Each organization has a broad spectrum of content that contains valuable, reusable information. Historically, technical content like manuals and design documents has been very structured, often following rigid templates, and static—often only altered during product or service updates. However, as collaboration has become more real-time, valuable information is being shared in dynamic forms like instant messaging, email, and telephone conversations.
KCS articles (just-in-time support content) are typically somewhere in the middle of this spectrum. KCS provides a methodology and set of practices for consistently capturing information in a way that is both structured enough to be useful, and dynamic enough to suit the rapidly changing environment of technical support. In addition to drawing from many content sources and creating a context-sensitive knowledge base for daily use, KCS processes generate new material that may reference existing content and feed into other business content like product designs, marketing plans, field training, and documentation.
We will start our discussion on content health with some general considerations and then describe the practices and techniques.
Knowledge has value - if we capitalize on it! Our goal is to maximize the value of what we learn in the support process. Following are some of the key content best practices that drive the value of what we learn to the whole organization:
Implemented in everyday actions, these core ideas enable the organization to realize the full value of the knowledge base. The next few sections provide specific techniques to help adopters understand the KCS article structure and maintenance processes for optimizing the value of the collective experience through the knowledge base.
One of the by-products of the KCS practices is improved relevance in search responses. If we are only finding relevant KCS articles when we search, we don't have to worry about how big the knowledge base is or if it contains "old stuff." In fact, we could make the case that if needed, the value of the seldom-used old KCS articles is higher than the set of frequently used KCS articles, since the knowledge about the current KCS articles exists in the Support Analysts' heads. Imagine an issue arises about an older product, demanding knowledge that most people have forgotten (or those who knew it have left the organization). Having access to the older, seldom-referenced knowledge can be of tremendous value.
However, findability is a common problem as organizations grow their knowledge base. Archiving old articles treats the symptoms of findability, not the cause. Relevance is the key. Relevant responses to a search are a combination of the search technology and the content. The practices described in the KCS Solve Loop address the content issues. If we are having findability problems, is it because of structure, context of the content, or not enough information in the environment statements to distinguish one article from another? The search technology issues are covered in the Workflow Integration section.
Some have tried to improve relevance by reducing the number of KCS articles in the knowledge base. This reduction will compromise the completeness of the knowledge. The greatest value from the knowledge base comes from the complete reflection of the collective experience.
This is not to say that knowledge base cleanup should never be done. If we have search technology that returns irrelevant content, it is prudent to trim and normalize from time to time. We will discuss knowledge base cleanup later, in the context of reusing legacy content and Evolve Loop processes.
Deciding what to do with non-KCS knowledge content, multiple knowledge repositories, and disparate document management systems can become very challenging and time consuming. The members' experience shows that 90%-95% of what is in the old knowledge base will never be referenced. And more importantly, the legacy content is most often not structured or expressed in the context of the customers. A mass import of legacy content will significantly reduce findability. The investment of time and money to clean, write scripts, and move legacy knowledge is not worth it.
A better choice is to create a "demand based workflow" process. Let customer demand focus our attention. Pull forward only the content being used, implementing the structure of KCS. Following are some considerations that support a demand-based migration:
KCS is a demand-driven system; this means we should not add content in the absence of demand. Just as we should not try and anticipate the future value of a support experience (if it is worth solving, it's worth saving) we should not load articles into the knowledge base in anticipation of demand. There are a few exceptions to this rule where having an article about a known pervasive issue would have value. The general rule of "don't add articles until someone asks" raises a problem when introducing new products. How do we prime the knowledge base for them?
Perhaps the worst thing we can do is have development or engineering write articles about the new product - those will be in the context of how the product was designed and built, not how customers will use it and not how it will break. We can, in fact, capture information about new products in a useful context. As a new product is going through alpha and beta testing processes or user acceptance testing we should capture those experiences in the context of solving real problems.
During product beta cycles, we pay special attention to creating content in response to the demand of beta testers, whether or not their feedback results in a normal entry in the incident management system. Generally this early release content should be in a Draft or Approved state (not visible to customers) until they have been reused to solve a customer issue, and, as a result, updated with the customer context and then Published for customer use.
KCS articles can also be pre-populated in the new knowledge base during the KCS training and pilot phase. Students bring their top ten current issues to training and use these issues as examples during the training. We structure and enter the knowledge according to the KCS content standard. As these KCS articles are reused in the support process, they should be modified to include the customer context.
The majority of the Consortium members operate in global, multi-lingual, multi-cultural environments. Both the growth markets revenue and the sources of support resources are in emerging markets where the language and culture are different from those of the home office. Many companies in the high tech sector have standardized on English as the language for business, even though they are based in non-English speaking countries and serve markets and have employees in non-English speaking parts of the world. This presents some big challenges when it comes to sharing knowledge on a global basis. As best we know, there is no easy answer. Cultural sensitivity and language translation are both difficult and expensive to maintain.
KCS as a methodology does not address cultural sensitivity but KCS does offer some relief in the area of multi-language support. If an organization adopts the content structure and style recommended in the KCS methodology of "complete thoughts, not complete sentences" then this creates the following benefits in a multi-language environment:
The use of machine translation has increased dramatically over the past few years. It is not perfect but it is gaining acceptance as sufficient for support content. Following are some examples of how companies are leveraging machine translation:
For more information on machine translation visit the Translation Automation Users Society at www.taus.net.
KCS articles in the knowledge base enhance Analyst training by allowing Analysts to find information that they may not have known prior to their search. For example, an Analyst might get trained on a product and then not have any contact with that product for several months. When a call comes in, the Analyst can search the knowledge base and find information that they forgot or may not have learned. This is just-in-time training.