[Guest Post] “High Octane Documents: Don Day’s DITA Model”, by Don Day
We held an intriguing webinar on June 12th with guest Don Day. In this blog, Don Day reviews some of the concepts illustrated and shared in this webinar.
You may view the webinar recording of this session here: What “Model” DITA Specializations Can Teach Us About Information Modeling.
Webinar Minute Markers
For your convenience, the follow lists give minute:second markers into the webinar recording when Don Day covers key points:
Goals of Information Modelling
Minutes: a building as an example of a model
What an Info Model promotes
Useful data types for processing
Association of business rules to the content
Comparative review of DITA Open Toolkit plugins with a “CSI” methodology
Example of Music plugin
Msgref plugin example
msgRef DTD fragment
FAQ plugin example
Enote plugin example
A Design Approach for DITA
Smaller project ideas for testing on your own
Considerations for your own projects
Skills to obtain and resources
High Octane Documents
Your documentation drives your business in the sense that without good documentation, most products just don’t get out of the driveway very well. Car engines generally perform better with refined gasoline that has a higher octane rating. In a sense, there is also a Content Octane Rating that indicates whether content has the metadata and structural refinement necessary to keep the business engine running smoothly under load.
The lowest grade content is a conventional text file–one having just words with whitespace. It conveys information, but the lack of structure means that you have to provide external intent to do things with the content (to style a title as a heading or to use it as a link, for example). We’ll give this version of content a COR (Content Octane Rating) of 85, in keeping with the lowest generally marketed gas octane rating.
At a COR of 87 are text files that include basic styling markers that the rendering engine can use to drive the appearance of the content. These formats include HTML and Markdown (or any of the so-called family of “lightweight markup” systems). The markers in this grade of content make it easy to find and reuse headings or to generate collections of things that have the same stylisms. You can programmatically search for all things that are italicized, for example, but you have no way to separate the intent of that style, whether it was for a citation, for a foreign phrase, to indicate variables, or just for emphasis.
A COR of 89 recognizes the use of markup that names these fragments for what they are rather than for their appearance. Now our content engines can perform queries to pull out “things of a kind”: quotes, citations, variables, and more. Our content engine is now able to make use of the intent of the markup: it is aware of the meaning of bits of the content.
A COR of 90 might represent data models that describe the complex organization of data, such as the structure of a book or journal or the formal, repeating parts of a recipe.
And at a COR of 91, we come to the ultimate level of content engine performance: Content that is sufficiently descriptive of the rules of the business so that it can actually be used to program the production tools, to engage the required reviewers and signoffs for a release, to define the metrics by which the rest of its content should be rated for quality assurance. Content at this level is intimately part of the way the business operates.
It drives the business, or, at least, the documentation side of the business.
It is a corporate asset of the highest possible COR – truly High Octane Documentation.