How do you slice and dice the many KM related technologies and tools?
Etienne Wenger has done a great job methinks
So what are the essential or core KM genre?
1) Document / content / publishing management (includes intranets)
2) Helpdesks / customer service
3) CRM (includes sales force automation)
4) Business intelligence (includes technology scanning, newsfeeds)
5) Activity co-ordination (calendars, event notification, PM)
6) Knowledge markets / exchanges (FAQs, blogs)
7) Collaboration tools (discussions, chat, IM)
8) Community / association tools (membership, content, personal profiles, discussions, learning)
9) Learning environments (assessemnt, tracking, content, facilitation)
10) Corporate memory (conversation & content, annotation, reminding)
Next come environments and enablers:
- Intranets
- Extranets, VPNs
- Portals
- Number & text crunchers
- Visualization engines
- ASP delivery models
- Meta search & clustering
Beneath this level we then have supporting technologies:
a) Search
b) Clustering and profiling
c) Asynchronous discussion
d) Synchronous conversations
e) Web publishing
f) Notification & annotation
g) Persistent objects
h) Privacy gradients & security
i) Work flow & routing
j) Intelligent agents
k) Data & text mining
At a lower level we have:
i) Interfaces
ii) Algorithms
iii) Exchange standards
iv) Tools e.g. ontology builders, time reasoners, rule engines, triggers, workflow agents, shopping carts, directory services, encription, matching & reputation calculus.....
What are the main models where KM has shown great and consistent returns?
Automatic profiling: clustering and indexing of document collections and electronic messages, making content and people connections in-the-fly. Autonomy, Semio, Tacit, Orbital
Customer service knowledge bases: capturing solutions to common problems and publishing these on the web so customers can help themselves self-sevice. Think Eureka, World Bank stories.
Personalization: serving dynamic content based on web behavior mined from click streams.
Collaborative filters advice, recommnedations and suggestions based on similarity measures from a large database.
Further thoughts and links
Comments