Classification is the key to successful information governance. It aids findability, content migration, legal hold, information privacy and protection, records retention and disposal.
But classifying, by manually capturing descriptive metadata into information systems, is costly and time-consuming.
The volumes of data are now so huge that the only way to ensure consistent and complete metadata is through auto-classification – the AI process where documents are classified (tagged with metadata) by machine.
its’ about working smarter, not harder.
Business users just don’t have time to classify the high volumes of content that they manage. Even an expert would take 2-3 years to process what an autoclassification engine can process in one night. Autoclassification enables business users to get on with their day jobs.
Once you start the process of automated crawling and classification – you can ensure nothing is missed overlooked.
Autoclassification delivers consistent metadata, because the rules that are established for auto-classification are applied consistently for every piece of content. It is this consistency that makes search work, enabling the refiners and filters in SharePoint and other search engines.
Autoclassification is a knowledge based process. The more knowledge your feed into your engine, the better it will classify. Finetuning your taxonomies and associated rules leads to high levels of precision, in many cases 85-95% for specific taxonomies.
Taxonomies and ontologies are the foundations of an autoclassification engine.
If you read this Webopedia definition, you could easily think that the machine can make informed decisions about classification just by reading the document. In reality, the classification is dependent on the knowledge that you build into the autoclassification engine.
Taxonomies and ontologies deliver the knowledge against which rules are built and “teach” the engine how to understand and categorize content.