Storage data profiling is an essential system for the Information Technology professionals as it can be used to reclaim storage capacity that is valuable as well as in controlling costs in the data centers of IT departments (Gonzalez, 2013). Additionally, data profiling helps IT professionals deal with networks that are clogged due to the ability to increase the data capacity.
The software for the data profiling can process all types of files that are not structured and document the files by developing an index of metadata that is searchable through what exists, its location, the owner of the metadata index, and the last time it was accessed (Gonzalez, 2013). Additionally, the search can be done by finding out the key terms in the metadata index, developing a 1% original data’s index after retrieving the data from all the files and emails on a tape set or server.
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Also important is that the data profile conveys knowledge of email and files that are not structured in a comprehensive manner when the profile is being used (Gonzalez, 2013). As a result, it is possible to make decisions regarding the disposition of the data due to the exposure of the mystery data and the formation of the retention of the policies.
What questions are asked when this is done?
What are available on the file system?
Which data require the use of certain applications?
Which data should be archived?
Which data require efficient management?
Are there several copies of the data that is being accessed?
An Example of a Business that would benefit from Storage Data Profiling
Storage data profiling would benefit the businesses that deal with the exploration of gas and oil because in such business, the data workflow increases at a rapid rate and thus it becomes very difficult for the recovery and protection of valuable information (Gonzalez, 2013).
The type of business that would not use this method of tuning their storage network
A business that would not use this method of turning their storage network are the small scale businesses that do not need to process large data like a small food kiosk.
Reference
Gonzales, M. L. (2003). IBM data warehousing: With IBM business intelligence tools . Indianapolis: Wiley.
