of data contextualization and why it is essential for any industrial operation.
Too much data, little value
Digital transformation is a concept that has been widely spread among any industrial operation. From this movement, industries have gone digital and this has meant that huge amounts of data have been generated.
Regardless of the industry, there are multiple systems working in numerous varied applications that are producing data all the time.
This massive amount of information brings to attention a major issue in many operations: How can I extract more value from my data?
Data contextualization: turning data into meaningful information
The concept of contextualization is not so simple, but it plays a key role in industrial operations, especially in heavy asset operations, process plants, power and utilities, and more recently in renewable energy.
Contextualization means putting data into specific contexts so that it can generate meaning. In the contextualization process, three questions are fundamental.
what is it? what is it for? and who is it for?
This initial filtering and classification of data is known as data ops and occurs in the stream of data coming from the database, which is then processed, converted into usable formats, and sent to the integration stage.
It is at this stage that the data will be classified as useful or useless for a particular sort of application.
The context
The context is a fundamental part of the process. This is where the purpose for which my data will be analyzed and what applications will be obtained from it are defined.
Once connections between data from multiple systems are established, it is possible to use them to obtain insights for decision-makers.
Advantages of a contextualized operation
Some of the benefits of contextualizing data are:
- access to highly relevant information
- organized and easy-to-understand data availability
- high-value insights for decision-makers
- in-depth analysis with information from multiple sources
Conclusion
While digital transformation has been crucial in extracting and processing massive amounts of data, industries need this data to be contextualized in a way that they can deliver high-value insights to decision-makers.
Data that is integrated and correlates with each other for defined purposes can be great tools for meaningful actions within an industrial process or operation.
The result is a smarter, more assertive, and safer operation.
About the Author: Andre Andrade
