Contelligence is Intellligent Content without the Herculean feats!
Operational systems and databases contain information that is routinely delivered for customer self-service, mined for business intelligence, and monitored for governance and risk mitigation. This is possible because the data has been structured in such a way to enable automated processing.
Various forms of written content created by knowledge workers across the organization present a different story. These represent a type of unstructured data, which experts agree represents 70-80% of an organizations actual information assets. The term unstructured, in this context, indicates that the data is difficult or impossible to process automatically. Unstructured data inhibits or stops automated processes for customer engagement, business intelligence, governance, and more. Intelligent Content represents the technologies that allow us to make improve unstructured data so that it can more easily be processed automatically while Content Intelligence represents technology that makes computers smarter and more capable of understanding unstructured data that has not been improved.
Intelligent Content and Content Intelligence are obviously two sides of the same problem their respective technologies have been developed and implemented almost completely in isolation. Readers familiar with these industries will recognize the common questions faced by organizations:
- Start by authoring semantic XML or automatically convert standard Word documents?
- Manually add semantic tags or automate entity extraction and metadata?
- Show XML markup when authoring or accept a lower level of “intelligence?”
Intelligent Content and Content Intelligence are so mutually exclusive today that their respective vendors normally compete for the same customer business. That’s right; compete not collaborate. The “aha moment” we are promoting is the vision of these two technologies working together. Here's why:
By itself, Content Intelligence assumes that we must place the burden completely on technologies that mine unstructured data which makes the problem extremely difficult and far more complex than it needs to be. In many cases the resulting problem is too complex to be solved by currently available technology.
In the same way, Intelligent Content by itself assumes that we must place the burden completely on the users who create the content. This results in usability and change management problems that are far more disruptive than they need to be and that can lead to solutions that sound good but do not work in practice.
When Intelligent Content meets Content Intelligence the user becomes responsible for a much smaller amount of "data improvement." At the same time mining technologies do not have to be as sophisticated because even a small amount of data improvement dramatically reduces the technical challenge.
When Intelligent Content and Content Intelligence work together the result is that unstructured data is transformed into meaningful and useful information that can support automation. Should we really care which is being improved: the data itself; or the technology that mines it?
Contelligence represents the full scope of technologies that enable automated processing of unstructured data. This includes technologies designed to help knowledge workers create content that contains additional structure and semantics and emerging technologies that improve the ability to process unstructured data in its native form. Combining these two technologies provides improvements in usability that are needed for enterprise deployment, along with the rich structure and semantics that are needed to empower enterprise automation.