Social Network Analysis Surfaces Insight from Interactions

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  Social Network Analysis Surfaces Insight from Interactions  
  By Carol Rozwell, Gartner Research  

When you ask most people what they know about social networking analysis, you’re likely to hear a bevy of replies mentioning technologies that help us build and manage our social networks, such as Facebook, LinkedIn or Twitter. But social network analysis goes much deeper than simply making connections. Social network analysis reveals new and necessary information such as which employees are most influential in the organization and could be essential in the team’s adoption of a new technology, or which employees are most likely to support a company’s new direction. Social network analysis is more than social media sites; it is the discipline and technology that provide a view into the relationship dynamics underlying business might. The opportunity lies in connecting social network analysis and context-aware computing to surface insight.

Enter, MySocialNetworkSpace
The concept of using social networks to deal with large amounts of information is not new. Historically, decision-makers have relied upon people whom they trust for advice on myriad issues ranging from, "which airport is easier to fly into if I want to go to London, Heathrow or Gatwick?" to, "what's the impact on our business model if we acquire company X?" Whether the decisions are straightforward and fact-based (such as the choice of airports) or more nuanced (such as the acquisition question); other people can help narrow the range of choices.

While it is difficult to pin down exactly which application began the “gold rush” of social computing players, we could arguably state that, among the public applications, the surge began with MySpace - innovative in its time, pervasive in its acceptance and now just about obsolete. More comprehensive and deeper applications that bring context into the mix (such as Facebook and LinkedIn) have emerged to compete with other communication channels. And then there is Twitter, still an oddity to some, but many organizations are already using it for important public relations damage control work, such as BP. British Petroleum’s global public relations arm launched its Twitter account following the massive oil spill in the Gulf of Mexico, stating: “This page exists to get BP's message and mission statement out into the twitterverse!”

These social networking sites (Twitter included) are, in part, an attempt to consolidate disparate communication streams such as information sharing, Short Message Service (SMS) and chat into a more manageable "place." Users maintain a profile that allows them to share information with others who can then interact with them in many ways. Users can maintain a wide variety of loose connections, keep a small circle of close friends, or join groups of people they may not know, but with whom they appear to have similar interests. Some organizations are creating "Facebook-like" functionality privately in their companies to spur cross-functional communication and knowledge sharing. Social networks range from loose associations of the type encountered on an open social networking site to the tight affiliations of a traditional organizational hierarchy. As shown in Figure 1, they vary, based on the degree to which they are emergent or engineered (shown on the "X" axis) and how purpose- or interest-driven they are (shown on the "Y" axis).

Source: Gartner (August 2009)

Context Enables Effective Information Usage
Gartner has identified four categories of context information:

  • Process: system and user-defined rules;
  • Environment: location, network capability;
  • Community: groups, links, tagging and presence; and,
  • Identity: trust, role, tasks and reputation.

Context information can be problematic in that it often must be aggregated from multiple sources, is potentially vast and it is volatile. The social network becomes an asset to deal with situations where the decision-making activities being performed have a social dimension such as travel, entertainment and retail. Social network analysis creates a lot of information about network participants and characteristics, which can be made available to context-aware applications. Similarly, context-aware applications can supply information to social networks. The social network then becomes both the creator and consumer of context information.

The Social Network as a Consumer of Context Information
Context-aware recommender systems are being tested with mobile tourism guides. When people are traveling, they often enjoy activities in groups. They may also want to share photos and experiences with others in their social network who may not be traveling with them. The travel guides use information about the traveler's situation such as location, time, user preferences, and relationships to make more focused recommendations than were previously possible. Mobile devices have address books and buddy lists. Using location technologies and filtering of social network data, they can detect friends near the traveler and allow them to meet. One potential scenario is to query the tourist guide about possible points of interest where a group could meet. The guide would provide suggestions, based on the makeup of the group and its common interests.

Another opportunity for combining context awareness and social networks is to apply these principles to people attending a conference or similar event. Most people comment that they attend face-to-face events to network with colleagues, yet few event organizers have the information needed to determine if they are meeting this desire. They lack basic information that could foster more attendee-to-attendee interaction. Using a combination of context information including GPS location, activity (tweets) and social profiles, it is possible to determine:

  • Community pulse. What are the major issues resonating with attendees? What are their current topics of interest and how can they be satisfied immediately or in events planned for the future?
  • Community connectedness. How many people who are at the event know each other? How many people have similar interests? How can they become aware of each other without violating privacy sensitivities?
  • Engagement. Has the event enabled people to interact with peers in addition to receiving one-way input from presenters? Will attendees continue to engage with each other once they leave the venue?
  • Influencers. Who are the most connected attendees in the group? How can they be encouraged to communicate key messages from the event to the attendees and their social networks?

