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Clean up in Calypso is advisable on non-prod systems, the steps mentioned in the course are intended for use on application during testing phase to start on a clean slate.
Data cleansing or data cleaning is the process of identifying and removing (or correcting) inaccurate records from a dataset, table, or database and refers to recognizing unfinished, unreliable, inaccurate or non-relevant parts of the data and then restoring, remodeling, or removing the dirty or crude data.Data cleaning techniques may be performed as batch processing through scripting or interactively with data cleansing tools.

After cleaning, a data set should be uniform with other related datasets in the operation. The discrepancies identified or eliminated may have been basically caused by user entry mistakes, by corruption in storage or transmission, or by various data dictionary descriptions of similar items in various stores.

Data cleaning is the process of preparing data for analysis by removing or modifying data that is incorrect, incomplete, irrelevant, duplicated, or improperly formatted. This data is usually not necessary or helpful because it may hinder the process or provide inaccurate results. There are several methods for cleaning data depending on how it is stored along with the answers being sought. Data cleaning is not simply about erasing information to make space for new data, but rather finding a way to maximize a data set’s accuracy without necessarily deleting information. For one, data cleaning includes more actions than removing data, such as fixing errors, standardizing data sets, missing codes, and identifying duplicate data points.

This module will provide the details to delete unnecessary data from the database. Notice that any of these procedures should never be executed in a productive environment.