Data Quality Management
Data Quality Management (DQM) is a basic business train that requires a blend of rules, procedures, and innovations to safeguard the precision, culmination, auspiciousness, and consistency of big business data.
Data cleansing or data cleaning is the way toward recognizing and rectifying (or evacuating) degenerate or off base records from a record set, table, or database and alludes to distinguishing inadequate, wrong, off base or unimportant parts of the information and after that supplanting, changing, or erasing the grimy or course information.
Understanding sensitive data risk is key. Data cleaning analysis includes discovering, identifying, and classifying it, so data stewards can take tactical and strategic steps to ensure right data is cleansed.
Data integrity is the upkeep of, and the confirmation of the exactness and consistency of, information over its whole life-cycle, and is a basic viewpoint to the outline, execution, and utilization of any framework which stores, forms, or recovers information.
Data enrichment is a general term that alludes to forms used to upgrade, refine or generally enhance crude information. This thought and other comparative ideas add to making information a benefit for any cutting-edge business or undertaking.