In a couple of blogs over the last month, I have mentioned the possibility of Predictive Information Governance (PIG) - automated information governance based on unsupervised machine learning technology. Just like the name implies, unsupervised machine learning (computers teaching themselves) removes the iterative manual training cycles of the learning process and allows the system to automatically categorize, store, apply retention/disposition, and manage content as it flows within the system.
In past blogs I have discussed the possibilities of machine learning and information management, i.e. predictive information governance (PIG) and auto-categorization to automate the management of electronically stored information (ESI). One of the challenges the information management industry continues to face is how to extend this machine learning capability to audio and video content.
For centuries, records/information managers have had to rely on end-users to take the first, second, and third steps in information governance which are:
- Make a decision on a document as to whether it should be retained
- Decide how long it should be kept (retention period)
- And actually take the step to move the document somewhere for safekeeping and management.
Over the last 15 to 20 years, many companies have marketed and sold “records management systems” that would supposedly make information management much easier. However, these systems didn’t address the 3 points above; the reliance on end users to initiate the process and to make decisions on the importance of the content.