Data classification refers to the act that involves placement of an organization’s data into different categories (Markham, 7) that usually determine the level of control within the organization. It serves to protect the organization’s data against theft as well as maintain its integrity. This will influence its importance within the organization. This report provides an insight into some of the best practices involved in data classification. The efforts involved by corporations in securing of data must be in line with, the set down regulations, tier storage as well as be able to meet the provisions within the law (Reed,1). The dependence on traditional modes of classification are proving inconvenient as they fall short on the visibility of the content.
New techniques of data classification have arisen to fill in this void such as the Information Classification Management. It is preferred in the sense that they offer higher performance standards such as metadata parsing. There are different classes of data such as public data, that is open to the public and confidential data that is restricted from public viewing. These require sensitive handling techniques such as the movement of data into appropriate repositories can only be done with the help of policy engines that comprise classification of data as well as tagging of files. Before this is known, the data values have to be identified and policies established while file stubs left. Conclusively, each organization has come up with its own mode of classifying its data depending on the level of importance it has attached to the data in question.
REFERENCES
Reed, B. (2007). Data Classification- Best Practices. Network World.
Retrieved from: http://www.networkworld.com/news/tech/2007/012207-techupdate-data-classification.html/
Markham, B. Data Classification and Privacy: A foundation for compliance. Retrieved from :
http://www.oit.umd.edu/Publications/Data_Classification_ Presentation_022908.pdf
