As “collaborative” technologies emerged in the last decade, there has been a series of breakthroughs that better enable one:many and many:many interaction – supplementing f2f, e-mail, and IM for one:one interaction. (How many of you remember Groove.net?)

Most of these technologies are greeted with enthusiasm by new users – and that generally lasts until the SIGNAL:NOISE ratio begins to deteriorate. Slowly, participants withdraw from the fray and leave behind a wasteland of collaborative detritus. (This is illustrated quite well in 2009 by by the rapid attrition of Twitter accounts.)

In my experience, I have often used the analogy of Digital Signal Processing (to help explain the importance of consciously managing the Dynamic Signals you have described to ensure a sustainable community or collaborative workspace. Briefly, signals must be generated and *amplified* before they can traverse any significant distance (whether that’s true geographic distance or elapsed time). Without a conscious effort to manage these signals, they deteriorate rapidly and the community of users simply move on to the next “shiny object”.

I am very interested in what the Dachis Group is offering, and how you’re doing it. Rock on!

That’s a really great point. We know all systems can be gamed. Makes me think of all the ways Twitter gets abused so people can magnify follower counts.

I think this is where the analysis comes in. My guess is that as with any data which needs to be interpreted—there will have to be some kind of human intervention which can pick up on nuances. It’s interesting that you point out quality. That’s hard to fake. Community dynamics are usually very good at policing those who provide value vs. those who don’t. So perhaps that will figure into the equation as well.

How long will it take employees to “game the system,” and look like they’re working and communicating with one another the same as putting a coat on the back of a chair and taking a 2 hour lunch? In constructing a social business what types of awareness filters will be put in place to ensure that lower level employees are contributing quality and not just adding to the noise stream?

“Google doesn’t index these effectively. It seems to me there’s an opportunity here for a kind of “information stock market” that identifies trends and ranks them dynamically.”

Hmmm, someone is probably going to build that.

Great points about semi-persistent signals as well. I think there are some depth layers to consider. One gets attention, provides some information and you go a level deeper for further context. But there’s most likely overlap as well.

The connection here is that signals can not only be dynamic but persistent, or they might be temporarily persistent, like the flag the postman puts up to signal that there’s mail in your inbox.

Even in your face-to-face examples there are signals that are more persistent, such as who has a tattoo, who wears a tie, who hasn’t shaved or ironed their shirt, etc.

The space that seems interesting to me is not the volumetric flow so much as the semi-persistent signal which flags a temporary condition, such as a task on a to-do list, a note that points people to an event, or the hashtag of a trending topic. To use your Basecamp example, a flurry of activity on Basecamp signals presence but not progress. A much more interesting signal to me would be the number of to-do items, the rate at which they are being checked off, and the ratio of done to undone items in the system.

These signals can flag meaningful trends that bear watching. As you point out, Google doesn’t index these effectively. It seems to me there’s an opportunity here for a kind of “information stock market” that identifies trends and ranks them dynamically.

“What do you see as being the likely business value of such signals? How will we be able to most effectively use them?”

As you probably know, my Colleague Jevon has spoke in length about the concept of a “real time” enterprise. The bottom line is that if a system, process and culture is in place which encourages and collects signals, an organization can better adapt to, or even anticipate change. The dynamics externally to an organization are manifesting thanks to both technology and user behavior—if there is a lot of negative chatter about an organization moving quickly (in real time), it’s possible that a groundswell is taking place. This can be diffused with the right people, process and technology in place.

Internally there are similar benefits. If an organization can encourage, manage and make meaning from the dynamic signals of it’s employees etc. it can better manage, retain and perhaps get more value from them and vice versa.

“As you indicate, when Twitter volume is high, you’re engaging the ecosystem. Great. But that’s like saying, when the skyscraper’s on fire, smoke comes out of the top of it.”

A good point. I think our own stream is a very light example of something with more depth, but the point is—in better understanding the work patterns within your ecosystem, an organization has the potential to have better relationships with its employees and between them. That’s the first step in collaboration right? Context.

To your example. I’d wager that a professionally trained fireman can determine a lot from smoke itself. The direction it’s blowing, the smell, the color. I think we’re early on but to the point of where I started the post, participants in business meetings benefit from sending and reciprocating the right kinds of signals in real time. Likewise, the wrong ones can ruin a business meeting. I think there is a parallel here that will apply at scale.

Or another way to look at this is to think about what Facebook is going to do with all that data? If they benefit from it, can an organization?

Interesting post, and nice to see Dachis’ Collaboratory up and running (after a seemingly rough start…it happens!).

I like the general thesis you posit about the potential of dynamic signals, but the post leaves me hanging. What do you see as being the likely business value of such signals? How will we be able to most effectively use them?

As you indicate, when Twitter volume is high, you’re engaging the ecosystem. Great. But that’s like saying, when the skyscraper’s on fire, smoke comes out of the top of it.

And?

The other technologies you point out, while effective, also have major flaws: the biggest of which is corporate culture, hierarchical mental models brought to these new tools from the habits based in years of email.

It’s very difficult, even when people are motivated, to get them integrating Yammer on the desktop and device as part of their workflow. Basecamp’s usability is inversely proportional to the amount of information and people flowing through it. These flatter, niche purpose devices and communication methods only hold out so long as they find their place in a team or a project. All in all, they’re not that mature, but they point to a void that needs filling.

All of these technologies, as you point out, are getting us through this transition to being capable of dynamic signaling via technical infrastructure and interface design. Part of this transition is the way we can look at these data points visually. I often think how much more effective email would be if it had an interface closer to that of Tweetdeck, where messages can be filtered (which it already does), and then presented visually in columns of the user’s choosing. In this way, you could also see the dynamic signal trends with more clarity.

The other part of this equation is what you also pointed out in terms of being able to receive these signals without as much conscious designation of that signal by the creator. In Flash, you can have objects that are always “listening” for events to happen, and then respond with an action. Maybe the next step with these tools is to integrate similar technical listen/respond devices between nodes on the network.