Today we’ll continue exploring the process, people, and technology considerations for implementing an expertise locator system—now with a look into the key technology considerations. In defining and selecting the technology for implementing an expertise locator system, it is important to consider the following features:
1. Select technology that is compatible with and “integrated into…legacy information systems” (Thompson, 2003, p. 14).
2. Like NASA’s Expert Seeker system, “provide a unified interface to access competencies within the organization, such as completed academic and non-academic courses, past projects and other relevant knowledge” (Becerra-Fernandez, 2001, p. 34).
3. At a minimum, select a tool that helps people locate one another’s expertise. Ideally, select a tool that can also expand to provide question and answer functionality (including archiving of those answers) to provide support for the growth of future expert communities.
4. Select a product that has a flexible and robust search engine, e.g., select one that “uses text fields to search for employees based on their fields of expertise, names, or other applicable search fields…[such as] intellectual property, skills, competencies and proficiency levels” (Becerra-Fernandez, 2001, p. 35).
5. Have a taxonomy or thesaurus developed from the beginning to help organize the areas of expertise and thus the system. In doing so, also aim to strike a “balance between a structured taxonomy of expertise areas and the anarchy of a free-form entry…[because] if there is too much structure staff feel constrained by ‘box-ticking’” (Collison, 1999, p. 12). At the same time, be careful to not let the taxonomy structure be so limited as to hurt the precision of the system and its search results (Dooley, Corman, & McPhee, 2002, p. 227).
6. Give the users the ability to “build it themselves” without having to rely on programmers or system experts (Thompson, 2003, p. 14). Consider a technology approach that utilizes personal home pages employees can easily create themselves. For example, employees could “upload photographs and resumes; choose from an evolving list of expertise categories; note their contracts and network affiliations; write as much as they need to; and link to other Web-based items of relevance – both intranet and Internet” (Collison, 1999, p. 12).
While some researchers (Maybury, D'Amore, & House, 2000, p. 13; Becerra-Fernandez, 2001, p. 35) indicate it would be preferable to select a system that has data mining and other automation features to keep the expert database current, for speed and ease of implementation it is recommended that this company start with a manually-populated system at first. To enable easier migration to a data mining approach in the future, it will be helpful to have the company’s intranet team ensure that they are gathering authorship meta-data (e.g., who developed and who else contributed) for all documents and tools posted on the intranet. Once data mining is added, it will aid in keeping the information contained in the system current (e.g., through mining for contributions to the company intranet and/or documents on shared drives).
References
Becerra-Fernandez, I. (2001). Locating expertise at NASA. Knowledge Management Review, 4(4), 34-37.
Collison, C. (1999). Connecting the new organization: How BP Amoco encourages post-merger collaboration. Knowledge Management Review, 2(1), 12-15.
Dooley, K., Corman, S., & McPhee, R. (2002). A knowledge directory for identifying experts and areas of expertise. Human Systems Management, 21(4), 217-227.
Maybury, M., D'Amore, R., & House, D. (2000). Automating the finding of experts. Research Technology Management, 43(6), 12-15.
Thompson, E. (2003). Effective knowledge management in a cost-cutting environment. Knowledge Management Review, 6(1), 12-15.
- Robin
Copyright Robin Donnan 2007. All Rights Reserved.
Performance Associates, Inc.
