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.

A typical customer experience problem state

At [A], working with a number of corporations handling massive enterprise data, we’ve architected Knowledge Management solutions to access this vast store of information. The following description is a generic amalgamation of several clients that provide a typical “before” picture of how KM is handled prior to new methods implemented. 

One client had been using Oracle Knowledge Management (OKM) for several years. However, various Subject Matter Experts (SMEs) and authors in different departmental silos were inconsistently creating information:
 
  • Content creators would create a new article in Microsoft Word.
  • The author would have to strip out the formatting, and manually copy and paste the content into the OKM.
  • When content was extracted from the OKM, it would be used to create one specific document-length deliverable, and new formatting was applied.

In this instance, the knowledge management system was being underutilized and effectively treated as a glorified document management platform.

Challenges:

  • KM content acquisition was extremely manual
  • Duplicate content was created
  • Processing was slow because knowledge was captured in large document fragments

The target solution

Interviews with multiple stakeholders and team member collaboration led to the creation of a Content Intelligence transformation roadmap for moving forward. The main goals were:  
 
  • Simplify authoring
  • Remove inconsistencies
  • Content enrichment via metadata-compliant content with newly established, consistent taxonomies and ontologies 

The system architecture for Knowledge Management would shift to a cloud-based, Content-as-a-Service (CaaS) model focused on creating consistent intelligent content, and the process as much as possible. 

Direct benefits from Structure and Semantics

Intelligent content powers Intelligent Customer Experiences. Those contextual and valuable interactions that drive behavior around an organization's knowledge.  [A] helps orient teams around the principles of Intelligent Customer Experience, and then helps them to build the systems and processes that engender those experiences in an organized way.  In such engagements, [A] helps to engineer a way for clients to easily enrich their content to identify structure and semantic patterns across the content domain.

Of course, structure and semantic patterns don’t work in isolation. Delivering Intelligent Customer Experience requires a symphony of software connected with people and process. On the software side, every intelligent content solution needs to include:
 
  • A component based content management environment
  • A semantics system

Semantic systems can integrate with tools and content management systems, even via Word templates, to empower authors to inject semantics directly into the content. Thus, they can apply this externally to the content-based IDs or to the component content management system (CCMS), and can add semantics to topics, even using managed word lists. With the addition of metadata, controlled word lists or controlled vocabularies can be applied more efficiently.

This new method of content enrichment makes the content more findable within the KM itself. So new content is also findable for other authors, helping to avoid content duplication. 

When content is published downstream, it takes all of that metadata and semantic data with it, so the content can travel along through the ecosystem and be transformed into whatever form is needed. It also ensures that the content is queryable. Customized, on-demand delivery can now be based on personalization, thus providing a much more intelligent solution, both internally and externally. 

Tangible benefits in practice

  • Internal: An effective CaaS-based Knowledge Management solution can save time for internal team members and help break down silos across many workgroups, markets, and businesses. Tagging mechanisms based on intelligent content management can easily connect staff with articles, white papers, tips and tricks, public communities, and experts based on their specific needs and interests.This can deliver huge time-savings: for instance, account managers and sales engineers can spend fewer hours per week searching for information on an emerging industrial sector or the results of the latest case-study . 
 
  • External: Because the solution described here makes knowledge and content more findable, external consumers can now find knowledge without assistance from customer service. This can lead to massive savings through service desk call deflection. The future enterprise is being built with modular content. Innovative teams are enriching critical knowledge assets into structured, semantic content forms, then connecting these modular components into Content-as-a-Service (CaaS). These changes in enterprise knowledge and content management are here now and rapidly accelerating. Leading organizations realize the impact of orchestrating intelligent customer experiences and are investing in these practices and roles.
 
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Ready for more?

Check out  [A]'s keynote on Intelligent Customer Experience, and view the trove of resources in the [A] Treasury for even more information about content intelligence, and how the practices of content strategy, content strategy, content engineering, and content operations fit into a content intelligence framework.