About Me

We are building a social media analytics tool to turn social media monitoring into social media action. We go beyond mentions to let you Explore, Discover and Engage with the tribes talking about your brand.

My Other Sites
TwitterTwitterFacebookPicasa
Contributors
Tags
Archive
2010 (5)
2009 (15)
Filed under

expertise

See all posts on posterous with this tag »
 

Here comes everybody

Here comes everybody, the power of organizing without organizations is a book by Clay Shirky that discusses the power of the group when bought together by the Internet.  One quote in particular that caught my attention is this:

Every webpage is a latent community. Each page collects the attention of people interested in its contents, and those people might well be interested in conversing with one another too. In almost all cases the community will remain latent, either because the potential ties are too weak, or because the people looking at the page are separated by too wide a gulf of time, and so on. - Page 102.

Without splitting hairs too finely (a blog or wiki is a web page after all), I do think that this is an emerging problem in the space of Enterprise 2.0 content.  As we build up internal communities around tools like Lotus Connections, Microsoft Share Point and Jive SBS, we also build legacy content.  When I read a blog post that was posted 6 months ago, I am separated by a wide gulf of time from the community that interacted with it when it was created.

We need ways of linking the conversation from yesterday with the conversations of today, to bridge this gap in time and place.  Two ways that can help with this problem are:

  1. It's all about the people - make it easy to discover the current and active experts in a particular area.
  2. Keep tags current.
HiveMind can help with both of these problems, but it's the second one I'd like to talk about now.

Having created a blog post, most users would not return to update the tags, even if they created them in the first place.  Yet particularly for emerging knowledge, the way in which we understand and describe content changes over time as new words (Web 2.0 anyone) come into existence to describe what we are doing.  Six months from now is the way you described your post the way the organisation thinks of it?

By focussing on the content and it's demonstrated expertise, HiveMind is able to understand when content demonstrates an expertise and should be tagged with it, even if the expertise the post demonstrated doesn't exist until some time AFTER the post was created.  By helping to maintain the expertise tags on content across the organisation, HiveMind can help organisations bridge the content age gap, but ensuring that like posts are tagged alike as people go about their business of creating great content to share.


Filed under  //  expertise   hivemind   tagging  
Posted by Tim Bull 
 

Google Flu Trends - using signals to predict a real outcome

Today we were at the IBM Smart Work Summit in Melbourne

As we continue the conversation about HiveMind and the concept of mining the signals in an organisation to work out what people's expertise really is, one common question is "but can it work"?

Peter Sheahan had the key note speech and in it raised a great example from Google which demonstrates clearly how the concept of mining hidden signals can deliver real world value.

This comes from Google's Flu Trends site, what it shows is that by mining the common search terms related to cold and flus, they can (very) accurately track flu occurences based on searches for flu symptoms.  Not only this, it's a leading indicator over the data from Doctors which takes time to aggregate.  Similarly, we believe that by mining a broader range of signals within an organisation, for example what people blog about, e-mail about, contribute and comment on, check-in and perhaps yes, even search for in your Enterprise Search solution, we can mine a clear profile of their expertise.

I've been fascinated by these Hidden Signals in organisations for a while now.  In some ways the original inspiration for HiveMind was this Tweet back in January 2009 where I tweeted the following:

A good layoff indicator is LinkedIn, my network activity is way up - 1st action when laid off is to connect and seek recommendations. http://twitter.com/timbull/status/1159905073

Shortly after this, TechCrunch followed up with a more detailed post confirming my own suspicions.

Media_httpcache0techcrunchcomwpcontentuploads200902linkedinchartjanpng_hdzgftjfihapghf

The key take away was this message:

Total minutes spent on the site doubled in January to 96.8 million, from 47.6 million in December.

Part of what is driving all the activity is people looking for job, and helping friends who are out of work. Recommendations are up 65 percent since December, says spokesperson Kay Luo.

I then created a more detailed response http://timbull.com/hidden-signals-in-social-software that posed the following question:

Most of us are already well aware that we leave a footprint on any social networking site, and in some instances, may come to regret that photo we posted on facebook.  What I find more fascinating however is how these sites are generating new signals and measures that over time could be used to perhaps supplement or replace existing messages. One person updating there resume is not newsworthy - everyone updating it sends a strong message about job security. Good systems are those which can make visible these hidden messages and signals.  What hidden signals are you observing in other systems which provide insight (of any kind)?

With HiveMind we are building a tool to help mine these signals from organisations and turn them into expertise.  What's your thoughts?  What signals are there inside your organisation that send messages on people interests and abilities, if only there was something listening for them?

Filed under  //  expertise   flu trends   hidden signals   linkedin   mining  
Posted by Tim Bull 
 

Expertise location across E2.0 is gaining momentum

Ross Dawson wrote today on his blog that:

Unless a large organization can bring the most relevant expertise within the firm to bear on the problems and issues at hand, it really has no reason to exist. A smaller more nimble organization could do as good a job with lower costs.

This is a problem that we are keenly aware of at BinaryPlex.  Our personal experience is in large professional services firms where this problem is a very real one.

With the rapid growth of Enterprise 2.0 tools finding their way into large organisations, there is now scope for tools that mine this information to identify the key people based on the skills they demonstrate, not the skills they say they have.  Using smart software that mines the expertise from documents, automated expertise locations tools could help organisation ensure that their people profiles are more accurate, up to date and contain information on what people really do rather than what they want to promote (although there is a place for both).

Enterprise Search solutions don't help in this environment because they target a different problem.  They are very effective at indexing large amounts of content, but when searching, you are often returned large numbers of documents.  This is typically one step away from what is needed for expertise location problems - I generally need to know who are the key people I need to speak to, not which document do I need to read.

Filed under  //  articles   e2.0   expertise   industry   location   search  
Posted by Tim Bull