Data analytics is the practice of investigating large amounts of data in order to formulate hypotheses about the information they possess, progressively with the help of specific software and methods. Data analytics practices and systems are widely used in business sectors to facilitate organizations to make more informed business decisions and used by engineers and researchers to verify or disprove theories, models and hypotheses. Data analytics can help companies increase revenue, improve operational efficiency, develop marketing campaigns and consumer service challenges, react more quickly to changing market trends, and gain an edge over industry competition , with the ultimate goal of increasing business performance. Depending on the specific application, the analyzed data may include past statistics or new practice data collected for real-time analytical use. Greater uncertainty and low growth have caused manufacturers to squeeze every asset for maximum value. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay The next target is their own data. Process manufacturers have come under pressure from across the board of late as raw materials have become more expensive or difficult to source and growth has slowed. Most manufacturers have already made major changes to streamline their operations, using traditional approaches to get the most productivity possible from their supply chains and operations. To do more with less in an unstable, slow-growth environment, however, companies must explore new ways to maximize the productivity and profitability of their processes. There is one substantial resource 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, thanks to cheaper computing power and rapidly advancing analytics opportunities, process manufacturers can use that data, gathering insights from different data sources and leveraging machine learning packages to expose new ways to optimize their processes from the traceability of raw materials to the sale of their finished products. Advances in advanced analytics also allow manufacturers to solve previously unsolvable problems and reveal those they were unaware of, such as unknown bottlenecks or unprofitable production lines. This is the first and arguably the oldest rule of manufacturing. In the past, the best way to handle it was to hope that someone on the factory floor, using a combination of instinct and experience, would see indications of a machine or process about to fail and fix it in time. However, with increasingly complex machinery to keep track of, continued pressure to increase uptime and productivity, and the growing demand for flexible operations, hope is no longer a viable strategy. Companies can maximize the uptime of critical assets and machines by analyzing big data to predict failures. Predictive maintenance systems collect historical data (structured and unstructured, machine-based and non-machine-based) to produce information that cannot be observed with conventional techniques. With the use of advanced analytics, companies can define the circumstances that.