Data mining techniques have been widely applied to various fields including industrial, business, and governmental applications. Missing data is a common occurrence in a number of real-world databases, which may substantially affect the accuracy of data processing. In this paper, we propose a novel approach for missing data estimation by efficiently exploring inter-sensor correlation. Namely, given multiple sensors for data collection, we attempt to recover the missing data of a few sensors by using the measurement data from other sensors. Towards this goal, we develop an iterative solver for missing data estimation. Our numerical experiments on two industrial datasets demonstrate that the proposed method can reduce the imputation error by up to 7.25× compared to a conventional method in the literature.