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The use of Mean Time Between Failure in Predictive Maintenance is increasing and is in line with the number of industries that are turning to industry 4.0 . Previously, predictive maintenance is being done by analyzing each machine individually and manually calculate them. This caused the predictive maintenance to become somewhat complex and a long process while it should not be. Big data helps to organize the data needed to calculate mean time between f ailure ef ficiently and PowerBI helps to visualize and analyze said data. We use data f rom several machines which record their runtime, downtime, and the type of downtime to get the mean time between f ailure. Contrary to the majority of existing implementations that mostly use complex data to schedule predictive maintenan ce, Our f indings f ind that simple data is suf f icient as long as it is processed in an organized environment such as using big data and visualized clearly and well using visualization applications like PowerBI.