A good information governance program has the ability to apply policies, procedures, processes, and controls to all enterprise information, whether it's corporate records, business data, email, or office documents. Check out our blog to help you make the most out of your information governance initiative.
When governing information, it works well to identify and bundle rules (for legal compliance, risk, and value), identify and bundle information (by content and context), and then attach the rule bundles to the information bundles. Classification is a great means to that end, by both framing the questions and supplying the answers. With a classification scheme, we have an upstream “if-then” (if it’s this kind of information, then it has this classification), followed by a downstream “if-then” (if it’s information with this classification, then we treat it this way). A classification scheme is simply a logical paradigm, and frankly, the simpler, the better. For day-to-day efficiency, once the rules and classifications are set, we automate as much and as broadly as possible, thereby avoiding laborious individual decisions that reinvent the wheel.
This post comes from Alexander Goerke, CEO and Founder at Skilja. The original article can be seen here.
Automatic, context based classification for mailrooms has proven to generate significant ROI and acceleration of processes in the last few years. But we have also seen failures and disappointments. I have managed and monitored many of these projects in the past and would like to share 10 golden rules derived from my experience to make a mailroom classification process successful.
I hope you find these practical tips useful for your projects. Please leave a comment below to share your thoughts.
If you want to learn more about auto-classification, please check our regular blog onwww.skilja.com
Digital information is doubling in size within your organization at a minimum of 24 months. The cost to finding lost information and the cost of eDiscovery are also on the rise. Other problems with information sprawl are the value of analytics when there is too much noise (obsolete information) and the fact that although the price of a hard drive may continue to drop, the cost of full time employees, electricity and other data center related costs will continue to increase.