The holy grail of records management is automated classification – taking people out of the record retention process and having computers automatically decide which electronic documents are which record types. Quite frankly, in the past, I’ve had my doubts on auto-classification of records, but maybe it’s time to take a new look.
There are problems with traditional record retention approaches for electronic information. One traditional strategy is to have all employees manually classify all electronic records, but this does not work very well. Electronic information is growing about 30 percent to 60 percent per year. Deluged with electronic documents, it takes a lot of time for employees to classify them and many employees tend to put it off. In assessments across a number of companies, we have found these employee-driven, manually-oriented retention programs experience fairly low program compliance, especially for electronic documents. This is a big problem as electronic records typically constitute 90 percent of an organization’s records.
The other approach we often see is implementing a monolithic retention strategy, for example saving all email for seven years. As only a fraction of emails are business records, this “save everything” strategy tends to drive significant over-retention, which is not a good thing. Furthermore, monolithic retention policies still don’t ensure compliance, as a very small yet material number of critical electronic records need to be saved longer than the monolithic retention period. These records will be under-retained, driving noncompliance.
The record management discipline can benefit from the inroads being created by predictive coding in e-discovery. Predictive coding technologies allow first pass review of documents, doing a pretty good job of determining which documents are relevant. Even with predictive coding, people today still need to re-review the computer’s work, but it does make the job easier. The same search and identification technologies currently being used in e-discovery are making their way into record retention products.
Questions arise whether auto-classification is compliant. There is very little case law validating predictive coding, and even less around the sufficiency of automatic classification for records management. Nevertheless, I think we are approaching the point where these auto-classification technologies do an imperfect, but better job of records classification than traditional methods. As record compliance is measured by how well you actually follow your policy, if auto-classification produces better end results than manual classification, one could argue that it is more compliant.
Even with the advent of this new technology, there is no “easy button” for auto-classification. While auto-classification holds the promise of decreasing the amount of time employees spend classifying records, creating a compliant and defensible program actually requires more work in the form of updating policies and creating new processes. These include creating clear, content-based retention schedules, identifying exemplar documents, creating testing programs as well as audit and review, and ensuring overall transparency. Auto-classification will mean doing more work upfront to reduce the amount of work employees will do.
While still in its infancy, auto-classification holds promise of better retention with less effort by employees. I have been a skeptic, but now I think it’s worth taking a look.