Data cleansing, also known as data cleaning or data scrubbing, is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. The goal of data cleansing is to improve the quality of the data and ensure that it is accurate, complete, and consistent. This is a labour intensive process and can take upto 70%-80% of any data science project.
Data cleansing involves several steps, including:
Data profiling: This involves analyzing the data to identify any inconsistencies, errors, or outliers.
Data standardization: This involves converting data into a consistent format or structure to ensure that it can be properly analyzed.
Data enrichment: This involves adding additional data to the dataset, such as geographic or demographic data, to enhance its value.
Data matching: This involves comparing data from different sources to identify duplicates or records that refer to the same entity.
Data validation: This involves checking the data for completeness, accuracy, and consistency.
Data transformation: This involves converting data from one format to another, such as converting text data to numerical data.
Data normalization: This involves scaling the data to a common range or distribution to make it easier to analyze.
The benefits of data cleansing include improved data quality, better decision-making, reduced errors and costs, and increased efficiency. Data cleansing is an essential part of data management and should be performed regularly to ensure that the data remains accurate and useful.
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