Data Cleansing Types and Benefits

Data Cleansing Types and Benefits

What do we mean by data cleansing? It defines that a set of data is accurate. Companies rely heavily on computerization of data in a simple way, so data cleansing is a very regular task. In cleansing operation, to check for the accuracy and consistency different types of tools are used to check for consistency and accuracy.

Data Cleansing is of two categories depending upon the complexity of tasks.

Simple Cleaning. In order to verify accuracy various set of records are read by individual person or group of persons. In this task, correction of spelling mistakes and typos are done, proper filling and labeling of mislabeled data are done. Further incomplete and missing entries are completed. In order to ease operations, outdated and unrecoverable data are eliminated.

Complex cleansing. In this data, verification is done by a computer program according to a set of rules and procedures provided by the user. Misspelled words are corrected and the data which has not been updated since last five years are deleted. Even the missing city in the database can be filled by a more complex program. This is based on postal pin code and changes in currency types on pricing.

Data cleansing is required for creating efficiency of data related businesses. If the database is not updated or not correct, there is no use of contracting clients by the way of phone numbers given in the databases or sending regular emails saved to the addresses thereon. Further, it ensures that there is always consistent and correct data available in the databases. This helps to minimize errors and assists to maintain useful and meaningful records even if there is a large volume of data stored.

When two database work in cycle, data cleansing is considered as more relevant. Customer information available at one branch is available at the other branch and this gets updated at one branch gets automatically revised in the database of other branches also.

Database cleansing use techniques like transformation, rationalization, and standardization. Further, these comprise data profiling, data enrichment, and augmentation. So, databases need to be run through data cleansing periodically in order to avoid the errors which could lead to inefficient work and more complications. This process involves conversion, formatting, and preparation for upload. Since it is time-consuming, it is wiser to wiser to outsource the selected components. of business and it requires a lot of experience in data migration.