The combination of context information - especially location context - with social networks is particularly intriguing. It affords the possibility of further refining the suggestions that have been provided through another technique, collaborative filtering, which is often used by vendors such as Amazon or L.L. Bean. For example, when trying to find a restaurant, a business traveler may find comments from other "weak tie foodies" in their social network, augmented by information on their company's travel policy to be more useful than recommendations from family members, who are closer ties, but have never visited that locality or who prefer fast food. However, despite the potential value of improved relevance, there is still a question of how much information an individual will reveal about themselves - how much privacy they will give up - to get more useful recommendations. The answer will be different, depending on the type and characteristics of the network (such as whether it is a closed workgroup or a newly formed community of practice).

The Social Network as a Provider of Context Information
Applications that provide context-aware computing are becoming more socially informed. Social software and collaboration tools designed for enterprise use are learning to adapt to social contexts and support group interactions without becoming so much of a distraction that they hinder the activities they are intended to streamline. Vendors are including functions in their applications that make it easier to uncover the structure and interactions in a social network by "observing" the actions of users. This is an alternative approach for gaining a perspective on the social network, which does not require organizations to perform a traditional social network analysis. The two views shown in Figure 2 are stylized examples of the results obtained when someone searches for people who know about a certain topic (topic view) and the expertise of any individual who knows about that topic (expertise view).

Figure 2. Switching Context From Topic to Expertise Views.

LD - Leadership Development; WFA - Workforce Analytics; HRIT - Human Resources Information Technology; EE - Employee Engagement; PM - Performance Management; CM - Compensation Management Source: Gartner (September 2010)

One common use of social network analysis is to examine the communication patterns of people working together in groups to gain an understanding of the interactions that could be supported better by social software and collaboration technology. For example, an HR organization may have people working on a similar topic, but geographically dispersed and unaware of each other. The impact of this lack of awareness is reduced productivity, the potential for duplicate work, and an inability to collaborate effectively.

The social network map shown in Figure 3 is a representation of what one organization discovered after conducting social network analysis and analyzed the interaction patterns of people working on the same organization, but reporting to different managers. Each employee in the HR organization is shown as a "dot" and the interactions among employees are shown as lines connecting the dots. The map is color coded by geographic location. All employees work for the manager whose dot is red. The blue dots represent the employees working in the European HR organization; the green dots represent the employees working in the U.S. HR organization. What is evident is that employees in Europe have infrequent communication with their U.S.-based counterparts and their manager.

The organization decided to do the analysis before it rolled out new collaboration tools because it wanted a baseline of the existing relationship patterns. It felt this information would help to target the key influencers in the social network, obtain their support and, thereby, increase adoption of the new tools. It also wanted a "before" and "after" picture it could use to compare the impact of implementing collaboration tools and to derive a statement of value.

Figure 3. Social Network Analysis Reveals Communication and Interaction Patterns.

Source: Gartner (August 2009)

Social Network Analysis at Work
As organizations explore how they can use social network analysis to generate context information, they will find many situations where people need to work together, but have limited information about each other.

  • Dispersed Employees - In today's work environment, it is common for people to be working on multiple projects at the same time, all with different topics and deadlines, and often in different geographic locations. People are often thrown into teams where they are supposed to produce results quickly, even if they have not previously worked with others in the group. Research indicates that frequent context switching reduces productivity because it takes time for us to adapt to a new train of thought.
  • Effective Meetings - Combining social network context with context-aware computing tools can make meetings more productive. Meeting participants can use networked devices that are able to identify individuals, present profile information and establish communication links to be used in the session. A map of the meeting room would indicate where people are sitting in the room. Participants would be able to view each others profile information as well as to exchange other types of content. Communication via the select modalities such as instant messaging (IM) or e-mail can be limited to the meeting participants, whether they are in the face-to-face meeting or connected remotely. After the meeting, participants could reopen artifacts to find profile contacts and interaction information, as well as content shared during the session.
  • Smarter Decision-Making - Social network context information can be used to inform decision-making by filtering a wide range of options to those that will more likely meet an individual's needs. Search engines could use social network context along with other techniques to improve the relevance of the results they present. Social network analysis presents a view of communication and relationship patterns that should be factored into technology implementation decisions.

Combining social network context with context-aware computing tools can make organizations more productive as the ability to focus functionality on the immediate needs of ephemeral groups of users increases. Human Resources has a role to play to encourage the use of social network analysis to mine relationship-oriented business intelligence for its own benefit, as well as to support greater organizational productivity.

Fortune Favors the Bold
While we all sit back and learn from BP’s potential misstep into the Twitterverse (almost all the comments posted are strongly negative), take a moment to imagine how adding context to whichever social networking tools you use can bring your employees together, help aid understanding among groups, promote knowledge and idea sharing, and bring your business closer to its customers and, ultimately, to its goals. If you haven’t already begun to examine the social network at your business, the time to start is now. Technology may come and go, but the human dynamics involved in sharing and using information are here to stay. The sooner you start to “connect the dots,” the sooner you will see your company’s “big picture” come to life.


About the Author
Carol Rozwell is a vice president and distinguished analyst on the Social Software and Collaboration team at Gartner. Rozwell explores the relationship dynamics of social networks and social media. Prior to her current assignment, she focused her research on technology and standards for R&D productivity in the pharmaceutical industry

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