Data analytics involves studying large amounts of data in order to make hypotheses about the information they contain, incrementally through using specific software and methods. Data analytics practices and systems are widely used in business industries to help organizations make more informed business decisions and used by engineers and researchers to verify or disprove theories, models, and hypotheses. Data analysis can help businesses increase revenue, improve operational efficiency, develop marketing campaigns and resolve consumer service issues, respond more quickly to changing market trends and take an advantage over industry competition, with the ultimate goal of increasing business performance. Depending on the specific application, the data analyzed may include either past statistics or new data from practice that has been collected for real-time analysis. Increased uncertainty and slow growth have pushed manufacturers to leverage every asset to maximize its value. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get an original essay The next target is their own data. Process manufacturers have recently faced pressure from all areas as raw materials have become more expensive or difficult to source and growth has slowed. The majority of manufacturers have already made the most obvious changes to streamline their operations, using traditional approaches to get as much productivity as possible from their supply chains and operations. However, to do more with less in a slow and unstable growth environment, companies must explore new ways to maximize the productivity and profitability of their processes. There is one important asset that manufacturers have not yet optimized: their own data. Industries generate enormous volumes of data, but many have failed to exploit this potential source of information. Traditionally, manufacturers have lagged behind other industries in terms of IT capabilities. However, due to less expensive computing power and rapidly advancing analytics opportunities, process manufacturers can utilize this data, collecting insights from multiple data sources and leveraging data packages. machine learning to expose new ways to optimize their processes, from tracing raw materials to selling their finished products. Advanced analytics developments also allow manufacturers to solve previously intractable problems and reveal those they were unaware of, such as unknown bottlenecks or unprofitable production lines. This is the first and arguably oldest rule of manufacturing. It used to be that the best way to manage was to hope that someone in the factory, using a combination of instinct and experience, would see indications that a machine or process was about to fail. and would fix it in time. However, with more complex machines to keep up with, constant pressure to increase uptime and productivity, and a growing demand for flexible operations, hope is no longer a viable strategy. Businesses can maximize the uptime of critical assets and machines by analyzing big data to predict their failure. THE.