The digital transformation was a period of many changes for industries with the rise of many innovative technologies that have gained application within industrial plants. However, it was due to various technologies coming from an area of knowledge called Big Data that the technological revolution began to gain new proportions.
Understand how digital transformation has brought new opportunities for the intelligent use of large masses of data by digitizing processes by reading an article that will revolutionize your understanding of the subject. Click and read!
In a new digital era where Digital Twin, IoT sensors, and Artificial Intelligence algorithms are beginning to be applied within an industrial plant, it is with the use of Big Data methods that the advantages of digitalization take shape.
Within an industrial operation, there are many variables that need to be monitored and that can be better managed through data control.
The efficient processing of large amounts of data and the extraction of knowledge from them are the main objectives achieved with the use of Big Data methods. In an industrial installation, there are immense amounts of structured and unstructured data that must be extracted and processed.
Big Data becomes the main responsible for extracting useful knowledge from structured and unstructured data. And with the extraction and processing of this information, industries are able to better understand their present and prepare for the future.
MACHINE LEARNING E BIG DATA
It is very common for Big Data technology to be used in industrial plants with the aid of Machine Learning algorithms. The combination of techniques and methods from these two areas (Big Data and Machine Learning) allows the extracted data to be processed and interpreted, generating useful information for obtaining insights, which could not be achieved without automating the process of obtaining high-level knowledge from raw data and information.
Pattern recognition and prediction calculation performed by Machine Learning algorithms can be applied in the most different disciplines within an operation or process. The use of these algorithms allows companies to have the necessary tools to understand their market and segmentation, to have data help for decision making, to recognize recurring patterns from past data, and to predict possible future scenarios.
The gains generated with the use of Big Data aligned with Machine Learning within industrial processes is something that has been attracting more and more attention from these industries. However, there is still a big gap when it comes to the transition between current methods and aid in decision making through more efficient data processing.
The transition process must be careful, due to the great complexity involved in industrial processes, and adaptation to the fundamental prerequisite that is data extraction. It is necessary for industries to find the ideal application of these technologies for their specific processes.
We can say that data extraction and pattern recognition is just the first step in a long journey to digital transformation at its most innovative level.
What industries need from now on is to move towards digital transformation with efficient strategies and seek integration with tools that enable the application of Big Data and Machine Learning knowledge in applications that make sense within their own operation.
Thus, industries tend to maintain their competitive position within a competitive market, generating greater value on their production chain with the use of technology aligned with their interests.
Having these technologies as tools means not only having greater control over variables through the digital medium, but certainly having the necessary tools at hand to overcome the adversities of a market that increasingly demands flexibility, risk reduction, and trend prediction. The industries that are open to innovation will certainly be the ones that stood out and will be more prepared for the future.
Read more: Artificial Intelligence as an alternative for the Oil and Gas sector.
About the Author: Andre Andrade
