Making raw data analytics-ready using data cleansing

Raw transactional data can have many kinds of inconsistencies, either inherent to the data itself or developed during movement between various data processing systems, during the data ingestion process. The data integration process can also introduce inconsistencies in data. This is because data is being consolidated from disparate systems with their own mechanism for data representation. This data is not very clean, can have a few bad and corrupt records, and needs to be cleaned before it is ready to generate meaningful business insights using a process known as data cleansing.

Data cleansing is a part of the data analytics process and cleans data by fixing bad and corrupt data, removing duplicates, and selecting a set of data that's useful for a wide set of business use cases. When data is combined from disparate sources, there might be inconsistencies in the data types, including mislabeled or redundant data. Thus, data...