For our social computing metrics system, we have the ability to see how people act on others contributions. For example, given one person’s post, we can tell who is sharing, tagging and sometimes reading it, with identities of all. This can tell us how much a person is impacting those around them, who and how.
[Note: From an enterprise measurement viewpoint who the individuals are is not important but you need their ID to key off other demographics such as their job roles, geographic location, or organizational location. This might be of interest to each person, but I’m looking at the gestalt of the organization. Also this is information we are allowed to see per privacy guidelines.]
This leads to several possibilities, given person X’s post. The first set is diversity of reach:
a) What job roles are consuming their data
b) Where in the organization are the consumers from
c) Given a single post how much consumption is happening; and what’s the average per post
On the business level, this can tell us a lot about how well the organization is connected, and if the expected views of what job roles rely on others is actually occurring and how much. For example, sales people working with their sales engineers or seeking domain knowledge experts. It can show how far they reach across the organization, and what other roles they connected to that were not expected. For example, sales people in Slovenia working with Researchers in Israel.
The second set may look at secondary effects. Given person X posts, and person Y shares or tags, who is Person Z that eventually consumes it.
a) What job roles (persons’ Z) are the end consumers
b) Where in the org they come from
c) How much and what’s the average.
d) Is there additional resharing or retagging
This extends the first set by looking at eventual impact from the source.
So far, I’ve just talked about one path of action from a creator (source) to a consumer (sink).The next level is to look across many actions on if there is bidirectional interaction happening between the roles. This looks for ‘lasting’ relationships based on continued bidirectional interaction. This can happen in immediate sequence (e.g., I post, someone replies to me, I reply back, and so on); or it can be delayed sequence of events (e.g., I post, someone reads/tags it, a week later they send something else through a different social tool).
Here we are looking beyond immediate or unidirectional consumption, towards the idea of if people are forming lasting relationships.
Notice for one that I didn’t even say that it was necessary for people to friend each other before any of this happens. In fact, I think that friending action while certainly making it obvious is highly variable. Some people consider friending to identity those who they have lasting relationships with, but others use it simply to keep track of people they are watching rather than have any interaction with. The difference lies in the bidirectional vs. unidirectional relationship there. In other cases, some folks never actually friend others but certainly interact with them, therefore indicating a relationship.
Why is this any different than SNA (social network analysis) tools? Perhaps it’s the limitation of the SNA tools I have found in terms of the level of demographics and granularity they can show. For example, some do not show the demographics I need because they simply don’t contain that info, or don’t understand which demographics are useful for business reasons.
In terms of granularity, most SNA tools can show the structure for each person; i.e., the relationships and interactions between person X and those around them, but I need info about the aggregate level of everyone of one demographic (e.g. job category), and the relationships they form. This is beyond most SNA tools today.
The biggest part is that it takes a lot of data collection
and number crunching over many, many people to even begin to analyze this. This is beyond System level metrics (how many users, how many documents), or object level (how much activity per person or object), but goes into the meta level that we would like to understand. This is also only one aspect of many others.
On the business side, the goal is to better understand the connections across our organization, and where we can try to focus energies to improve communications or encourage interaction. It is using information from social systems to create a smarter organization. For enterprise 2.0 to become a success, it is not just about empowering individuals to use social computing systems, but it is to make the organization itself function better.