In the past decades, process engineers are facing increasingly more data analytics challenges and having difficulties obtaining valuable information from a wealth of process variable data trends. The raw data of different formats stored in databases are not useful until they are cleaned and transformed. Generally, data cleaning consists of four steps: missing data imputation, outlier detection, noise removal, and time alignment and delay estimation. This paper discusses available data cleaning methods that can be used in data pre-processing and help overcome challenges of "Big Data".