Data cleansing is the process of finding, correcting, or removing incomplete, irrelevant, or inaccurate data. The aim is to create a high-quality and accurate data set, which will make the results of the analysis more reliable.
When a company is small and turnover is low, a simple spreadsheet can track the evolution of the data, but as soon as it starts to develop and grow it is almost impossible to control data accuracy with human resources alone. Consequently, a company has two options: either let the valuable data information go to waste or ride the data wave and make the most of it. The latter is possible through a process of data analysis, the first step of which is data cleansing.
Data usually comes in large quantities, but in some cases, it is not in the right or consistent format. Data may also be following a set of rules from different sources that might pose further challenges. Data cleansing is the process of removing and, where appropriate, correcting erroneous, incomplete, inaccurate, or irrelevant data, thereby improving the quality of the information and thus the analysis as a whole.
Data cleansing techniques are multi-layered and include steps such as dealing with missing data, standardisation, checking accuracy, removing duplicates, and dealing with possible structural errors. There is no single, established method for performing data cleansing, as every database is different. There is not any benchmark or guideline that analysts follow consistently.
It is essential to distinguish between data cleansing and data transformation. In the former case, data is removed from the dataset, in the latter case it is converted from one format or structure to another.
The importance of data cleansing cannot be disputed. We live in an age where data is one of the greatest treasures, so it is not difficult to see the need to strive to make data of the highest quality. The more accurate data a company has, the more value it can get out of its operations, and the more efficient and successful it can be.
The process can help to save significant costs and eliminate or correct errors. It is therefore a form of prevention, but it can also increase efficiency, support decision-making, and build a coherent, well-functioning, data-driven system.
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