Content engineering is the practice of organizing the shape, structure, and application of content. Content engineering is broken down into seven primary disciplines: model, metadata, markup, schema, taxonomy, topology, and graph.
The Semantic Web is the knowledge graph formed by combining connected, Linked Data with intelligent content to facilitate machine understanding and processing of content, metadata, and other information objects at scale.
The era of customer experience management (CEM) is well upon us. The customer is calling the shots and technical communicators are scrambling to deliver the goods across an expanding buffet of audiences, devices, and channels.
Content engineers are often already staffed within the organization. They are those who naturally connect to the content strategy at a visionary level and can map that strategy to all the enabling technologies. Often, those who are best suited for the role of CE are the internal talents with the most cross-functional familiarity. Yes, building a CE practice can begin with existing staff. Simply start by looking at the talented individuals within the organization.
Major trends in product innovation software include Internet of Things (IoT), 3D printing, cloud computing, mobile computing, augmented reality, and social collaboration. The strategies of leading PLM vendors are in flux as they try to evolve from older legacy-based custom approaches while more commercial off-the-shelf applications (COTS) for PLM are gaining favor.
In this article in [A]'s series on Knowledge Management, we focus on the steps in creating a Knowledge Management System through a hybrid case study. In our white paper, "Creating Content-as-a-Service through smart Knowledge Management practices," we discuss why Knowledge Management's biggest value is still to be fully realized. Until we move beyond the traditional siloed workflows, full access to effective knowledge will remain impaired.