Predictive maintenance is very important towards industrial economy by improving equipment efficiency, reliability and reducing downtime. In recent years, abundant of data of rotating equipment is readily available from various sources. However, these data are not being utilized and analyzed for improving maintenance performance. This requires advanced techniques to analyze a variety of data in order to transform into relevant information. Most problems with a lot of parameters involved were not being specific to analyze the contribution of motor failure. Therefore, this research proposed an efficient data analysis using Principle Component Analysis (PCA) in determining the most influential factor to the failure of the industrial motor. The result will show the parameters that influence the motor failure. This finding can be used as a guideline for predictive maintenance in order to mitigate the risk of the plant shutdown. © 2018 IEEE.