Data cleansing is also referred to as data scrubbing. This is the basic and initial step of the Master Data Management (MDM) process flow. The rapid growth of e-commerce has increased the integration of multiple databases for which data cleansing is the must to ensure that the data in each database is standard, consistent and accurate to eliminate the errors.
It is the act of amending the data from the database by detecting and correcting corrupt or inaccurate data which is improperly formatted, incomplete, incorrect or duplicated. Usually data is held with duplicates and irrelevant data which should be eliminated by data cleansing.
It is the process of analyzing the data by experts to identify the content, structure and the quality of the data.
It is the systematic process to ensure the data structure is suitable or serves the purpose. Here the undesirable characteristics of the data are eliminated or updated to improve the consistency and the quality. The goal of this process is to reduce redundancy, inaccuracy and to organize the data.
It is a crucial process where the data is defined, formatted, represented and structured in all data layers. Development of schema or attributes is involved in this process. Data standardization helps in achieving better results in further steps down the road.
It is the process of removing redundant data. Here duplicate data is deleted, leaving only one unique copy of the data to be stored. This improves data protection, increases the speed of service and reduces the overall costs.
Quality Control is performed to check the accuracy of data in all stages and at the end of the process by data experts.
Improved data quality and accuracy
Improved operational efficiency & reduced hurdles
Reduced risks, costs and turnaround
Enables trend analysis and benchmarking
Improved data security and accessibility