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Measuring Your Intranet's Activity Through Social Network Analysis - Gordon Ross
Sure you know how your intranet content is performing, but how about who's collaborating and communicating with each other? What's really happening on your social intranet? Learn how to gain insight into your intranet's activity using social network analysis.
Good afternoon and thank you for joining us in Vancouver at our inaugural Social Intranet Summit. My name is Gordon Ross and I'm Vice President and Partner at OpenRoad and for the last 15 years I've been building websites, web applications, and intranets with a great group of talented individuals here in Gastown, some of whom you've enjoyed hearing talk today.
Around the time when Facebook's founder Mark Zuckerberg was celebrating his 10th birthday, I was sitting in a dark, windowless lecture hall at one of our fine local universities here listening to a professor discuss something to do with eigenvectors, nodes, and cliques. He spoke passionately about liaisons and isolates; weak ties, and propinquity. While I didn't get some of the finer points of graph theory at the time, many of the ideas in the course on social network analysis stuck with me.
My final paper that semester was a study of the patterns of email between university employees. I studied a small group of staff members in the research group I happened to work with. My question was simple: Who did they send email to? And, given that the costs of sending an email to someone down the hall or someone across the country was now the same, how did physical and geographic proximity to one another impact their email communications? The answer back then for those Unix email users, which some 16 years later still rings somewhat true, is that even though the tyranny of distance has been erased with our electronic means of communication, we're still more likely to send an email to a co-worker on the other side of the office than we are to someone in a different time zone.
Physical distance still matters and shapes our social networks. [cube farm slide] In fact, I'd stumbled across what MIT's Thomas Allen had been studying some nearly 20 years before in his work on proximal collaboration: when people are more than 50 ft apart, the likelihood of them collaborating more than once a week is less than 10% . My undergrad research paper was quite primitive in its methods compared to the tools we have available today to perform this type of analysis, but the questions and concerns of social network analysis are still roughly the same.
Social networks have always existed. Long before Facebook and LinkedIn, there was kinship, familial ties, and tribes. And of course, these bonds still exist and carry a huge influence on our lives.
Social network analysis also pre-dates our modern technological era. As a discipline it has been around for 80 years or so, with its early roots dating back to JL Moreno's studies of the 1930's of New York school children and developed through work in the fields of psychology, anthropology and mathematics. He was instrumental in developing the sociogram – a picture of the network of relationships amongst people. Social networks are a representation of social structure.
Just as an organizational chart is a representation of the idealized structure of your company
a social graph is a representation of the often informal ties that exist within that organization. This visualization appeared in an HBR article from July of 1993. It depicts who trusts whom in a network of executives in a company.
This visualization, of the same company, depicts who considers whom an expert.
Put all together, these three representations of an organization can tell us some interesting things.
Is the CEO the most trusted? No. That title goes to Benson..
Benson? Who's Benson? Oh, he's that guy who works in Field Design
Who's considered the expert?
That's Calder, the SVP, followed by Harris.
But where is Harris in our Trust network? Oh. Looks like he's off to the side here with only one friend: Baker. If you're the CEO and it's time for you to do some succession planning and you base it all on expertise, Harris might look like a good bet. But viewed through this network diagram, that changes a lot.
It's for this reason that Social Network Analysis is often referred to as xray vision into the previously unknown and invisible relationships that exist between actors in a particular system.
The basic unit of social network analysis is quite simple. Actors are nodes and the relationships are ties. NEXT SLIDE: DG/GR
Once people and their relationships are described using these concepts, we can apply mathematical theory to understand the distances between individuals, and visualize the resulting data in novel and often surprising ways. Network analysis yields network metrics, hard numbers about that distance on the graph. When that's combined with some underlying sociological concepts, the resulting analysis can tell us about the importance of certain members of a network, their influence, and their relationship to the broader network of people around them. Social Network Analysis operates at a macro and micro level. At a Macro level, it helps us understand What is the shape of the network?Are there sub-groups or clusters within the network? [diagram of entire network] At a micro level it helps us understand the individuals and their rolesWho is a the centre?Who is a the edge?Who is the bridge?
If you know what you're looking for and how to interpret the position of an actor in the network, it can be quite clear to see what's going on. The Kite Diagram is a famous teaching tool for newcomers to social network analysis. If we walk around the network, we can see where roles appear. Diagram: Kite with arrows Susan wins our popularity contest in the middle. She's a hub. She has the most connections. Jennifer is the furthest away. She might occupy our role of "peripheral specialist"Claudia is smack in the middle - she is the one actually in the centre of this network, holding together two pretty distinct branches. And finally Sarah and Steven have some prime real estate, as they have the least amount of people to go through to get to anyone else. Not even Susan, who seemingly knows everyone can say that.
