The Live Web of Interaction Signals for Business Users

Over the next 3 years, the ability to interact, analyze and filter our stream of interaction activities based on a live or real-time model will redefine our personal, work and mobile experiences with the web.
Let’s look at these two categories (i) the Live Web of real-time streams and (ii) Interaction Signals separately:
The Live Web of Real-time Streams for Business Users
The real-time theme continues to grow and it will increasingly define a new class of web.   While it has taken some time to get established, it is not a new idea.   With a hat tip to Stuart Henshall, I’ve found it very helpful to read the early insights and writings of David Gelernter  .
Professor Gelernter is a Computer Science Professor at Yale and his research centers on information management, parallel programming, and artificial intelligence.  As early as the 80’s, Professor Gelernter envisioned managing information based on a time paradigm and through a decentralized data storage system.  He is credited with setting the foundation for today’s time based activity stream and cloud computing models so prevalent in Twitter, Friendfeed, Facebook,, Google apps and a number of start ups.
In this 2001 interview, I like how Professor Gelernter describes the impact of moving from a space ( or file) based computing model to a time ( or activity stream) based model:
When I look at where software is heading and what it is really doing, … what’s happening and what will happen with the emergence of a new generation of information-management systems, and as we discard Windows and NT … these systems that are 1960s, 1970s systems on which we rely today, we’ll see a transition similar to what happened during the 19th century, when people’s sense of space suddenly changed.
If you compare the world of 1800 to the world of 1900, people’s sense of space was tremendously limited and local and restricted in 1800. If you look at a New England village of the time, you can see this dramatically, everything is on site, a small cluster of houses, in which everything that needs to be done is done, and fields beyond, and beyond the fields a forest.

People traveled to some extent, but they didn’t travel often, most people rarely traveled at all. The picture of space outside people’s own local space was exceptionally fuzzy. Today, our picture of time is equally fuzzy; we have an idea of our local time and what happened today and yesterday, and what’s going to happen next week, what happened the last few weeks, but outside of this, our view of time is as restricted and local as people’s view of space was around 1800. If you look at what happened in the 19th century as transportation became available, cheap and ubiquitous, all of a sudden people developed a sense of space beyond their own local spaces, and the world changed dramatically.

What’s going to happen, what software will do over the next few years has already started to happen and will accelerate… our software will be time-based, rather than space-based.

We’ll deal with streams of information rather than chaotic file systems that are based on 1940s idea of desks and file cabinets. The transition to a software world where we have a stream with a past, present and future is a transition to a world in which people have a much more acute sense of time outside their own local week, or month in which they now have a clear idea of what was different, why February of 1997 was different from February of 1994, which most people today don’t have a clear picture of.
With the introduction of activity streams, a business user, a department, an enterprise will have a more acute sense of time and how things have changed over time.  They will have a clearer idea of what is happening now… live… in real-time… with the people, topics and activities most important to them… and what is different between this week vs. last,  this month vs. last, this year vs. last … across people, projects, customers, product lines, businesses and markets.   This is an incredibly powerful shift.
Interaction Signals for Business Users
The shift to the live web is underway in our personal (consumer) lives and will … as with other consumer web innovations … be adopted and integrated within our work (enterprise) lives.    If we describe the enterprise live web as a flow or activity stream of information tied to our live transactions, conversations and relationships … then a number of new, live, time-based signals or metrics will be needed to help us optimize how we allocate time and attention in our daily interactions.
I think there are 4 classes of signals or metrics that are emerging to help business users understand how their interactions – and by interactions, I mean their transactions, conversations and relationships inside and outside the enterprise – are changing over time and will improve their productivity by embracing the Live Web. They include:
Interaction Activity Signals track the number, rate of change and scale of interaction activities for a business user over time.  Examples include:
  • number of posts, emails, meetings, conversations, orders, tweets, cold calls over time
  • count of views, votes, comment, responses, feedback, re-tweets over time
  • active, inactive subscribers / followers over time
  • total inbound/outbound connections, interactions, links over time
  • number of unique interactions over time
Interaction Role Signals track the involvement, collaboration, leadership and consistency of roles that business users play through interactions over time.  Examples include:
  • pattern and pace of interactions over time
  • degree of replication and emulation during interactions over time
  • variability in emphasis, rhythm and timing of interactions over time
  • degree of openness to new ideas and influence over time
  • inbound-to-outbound interaction ratio over time
  • positive-to-negative interaction ratio over time
Interaction Influence Signals track the diversity, reach and influence of interaction activities for a business user over time.  Examples include:
  • diversity of business user social graph in the enterprise over time
  • conversation to broadcast ratio over time
  • conversation reach across the enterprise over time
  • degree of reference / Link over time
  • diversity of reference / Link over time
  • influence of your followers over time
  • influence of your referencers over time
  • new conversation generation over time
Interaction Results Signals track individual business user, team or enterprise deliverables resulting from interaction activity over time.  Examples include:
  • new transactions (orders, contracts, demos, investment, etc. ) over time
  • conversation-to-conversion ratio over time
  • relationship-to-conversion ratio over time
  • correlation ratios between interaction activity, role, influence and results metrics over time
As the Live Web of activity streams and interaction signals emerge in the enterprise, one could say we’re about to step out of the quaint New England village of the 1800’s as described by Professor Gelernter.
The enterprise is shifting from a space/file software model to a time/stream based software model where the real-time interactions of business users have a past, a present and a future.
New value for business users – and revenue for enterprises – will come from aggregating, analyzing and filtering these interaction signals enabled by the Live Web.

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s