Big data in Industry 4.0. Thus, highlighting how to use the vast amount of data better today to improve industry decision-making.
Big Data In Industry 4.0? How Can One Collaborate With The Other?
As already said. Nowadays, industries have been generating a very expressive volume of data. And that amount only grows in volume and speed. Such growth makes it increasingly necessary to use specialized techniques to deal with the situation.
As a complicating factor, the industries have not been able to give vent to analyzing the data generated by their various sensors, actuators and systems. For example, we can mention the millions of events recorded during the operation of any industrial plant, which the millions store.
Although the ideal brought about by big data seems promising, little is proactively used of this data. Often they are only remembered in moments of incidents when they need to be analyzed manually.
Hence the importance of efficient data processing, aiming at excellence in the use of collected data. This is where big data in Industry 4.0 becomes critical to solving problems of this nature.
At this point, we can discuss the leading techniques and tools to obtain the expected results in data analysis. Currently, several terms are cited, varying with the source of information, but all are related to the optimized information analysis.
Some Of These Are:
- Data preparation: is defined as a fundamental process of collecting, cleaning, normalizing, combining, structuring and organizing data for analysis. This process represents the initial step for working with Big Data to be successful. This is because the process seeks to increase data quality. Consequently, it assists in data mining, serving as an initial sieve in the analysis process.
- Data mining is exploring large amounts of data to find anomalies, patterns and correlations to improve decision-making, thus providing strategic advantages over the competition. It covers a variety of techniques and methods in different analytical capacities according to the specific need.
- Big Data Analytics: The focus of extensive data analysis is on the strategy that will be used to manage the large volume of data. Distributed computing generally divides the big problem into more minor problems. Thus, aiming to achieve the objective of processing a large mass of data. There are several data-driven solutions to explore the potential of big data analytics better. Apache Hadoop is one of the most popular tools for processing large volumes of data.
Machine Learning represents a much deeper analysis method that uses algorithms capable of storing and structuring knowledge. In this sense, these algorithms can learn about a given problem interactively. This process is based on the data presented to the machines and conditions them for learning. Thus allowing computers to find hidden possibilities and insights, even when they are not necessarily programmed to look for something specific.
Big Data In Industry 4.0: We Have Already Seen That They Form A Super Duo!
Gathering everything we have learned so far, it is possible to conclude that, when it comes to big data in industry 4.0, these are not only allies but fundamental to each other.
In this way, finding errors more efficiently and making adjustments and corrections in processes more quickly are some of the numerous benefits this duo can provide. Collecting, analyzing and using data with maximum efficiency represents gains in various spheres of the industry.
Thus, when considering big data as a decision-making model in industry 4.0, some aspects of improvement are expected, such as:
- Reduced production downtime: According to Honeywell research, data analysis and cross-referencing, made possible through big data analytics, can decrease equipment downtime by 26%. In addition, the reduction of unscheduled downtime reaches 23%.
- Preventive Maintenance: The virtualized system will more accurately predict the need for equipment maintenance. This implies preventing more severe problems from happening.
- Access to truthful and accurate information: Data analysis is done quickly. Thus, industries can have more agile and accurate insights. These lead to real-time and better-informed decisions.
- Reduction in the number of operators: The system will make decisions and carry out operations independently. This will bring the industry better performance, plant safety and energy savings;
- Optimization of operational costs: The use of big data analytics allows a significant reduction of wasted resources, given the possibility of predicting risks of equipment failure ( predictive analysis ).
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