If you're in HR and you see this diagram and someone is talking about Claudia leaving, you know that while she only has 3 connections…
her departure could mean a problem for the rest of the network.
All right. So you're probably thinking, huh, this is interesting. But what does it have to do with me and my intranet. Well, before all of us started friending, following, and poking each other and getting all social on our intranets, social network analysis data was difficult to come by. Classic SNA in business settings, like the HBR example, utilized surveys to collect data on who trusted whom or who considered whom an expert. Data gathering and analysis was a time consuming and difficult process.
Well fast forward to the year 2010 and the increasing use of social tools inside organizations to communicate, collaborate, and learn. While surveys about who's an expert or who is trusted are still useful, those of you in the audience who manage or maintain a social intranet are sitting on top of a mountain of social network data, growing at a staggering rate. The bi-product of daily social intranet usage is a wealth of data that describes employees relationships with each other. I leave a comment on my CEO's blog. A coworker edits a version of a wiki page. I join a community of practice on making my company more sustainable. These are examples of actions that we can now analyze using social network analysis. That mountain of data is both exciting and daunting to the social network analysis newcomer. Even a small group of individuals collaborating across a relatively short period of time (a few weeks or a month) can generate a mind blowing amount of data.
This graph, for example, represents 1 year of commenting data created by Penn State University on their our.outreach intranet. The graph shoes the activity of 289 users that left comments on each others content, thereby creating a tie. Bevin left a comment on Christina's page and now that's represented as two nodes and one tie holding them together.
Over 4,140 unique ties were created amongst these 289 people and roughly 3000 duplicates. A duplicate would be where Bevin commented on Christina's page more than once. 7140 comments across 12 months is about 600 comments / month or 2 comments per user per month on average within the system.
It doesn't sound like a lot of data. Until you start doing some Social Network Analysis. What did I find out about Penn State's intranet? Well, I knew from working with Bevin and her two sidekicks Christina and Tara that they were an important reason why it was a success but the data actually showed me why. Bevin, is the darkest node in the visualization. [star picture]
Not only had she left the most comments of anyone in the system making her the most active, she was also the centre of the network -- Just like Claudia in our Kite Diagram, Bevin was a broker. She had high betweness and as a result had a major impact on the information that flowed through Penn State's intranet. That had both good and bad consequences - she had a powerful effect on the adoption of the system but was clearly a risk for its overall success if she were to leave or be moved into a different position and cease to occupy her role.
Okay. So by now, I'm hoping that you're thinking this is something you might be interested in doing as well. And so you should, as I believe that this is where a massive amount of value of social intranets can be realized. They aren't just a tool for posting your meeting minutes to, they are a living, breathing description of your organizational communication and collaboration patterns. And those patterns can be understood and acted upon.
As network researcher Dr Laurence Lock Lee, who performed an analysis of wiki author contribution and collaboration on Stewart Mader'sWikiPatterns site says,
"Social Network Analysis is to Enterprise 2.0 systems what Business Intelligence is to ERP systems"
I've used Marc Smith's phenomenal open source tool NodeXL, which is a social network analysis plugin to Microsoft Excel 2007 and 2010 to perform analysis of Penn State's ThoughtFarmer usage data. NodeXL is one of many tools available to perform SNA that exist, but I'd recommend using it for a few reasons, including the fact that it's free to download and use, there's some great tutorial material that supports new users, andit's based in Excel. The Excel part is a stroke of genius in my opinion from Marc and his Connected Action research team that produced NodeXL, because you wind up doing a lot of data wrangling in order to get your network data into shape along the way and if you're a seasoned Excel veteran and know how to make data sing, you've already got a great start in performing your analysis.
In order to get from raw data to cool graphs, you basically walk through 4 steps
Step 1: Define your goals -
what is it about your intranet that you're interested in analyzing? The reality: the possibilities are limitless. But your time isn't. So choose wisely and start by focusing your efforts on a specific task, like looking at team collaboration within a particular division or perhaps looking to see who's not using the intranet.
Step 2: Collect and structure your data - get your activity data from your intranet log files or event log and manipulate it into a format that can be analyzed by NodeXL or some other SNA tool.
Thankfully SNA data is pretty simple. It's pairs of nodes. A tie between Bevin and Christina is simply that: Bevin, Christina. Load up Excel with your pairs and you're on your way.
Interpret your data - this is the hardest and messiest part.
