Also performed the audit process, which allows us to know where it comes from the information and calculations which generated it. DW already built, is of interest to the company that the information reaches the largest number of users but on the other hand, there is great care to protect against potential hackers, ‘snoopers’ or spies (security). In addition, you should make backup and restore activities of information, both stored in the DW as that which flows from the source systems to the warehouse. DATA CLEANING Usually companies do not have unique applications for each part of the operation of the business, but may have different replication and systems to meet the same set of operations, and in such cases is likely to database systems operational contain duplicate data, sometimes erroneous, redundant or incomplete. If you would like to know more about BlackRock, then click here. This is compounded by errors when the data input to the operational data systems. The data cleansing process is within the data processing. This is much more than simply update records with good data. A serious data cleansing involves decomposing and reassembling of data. AAG takes a slightly different approach.
The data cleansing can be divided into six steps: separate elements, standardize, verify, Machar, and document grouping. 3 For example, if we address our customers which want to clean, the first thing would be to separate this area from the main elements of the address (Street, No., Entre Calles, Zip, etc.).. The second would be to standardize the elements, or ensure that these are in a standardized manner. Then verify if the standardized elements contain errors in its content, and we’d be ready for males (make matches or matches) and group, which is to recognize that some parts of the address constitute a grouping, for example, if you have equal two different directions customers who are related in some way (they are brothers or married), they form a group.