Over the past several years, there has been a rise in virtual working teams and a rise in fluid teams. A fluid team is one composed of individuals who work together to complete a project, but once finished, they disperse and go their separate ways to work on different projects, often with different people. This is in contrast to a fixed team, which works together often.
In such a dynamic working environment, knowledge management becomes crucial. To build high performing teams and maintain a high performing organization, it is important people have access to the existing knowledge about company processes. Clearly, recreating the wheel is inefficient. On top of that, learning from mistakes—ideally others’ mistakes—improves quality and saves time.
A bit of background info, knowledge management generally involves three processes: knowledge creation, knowledge retention, and knowledge transfer. Knowledge can be of three types: declarative (knowledge about something), procedural (how something occurs or is performed), and casual (why something happens).
According to the AMR Research (now Gartner) Knowledge Management Spending Report, US companies invested $73 billion in knowledge management system initiatives in 2007. But do these systems work, and if so how?
That is a question Bradley Staats, Melissa Valentine, and Amy Edmondson sought to answer. They recently completed the “first wide-scale evaluation of the objective performance value of an organizational knowledge system using archival data,” Using What We Know: Turning Organizational Knowledge into Team Performance (you can read the full paper at HBS Working Knowledge). They ask:
- How does knowledge use affect performance?
- Why do teams with similar access to stored knowledge differ in performance?
- How does geographic dispersion affect knowledge use and performance?
- How does the complexity of the work affect knowledge use and performance?
They focused on codified (as opposed to tacit) knowledge. The conclusions are as follows:
- Teams that are geographically disperse, composed of less experienced employees, and engage in complex work benefit more from knowledge management systems.
- When a team uses the knowledge management database more frequently, the team’s efficiency is improved (they spend less time on trial and error) but quality is not (not all knowledge can be stored in a database, so some problem-solving is still required).
- A divide and conquer strategy (when team members specialize or become experts in certain data) is associated with efficiency, but not quality (one person interpreting data is subject to making mistakes, possibly affecting the entire project if an error is made).
- With a share and share alike strategy—when all people on a team have access to the knowledge management database—quality is higher (person searching for data knows the context behind the search so details that may seem irrelevant to others become helpful).