Content and Information Modeling
Applications that rely on structured data depend on a Data Model to ensure things work as planned. Unstructured data applications such as Intelligent Content depend on schemas, taxonomies, and related models to fulfill the same role.
While most organizations have extensive experience modeling structured data, the directive to do the same for content is new and there are limited resources to guide the way.
One of the primary differences between structured and unstructured data modeling is the difficulty relating the models to an effective user experience. In the decades since computers were introduced, users have become accustomed to working with structured data. For example, there are seldom (if ever) complaints that an address must be entered into the appropriate fields of a form. Other than sections, titles, and a few other major document components, users do not adapt to constraints as easily with unstructured data. When typing an address in the middle of a paragraph we are conditioned to think of it as no different from the rest of the text.
Reconciling rigorous enterprise data requirements with usability that enables deployment is not an easy task. However it is essential. Without the proper model, the content cannot be processed. Without the proper experience, the content cannot be created. Either way, the business does not receive the required value. For a long time these two imperatives seemed impossible to reconcile, and many Intelligent Content (or Structured Authoring) initiatives failed. The situation is different today and there are best practice approaches that ensure content is usable and easy to create. The Contelligence Group pioneered many of these best practices in successful projects that have become a benchmark for the industry.