tools
Turning Conversations into Content
We're engaged in conversations all the time, and in an age where many conversations are conveyed digitally, a lot of those can be archived, searched, filtered, indexed. But is that all there is to it, from a knowledge point of view? Are there opportunities to be missed (or exploited) to provide tools and methodologies that turn conversations into structured formats that are more useful in terms of knowledge creation and dissemination than the raw flow? We'll discuss these questions and the ones below.
Q1: What's the difference between conversation and content?
Q2: What's the difference between content and knowledge?
Q3: How do conversations and content fit (or not) into a conventional KM perspective, and into conventional KM tools?
KM and Google Wave
Google Wave is an online software application product of Google, described as a personal communication and collaboration tool. It is a web-based service, computing platform, and communications protocol designed to merge e-mail, instant messaging, wikis, and social networking. It has a strong collaborative and real-time focus supported by extensions that can provide, for example, spelling/grammar checking, automated translation among 40 languages, and numerous other extensions. It is still in preview mode, thus not yet officially released.
When Google Wave was introduced it created a wave of enthusiasm all around the world, including from knowledge managers. At the moment, some people still underline its unique opportunities for collaboration, others were dissapointed by the buggy experience of the new platform, they could not see the practical use, or felt all alone because their colleagues weren't on it.
During this chat we will look at experiences people had with Google Wave, explore opportunities and threats, and share tips and tricks.
- Are you using Google Wave?
- What can Google Wave mean for KM?
- What are/could be succesfactors making Google Wave collaboration succesful?
- What Google Wave robots/extensions are particularly useful in a KM context? And how can they be used?