When I first ran my data visualizations with the Penn State Data, it was a mess. I wondered how I'd ever get anything useful out of it. And not only did I not have a ton of familiarity with the tool, I didn't recognize many of the names. I knew Bevin, Christina, Tara, and some of their extended team, but I was at a loss looking at 300 Penn State employees from here in Vancouver. I simply didn't know who they were. So, I decided to find a smaller data set to play with to gain a better understanding of how to use the software and master working with the concepts. My data set of choice: [SVEIN TUFT]
Canadian professional road cyclists. Obscure? You bet. Small? Absolutely. And it was data that I knew intimately, having competed with many of the athletes and having followed the sport for over 20 years. I found a cycling website that had kept a history of cyclists and teams and I fed that into NodeXL and created a few neat visualizations.
And the visualizations made a ton of sense. I could see clearly the pioneers of the sport in the 80's and 90's that had gone to Europe and come back to end their careers in North America. I could see the impact of Symmetrics, a local Vancouver pro team of the past decade, I could see Ontario riders and Quebec riders. And I found an unsung hero, who was there in the middle of it all spanning many different networks. I never would have thought he'd played such an important role in the sport in Canada, but it was clear he was an important glue holding other athletes together. It was really remarkable to look at the data I knew in an entirely different light.
Finally, prepare your results. There are lots of pitfalls to be aware of when using data visualization. Done well though you can demonstrate complex interactions in a remarkably powerful fashion.
Another non-intranet SNA visualization that captures this point emphatically is the analysis done on the voting patterns of US senators from the year 2007. This graph was generated using the SocialAction network analysis tool from the University of Maryland. Republicans are RED and Democrats are BLUE. This chart shows the relationships of the senators through their voting patterns. And in the middle, you can see 4 Republican Senators from northeastern US states who often voted with Democrats. I believe that a couple of months after this graph was done, 2 of those members crossed the floor and left the Republicans to join the Democrats. Looking at this graph, you probably could have figured that out a while before it happened. PAUSEWhile fun to play around with, you're doing the analysis for a reason. You're attempting to understand and improve the performance of your intranet, and along with it your organization. It stores more than your company's policies and HR forms, it's a phenomenal source of insight into how work happens.
So here's my 6 takeaways for an intranet manager who's looking to benefit from social network analysis.
Think network thoughts. Start thinking about your intranet not as a piece of software, but as a network, one that can be viewed in a variety of different ways. Don't focus on the software that powers your intranet, focus on the social interactions that it affords. Your intranet is a village and you're the town planner.
2.Read up. The tools won't make much sense unless you start with some basics on how networks work. Thankfully, networks have attracted some really bright minds over the past few years and there's some really great reads out there. Popular: Tipping point, six degrees, linked, connected, emergenceLots of great books. Business-y: Cross & Thomas: ONAAcademic-y: Marc Smith, Valdis Krebs, Ben SchneidermanClassics: Mark Granovetter, Stanley Milgram, etc.
3. Play with the toolsDownload NodeXL, it's free and about as intuitive as you get in this domain of software. Read the tutorial, play with the demo data, start small with a basic kite network analysis. Then start applying the concepts with the tool.
Make some friends in ITOnce you've decided you're ready to tackle your intranet, find some support in IT, especially someone with some good database or log file manipulation skills. A good SQL query or a clever couple of lines of Perl code can save you hundreds of hours of manual data wrangling effort.
Get curiousAsk yourself some questions about your intranet. Quiz yourself on how much you know is going on? Where's the action? Who's the biggest contributor? Who's in the middle of things? Who's not using it? Now go find the answers.
Be patientExpertise won't come overnight. You're dabbling in the realm only previously occupied by nerdy grad students in communications, sociology and math departments. But not having a PhD in graph theory shouldn't stop you. Our challenge, as a profession as a whole, is that if we are going to implement, promote, and manage new networks within organizations, we should take some responsibility for knowing how they work and what we can do to improve them.
Thank you for listening and Good luck!
Measuring Your Intranet's Activity Through Social Network Analysis - Gordon Ross
Thinking Network Thoughtsmeasuring your intranet’s activity using social network analysis<br />Gordon Ross<br />Vice President & Partner<br />OpenRoad Communications<br />
Fred<br />Claudia has the highest betweeness, is a bridge to Ben & Jennifer<br />Claire<br />Sarah<br />Jennifer<br />Ben<br />Claudia<br />Susan<br />Steven<br />Tom<br />Sarah and Steven share highest closeness, nearest to everyone<br />Jennifer has the lowest closeness, is the furthest “away”<br />David<br />Susan has the highest degree, most connections<br />