Kate proposed that there are three directions for expertise search:
- What you know
- Who you know
- What you do
-Enterprise 2.0 tools
Need reliable valid information
In technical settings, expertise matters
In staffing, you want the right skills
Hard to define expertise (varies by the person looking for it)
No single expert for everyone
Responsiveness - An expert cannot be reached has little value
Who you know
You typically get a better response from people you know
You are more likely to reach out to people who are trusted and whose knowledge is validated
It is easier and faster
You might not get the right expert
Sometimes you just want the answer and not a person
You can spend extra time being redirected to another person
What you know
(This is the core of most location systems. They rely on some database of information to transform into expertise.)
People are judged by what they do rather than what they know
Participation in public venues builds a reputation
Coverage -Not everyone uses the application
Reliability - You are not what you bookmark.
She went on to show how she developed these concepts into the Small Blue application at IBM. (Small Blue because it makes Bib Blue IBM feel smaller.) She took the approach of mining a system, instead of self designation, to create the analysis of expertise. In this case, she chose sent emails. Users need to opt-in to the system.
Personally, I am skeptical of a system that relies only on mining to generate expertise, especially for the transactional side of a law firm. I have found that they work well for the oddball requests, but miss core skills. In part this is because of the lack of rich language. For example, who is the expert on the UCC. I have hundreds of documents and emails with UCC in the text but I am not an expert in the UCC.
Small Blue Find delivers your search results of experts and shows how close the person is in your social network, up to three degrees (like LinkedIn). Small Blue Reach shows you how to reach through your network to that person. It also shows their recent blog posts, bookmarks and their community. Small Blue Net shows a visual representation of the network, color-coded by business unit with pictures of each person.
She had some interesting data on what caused someone to select someone from the list of experts presented to them. She found that page ranking had an impact, but the closer the person was in your community was the biggest factor. Participation in blogs was a big factor. The person blogging was advertising that they are willing to be contacted about the content of their blog.
She found a notion of expertise sufficiency. The expertise searcher gets to a point where expertise is sufficient and then other factors kick in.
She found that big users of the system are people interviewing for other positions within IBM. They use the system to find information about the person who is interviewing them and who they know that knows the person to find out background information.
Small Blue has been deployed for a year, with 2500 of the 300,000 employees opted in. Since each person in the system brings all of their contacts into the system they get great coverage.