2023-09-27 16:37:38
Big Data is the term used to define the large amount of data generated by industries, companies, government institutions and people every day around the world. Social networks, financial transactions, searches and various forms of consumption produce data that, when properly analyzed, can generate solutions, insights and present trends in the various fields in which they are obtained.
To portray the magnitude of this volume of data, the Finances Online portal found out that, in 2021 alone, over 1 trillion megabytes of data were created per day and predicted an increase in data consumption, going from 74 zettabytes in 2021 to 149 zettabytes in 2024.
In this context, industries and companies can count on the investigation and analysis of this range of data and information to make strategic decisions, operational improvements and innovation. This is what José Paixão Barbosa Sousa, data scientist and specialist in cybersecurity applications in technological innovations, says.
“Companies from different sectors are recognizing the value of data for strategic decision-making, operational improvements and innovation. In the automation industry, for example, they help to improve the efficiency, productivity and reliability of automated processes”, he comments.
To corroborate this view, a report published at the end of last year by the International Data Corporation (IDC), a consultancy specializing in information technology, telecommunications and consumer technology, pointed that by 2026, 75% of market-leading companies will have systematically invested in digital innovation.
Professor José Paixão Barbosa Sousa listed some aspects that he considers essential for the effective use of data analysis in automation industries:
Predictive maintenance: Data analytics can be used to continuously monitor automated machines and equipment. Sensors can collect data on performance, temperature, vibration, power consumption and other parameters. Additionally, data science algorithms can identify patterns and trends that indicate imminent failures, enabling preventative maintenance to avoid unplanned downtime.
Process optimization: Data science can be used to optimize production and logistics processes. Data can help identify bottlenecks, improve production sizing, optimize the transportation route of goods and improve inventory planning.
Product quality: Data analysis can monitor product quality in real time. Sensors and cameras can capture data about defects or imperfections. Machine learning algorithms, moreover, can identify defect patterns and make real-time decisions, such as shutting down a faulty machine.
Energy efficiency: Data science can be used to optimize energy consumption in automation systems. In this sense, algorithms can automatically adjust the operation of equipment to save energy when demand is low and ensure more efficient use of energy.
Quality and compliance monitoring: In regulated industries such as food and pharmaceuticals, data science can be used to ensure that automation processes comply with quality standards and regulations. The data can be used to track and document production and product quality.
Data-driven decision making: Data science can provide valuable insights for strategic decision-making in the automation industry. Data can help identify opportunities for improvement, reduce costs and increase competitiveness.
Finally, data from the Industry Portal published in July demonstrate that Industry 4.0 (which relies on technology as an ally in its processes) reached US$ 1.77 billion in Brazil in 2022 and is expected to reach US$ 5.62 billion in 2028. For Sousa, these results involve good use of data science.
“Data science plays a key role in industry, enabling companies to optimize their processes, improve product quality, save resources and increase overall efficiency. It makes industrial operations more productive and profitable”, he concludes.
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