From its inception, the Darwin Information Typing Architecture has represented a revolutionary approach to content. Rather than, as Precision Content puts it, seeing written content as a "monolithic, linear flow of information across pages," DITA emphasizes content as a set of smaller, reusable, self-contained components. This architecture allows hypertext-like topics to be warehoused, aggregated, and distributed to meet the ever-changing needs of those who consume content throughout the world.
"DITA allows authors to reduce, reuse and recycle data into new documents."
Several major innovations got their impetus from DITA. The role of the author shed those tasks focused on layout since DITA-based writing uses format-independent XML. Instead, authors became content curators, working closely with subject-matter experts to ensure the highest quality and accuracy of the data found in technical writing. Publication became fully automated as unformatted XML was paired with pre-defined XSL style sheets to deliver beautifully formatted content instantly and on demand.
However, it's DITA's optimization for smaller topics that drives another important innovation: reducing, reusing and recycling data into new documents. In this way, DITA takes on an almost "green" lifecycle, mirroring, in a sense, the global environmental and pollution crisis that drives our collective focus on reducing, reusing and recycling the things we produce every day. Indeed, the glut of information available today means we are putting unprecedented amounts of data and content into the world, clogging data lakes and making content creation wildly inefficient. The Association for Information and Image Management reports that nearly 50 percent of an author's time is spent just searching for information – well above the roughly 5 to 15 percent of time spent actually reading the information.
DITA renders the need to create new documents out of whole cloth because content is constructed from reusable and verified data. Also, DITA's built-in semantic information dramatically improves content searching and research efficiency, virtually eliminating the endless sifting through stacks of potentially irrelevant data.