By Evan D. Robertson, Project Associate.
I’m about six feet tall. I have blue eyes. My hair: blonde. While these
descriptions likely read as a personal ad, these data also share one
very important thing in common. They all describe attributes particular
to myself. Attribute data defines characteristics that belong to objects
or, in this case, people. It is the type of data with which we are most
familiar and serves as the cornerstone of our modern statistical
society. Most surveys, interviews, and other data gathering techniques
are essentially collecting attribute data in one form or another. For
instance, the United States Census Bureau is engaged in the vital task
of collecting and disseminating individual attribute data aggregated to
some specified level of geography. Household income, educational
attainment, age, and race/ethnicity are useful types of attribute data
we see with regularity. In a growing world in which we have begun to
realize the value of social networks, however, it is important to
identify another class of data which, while used extensively in social
science research over the last fifty years, has not reached such
predominance in society’s consciousness.
Relational data defines the connections, ties, and attachments which
relate one individual (or object, as you’ll see in my forthcoming post)
to another. Without at least two agents, relational data doesn’t exist.
Relational data hasn’t gained as widespread recognition for a variety of
reasons but the most central: it is highly qualitative and difficult to
gather. I have blue eyes; this is fairly easily observed and
classified. But, how does one describe my connection with my friends? My
coworkers? Close? How close? Do we measure our closeness by the number
of emails exchanged? Phone call length? How much time we spend in the
break room? There is, for now, a qualitative component of relational
data that social scientists are still working to codify and develop a
unified language to describe our social relations. Describing these
relationships, however, is becoming an imperative in our increasingly
networked world.
Social network analysis (SNA) is a tool that analyzes relational data.
Social network analysis attempts to investigate the connections between
individuals in a larger social web through the use of graphing
techniques. SNA can be used in a variety of settings, depending, of
course, on the needs of the user. For instance, if you are engaged in
economic development marketing, then SNA would be a fairly useful tool
to study your Twitter or other social media campaigns to identify gaps
or groups which you feel that your organization isn’t as well connected
to as it should be. In terms of analyzing an organization, SNA can help
to identify potential barriers to information flow or general
communication shenanigans within the economic development organization.
And for those data geeks, so near and dear to my heart, social network
analysis gives an outlet to visualize connections between various
objects (yep, it doesn’t have to be people, simply objects that have
interrelation…tune in on June 12th).
I’ll be the first to admit that there are a few short comings of social
network analysis as an analytic tool. First off, depending on the size
of the social network, I am not entirely convinced that social network
analysis will show you something that you don’t already intuitively
know. You can probably take a guess at which organizations your social
media campaign isn’t penetrating. The data is also difficult to gather.
While you can obtain information on your followers via
twittercounter.com and klout.com (see my colleagues past blog on these tools for more detail),
it is more difficult to obtain the broader social structure you are
attempting to influence. Matching your Twitter follower information to
your broader social network, say the entire business community in your
county, and understanding the quality of these connections is
prohibitive from a time and cost perspective. The power of social
network analysis is that it creates visually appealing, intuitive
graphs. It is one thing to instinctively know the workings of your
social network. It is another thing entirely too actually be able to see
it as well as manipulate it (not used in the Orwellian sense of the
word). Social network analysis gives the ability to organize data, to
think about the connections between things, and to reorganize the system
given new thoughts about their interrelations. It is an expedient tool
to step away from our social world and gain a bird’s eye perspective.
So, I’m at 735 words which I've been informed is approaching the
socially acceptable limit for blog length. On my next post, I’ll use
social network analysis to order and reorder national occupational
employment data published by the Bureau of Labor Statistics and see if
we cannot glean any useful information. My next few blog posts are
intended to be experimental, so if something doesn't exactly work out:
you have my humble apologies. As promised, there will be graphs.
