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Data Cleansing

Data cleansing is the process that removes data that does not belong in your dataset. Data transformation is the process of converting data from one format or structure into another

Improved decision making

Boost results and revenue

Save money and reduce waste

Save time and increase productivity

Minimise compliance risks

Remove duplicate or pointless observations as well as undesirable observations from your dataset. The majority of duplicate observations will occur during data gathering.When you measure or transfer data and find odd naming practises, typos, or wrong capitalization, such are structural faults.

There will frequently be isolated findings that, at first look, do not seem to fit the data you are evaluating. Removing an outlier if you have a good reason to, such as an incorrect data entry, will improve the performance of the data you are working with.

Most people concur that the quality of your insights and analysis when using data depends on the data you are using. In essence, poor data input leads to poor analysis output.If you want to develop a culture inside your business centred around sound data decision-making, one of the most crucial first stages is data cleaning, also known as data cleansing and data scrubbing.

Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. When combining multiple data sources, there are many opportunities for data to be duplicated or mislabeled