**Abstract**

Pre-processing data on the dataset is often neglected, but it is an important step in the data mining process. Analyzing data that has not been carefully screened for such challenges can produce misleading results. Thus, the representation and quality of data are first and foremost before running an analysis. In this paper, the sources of data collection to remove errors are identified and presented. The data mining cleaning and its methods are discussed. Data preparation has become a ubiquitous function of production organizations – for record-keeping and strategical making in supporting various data analysis tasks critical to the organizational mission. Despite the importance of data collection, data quality remains a pervasive and thorny challenge in almost any production organization. The presence of incorrect or inconsistent data can significantly distort the results of analyses, often negating the potential benefits of strategical making driven approaches. This tool has removed and eliminated errors, duplications, and inconsistent records on the datasets.

**Keywords:** Data, Data cleaning, Data collection, Data mining, Data preparation, Data collection, Data quality, Messy data
