Metadata. This is a fundamental digital building block of the Knowledge Economy. Metadata describes knowledge objects, and is used to support the indexing, search, discovery, retrieval, and use of those objects. If one thinks of the analogy of a web-based store, the metadata is analogous to the online catalog of products and the knowledge objects are analogous to the products themselves.

Metadata adds descriptive, technical, administrative, and structural value to data and information. Metadata assists in clustering related information resources and in providing the capacity for "chunking" information for easy reuse, interoperability, transaction, archiving, and preservation. A digital object that is not enriched with metadata cannot be used effectively in contexts for which it was not designed.

High-value digital repositories require well-described and organized metadata throughout the collection. Print collections can rely on a single front-cover or catalogue descriptions of content and context. Digital collections require extensive tagging that enables an e-book or journal article to be segmented into modular, durable, and independent chunks. The tradecraft of achieving metadata chunking in a cost-effective manner will be one of the critical emerging competencies of the e-Knowledge Industry.

Metadata is about both "how" and "what." The initial focus of working groups defining metadata standards such as the IMS and Dublin Core was how to describe characteristics of information through metadata fields and related subcategories. The what of metadata is twofold — deciding what fields among the dozens defined by these metadata standards are necessary for a particular market or application (e.g. Dublin Core), and how to specifically identify the subcategories within the identified metadata fields to make sense in different markets (e.g. postsecondary education, K-12, corporate training).

This involves a narrowing of options and providing a sort of "pull down" menu for many categories (particularly those related to subject area) so users can understand easily what the descriptors mean and objects can be more easily tagged. For example, the community of practice for physics could determine the subcategories appropriate for learning objects in the discipline. These would differ subtly from subcategories used in nearby disciplines and sub disciplines. Descriptions of concept domains, like physics, with controlled and specified vocabularies, meanings and relationships are called ontologies.

The early focus of metadata description has focused on technical, administrative and content-fixated description of information. A key challenge in the development of the Knowledge Economy will be to develop metadata standards to enable the flexible economic exchange of information objects. Knowledge objects will become substantially more complex, combining content, context, and best practices and requiring complex mechanisms for recognizing value. A sort of "matrix of economic value statements" will emerge as an essential component of metadata. This will enable the value webs that will develop for each market. But the most difficult economic challenge is to drive down the cost and price of metadata through dynamically generated knowledge objects, autotagging, and sophisticated tradecraft. Routinely and economically creating ontologies and metadata will be an important capability for the Knowledge Economy.

Content Management. It is now over a decade since the first standards began to emerge in the field of computer-supported learning. The Aviation Industry delivered the first such specifications; what’s more, they’re referenced today in an updated form within the SCORM (Sharable Content Object Reference Model), developed by the US Department of Defense Advanced Distributed Learning (ADL) initiative.