{"id":248999,"date":"2022-11-24T09:56:00","date_gmt":"2022-11-24T08:56:00","guid":{"rendered":"https:\/\/www.altermes.fr\/clean-and-homogenize-data-prior-to-visualization-data-cleansing\/"},"modified":"2023-12-14T12:36:50","modified_gmt":"2023-12-14T11:36:50","slug":"clean-and-homogenize-data-prior-to-visualization-data-cleansing","status":"publish","type":"post","link":"https:\/\/www.altermes.fr\/en\/clean-and-homogenize-data-prior-to-visualization-data-cleansing\/","title":{"rendered":"Clean and homogenize data prior to visualization (Data Cleansing)"},"content":{"rendered":"\n
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Now more than ever, rapid access to information is essential for making the right decisions and managing your business. But more than the quantity of data, it’s the quality of the data that provides reliable, easy-to-analyze information for results that are as close to reality as possible. That’s why data cleansing<\/em> is an important step for any company wishing to digitalize these processes.<\/p>\n\n

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What is Data Cleansing?<\/h2>\n\n

Beforeintegrating information into Business Intelligence tools<\/a>, it is essential to ensure that it is correct, to avoid analysis errors that can have disastrous consequences on decision-making.<\/p>\n\n

Data comes from multiple sources, both external and internal, and is most often stored in its raw state in a data lake or in databases. The information must therefore be cleaned and homogenized between storage and integration, to guarantee the quality of the input data.<\/p>\n\n

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What are the most common data errors?<\/h2>\n\n

There are 3 main types of error: syntactic, semantic and coverage.<\/p>\n\n

Syntax errors<\/h3>\n\n

These can range from typos to the use of the wrong format or unit system.<\/p>\n\n

Examples:<\/u><\/strong><\/h4>\n\n