Automatic Classification

Defensible disposal of unstructured content is a key outcome of sound information governance programs. A sound approach to records management as part of the organization’s information governance strategy is rife with challenges.

Some of the challenges are explosive content volumes, difficulty with accurately determining what content is a business record comparing to transient or non-business related content, eroding IT budgets due to mounting storage costs, and the need to incorporate content from legacy systems or merger and acquisition activity.

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I had few questions about my posts about automatic content classification. I would like to thank my blog readers for these questions. This post is to follow up on those questions.

Organizations receive countless amounts of paper documents every day. These documents can be mail, invoices, faxes, or email. Even after organizations scan these paper documents, it is still difficult to manage and organize them.

To overcome the inefficiencies associated with paper and captured documents, companies should implement an intelligent classification system to organize captured documents.

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An ability to find content in a content management system is crucial. One of main goals of having a content management system is to make content easy to find, so you can take an action, make a business decision, do research and development work, etc.

The main challenge to findability is anticipating how users might look for information. That's where categorization comes into play. The quality of the categorization of each piece of content makes or breaks its findability. Theoretically, good tagging will last the lifetime of the content. You would think that if you do it well initially, then you can forget about it until it is time to retire that content. But reality can be very different.

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The inability to identify the value in unstructured content is the primary challenge in any application that requires the use of metadata. Search cannot find and deliver relevant information in the right context, at the right time without good quality metadata.   An information governance approach that creates the infrastructure framework to encompass automated intelligent metadata generation, auto-classification, and the use of goal and mission-aligned taxonomies is required. From this framework, intelligent metadata enabled solutions can be rapidly developed and implemented. Only then can organizations leverage their knowledge assets to support search, litigation, e-discovery, text mining, sentiment analysis and open source intelligence.   Manual tagging is still the primary approach used to identify the description of content, and often lacks any alignment with enterprise business goals. This subjectivity and ambiguity is applied to search, resulting in inaccuracy and the inability to find relevant information across the enterprise.   Metadata used by search engines may be comprised of end user tags, pre-defined tags, or generated using system defined metadata, keyword and proximity matching, extensive rule building, end-user ratings, or artificial intelligence. Typically, search engines provide no way to rapidly adapt to meet organizational needs or account for an organization’s unique nomenclature. Read more

For records managers and others responsible for building and enforcing classification policies, retention schedules, and other aspects of records management plan, the problem with traditional, manual classification methods can be overwhelming.   Content needs to be classified or understood in order to determine why it must be retained, how long it must be retained, and when it can be dispositioned. Managing the retention and disposition of information reduces litigation risk, reduces discovery and storage costs, and ensures that organizations maintain regulatory compliance.   Classification is the last thing end-users want (or are able) to do. Users see the process of sorting records from transient content as intrusive, complex, and counterproductive. On top of this, the popularity of mobile devices and social media applications has effectively fragmented the content authoring market and has eliminated any chance of building consistent classification tools into end-user applications. Read more