Well educated, motivated knowledge workers don’t want to be forced to take on a dreary task like dividing documents into categories. And they see some in the future with a classification training process.
Typically, I provide a boring “It depends” answer, and dive into the variables that come into play. In the end, there’s a big range, depending on how well your business policies are defined and in turn captured in your business applications like records management systems. I’ll post more on the topic in the future. We can automate this process and make the classification training process less burdensome itself. But bottom line, there’s going to be some necessary dirty work.
But my colleague, Michele Kersey, provided a great answer to a customer this week on this question that cut to the heart of the matter. The quote that stuck out in my mind was “Content classification scales out the human element.”
The phrase struck me for two reasons. First, it nicely encapsulates the difference between rule based classification and the more advanced, context-based classification methods. Rule based methods force humans to try to classify content under the constraints of binary logical. Lots of IF-THEN statements. Sure, computer science majors can think that way, but John in marketing and Jane in HR don’t think in that manner. Embarking on rule based classification project exposes the classic IT/LOB gap.
By taking an approach to content classification that relies on a training-based approach (using example documents to “teach” a classification engine), training the system with samples provided and/or validated by those business stakeholders, you are encapsulating the human element in your classification logic. A human has written the documents that are being used to train the system. And a human is choosing these “typical” documents.
This training process takes effort, but the scale and scope of that effort pales in comparison to the effort you’d need to harness otherwise – which is the second reason I liked Michele’s quote. Automated classification using training based methods scales out the effort your organization has put into training the system. Yes, it takes effort to train a classification system, but you’ll earn back savings on that effort in the following weeks, months and years that the human-based logic is applied and re-applied to answering categorization questions that your workers would otherwise need to handle.
Train the system once, with a small set of workers, and the same work that those workers had to do manually, will be executed automatically over and over and over again throughout your organization. Do some work to plant the seed of classification and watch it eliminate repititive tasks throughout your organization.