As we move through the industrial revolution of data, companies are beginning to realize the inadequacy of traditional data management tools in handling the complexities of modern data. Many have had to experience rude wake-up calls with failed migration or transformation initiatives caused by poor data, missing data quality management systems, and a reliance on outdated methods that are no longer effective. Data profiling and data cleansing are the two fundamental functions or components of Data Ladder’s data quality management solution and the starting point of any data management initiative. Put simply, to fix your data, you must know what’s wrong with it. This post covers everything you need to know about the difference between data profiling and data cleansing. Let’s dig in. Data Profiling vs Data Cleansing – What’s the Key Difference? In a data quality system, data profiling is a powerful way to analyze millions of rows of data to identify errors, missing information, and any anomalies that may affect the quality of information. By profiling data, you get to see all the underlying problems with your data that you would otherwise not be able to see. Data cleansing is the second step after profiling. Once you identify the flaws within your data, you ...
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