Key Players and Capabilities

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Knowledge Domain Experts (or KDEs) are responsible for facilitating and/or conducting the Knowledge Domain Analysis. This is an important part of the continuous improvement element of the KCS Evolve Loop. Initially, the KDEs seek to understand what can be learned from the collection of articles created and used in the Solve Loop workflow. As the organization matures, the analysis expands to include the activity in self-service, communities, and social media. The goal is to have as complete a view as possible of the requestor activity and experience associated with a domain.

A successful KDA program can produce amazing benefits. However it does require executive support in the form of funding for the program and allocation of time for the KDE activities, as well as executive communications and expectation setting with other departments. The KDA program is a new role for organizations who are doing KCS, and it highlights and leverages the opportunity for knowledge workers to make a significant contribution to overall organizational learning and improvement. Oftentimes, executives must sell the value to their peer executives as many of the corrective actions necessary to realize the benefits will have to be taken by departments outside of the KCS implementation. For example, a shared understanding of the value of the Knowledge Domain Analysis between support and development executives will greatly improve the organization's ability to identify, communicate, and execute on root cause analysis and removal.

Knowledge Domain Experts look after the health of the knowledge domain from the point of view of the content. They help maximize the benefits of KCS through the analysis of that content. They usually focus on a collection or domain of content. KDEs must have both technical expertise in the domain and a profound understanding of the KCS principles and practices.

KDE responsibilities include:

Ensuring efficient and effective problem solving by the support team.
Applying expertise to perform trend analysis and finding significant patterns in the data using techniques such as the New vs. Known Analysis and data mining tools.
Feedback management and analysis of surveys and flagged articles.
Assisting in the development and maintenance of the knowledge base and the workflow, including the evolution of the content standard and the problem-solving process.
Developing and analyzing reports on key metrics that reflect the value of the knowledge base, such as article reuse rates, self-service success, improvements to resolution times, and the positive impact resulting from the corrective actions taken on pervasive issues.
Helping to communicate the value of the knowledge base and providing knowledge workers and management with visibility and feedback on the impact of people's contribution.
Advocating for changes necessary to maintain the knowledge base as an effective tool for achieving business objectives.
Identifying pervasive issues and conducting or facilitating, root cause analysis on those issues.
Building the justification or business case to promote corrective actions to address pervasive issues.
Influencing the owners of the offerings (product management and product development), processes, and policies to eliminate pervasive issues thereby improving the customer experience and loyalty as well as reducing support cost.
Identifying opportunities to tune the search engine and search engine optimization
Participating in the KCS Council.
Analyzing community and social media activity relative to the domain.

KDEs as Facilitators

The responsibilities of the KDE can be quite broad. While they may do some of the analysis themselves, they may also convene small groups of people to help with some of the tasks. For example, convening a small group of people to design a complex resolution path. The group should include a few subject matter experts as well as representatives from the audience that is expected to use the resolution path.

Another key resource for the KDEs is access to Data Scientists. Data Scientists understand the various machine learning and modeling methods and techniques that are used to automate the classification of knowledge (patterns and clusters) and create prediction, recommendation, and optimization engines. Digital automation and use of these sophisticated machine learning and modeling techniques has tremendous potential to relieve the burden of manual analysis and speed up the analysis process. However, Consortium members have found that if you automate crappy processes, you get really fast crappy processes, and if you analyze crappy data, you will only get crappy results.

Inevitably, initial attempts to use digital automation capabilities expose the frequency and quality with which knowledge workers are doing the Solve Loop. Adherence to the content standard and structure, adequate linking rates (60-80%), and a high level of link accuracy (90%+) are the enablers to leveraging these powerful emerging digital capabilities.

We cannot overemphasize the importance of making sure knowledge workers understand how their Solve Loop activities fit into big picture. Their consistent and accurate searching, linking, modifying, and creating enables the analysis that drives organizational learning and improvement. If we want knowledge workers to keep doing these activities, we need to show them how their work is making a difference. KDEs can help by ensuring knowledge workers and management have visibility to the patterns and trends their knowledge work is generating, as well as the improvements that were made possible by those patterns and trends.

Working Across Departments

As the KCS process matures in the organization, the value it generates is not isolated to a single department. The role of the Knowledge Domain Expert is to mine the knowledge, provide business cases and reporting, and engage other departments in corrective actions. Opportunities include:

Improvement of products and services (Development and Product Management)
Improvement of documentation (Technical Documentation or Publications)
New or improved support processes and functionality/applications/integration
Improvement to company policies (policy owners)
New product offerings (Product Management and Marketing)
Additional service offerings (Professional Services)
Improvements or new user applications (Application Engineers)
Increase in self-service effectiveness and customer productivity