Rise of the Machines: The Role of Text Analytics in Record Classification and Disposition .
The disparate nature of unmanaged data repositories hinders an organization’s ability to purge unneeded data because the size and nature of the repositories make it cost-prohibitive to evaluate the data for disposition.
However, if shared file data repositories continue to be left unmanaged, organizations will have vast wastelands of unstructured electronic information.
They will be unable to distinguish among data that does and does not need to be retained, and they will be locked into the ever-increasing cost of storing and maintaining the data in order to comply with their legal obligations.
Accurate classification of electronic information, or identifying and associating information types with electronic data, is essential to making the appropriate retention and disposition decisions. To reduce volume, organizations must be able to determine what type of data they have to understand what data must be retained and what data are no longer useful. And reducing the volume of data is one generally accepted approach to reduce data’s primary risk – the high cost of finding, preserving, reviewing, and producing it for litigation.
Accurately classifying data allows organizations to accurately retain it, place legal holds on it, and make reasonable disposition decisions about it, thus helping to minimize the significant legal costs and risks associated with continuing tostore it unnecessarily. But, because of the seemingly complex, costly, and insurmountable task of classifying many years worth of unmanaged data, little has been done to address the problem.
Continues @http://content.arma.org
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