The last couple of days I have had interesting discussions at the office about social media analytics and web analytics during the preparation on a proposal for one of our clients. During these sessions I noticed that some colleagues are mixing up the competences social media analytics and social network analytics. As I already discussed the first in my post about social media monitoring, I decided to write a new post solely to clarify the difference as my hands-on experience with social network analysis software is minimal. The main difference is that social media analytics is primarily used for brand management and marketing purposes where social network analysis is used for understanding the relationships (ties) between users or groups (nodes).
Social network analysis or SNA is a kind of technique that helps companies and governments to analyze the patterns of different types of relationships that exist among people and groups that are online. The main purpose of SNA is to examine the interdependence and social structure of all the individuals that are present in a specific organization. The main task of this process includes collection of data from various sources like surveys, blogs, e-mails, and electronic artifacts and then performing analysis of this collected data to identify the relationships that are present. After that mining is done on extracting information like quality and effectiveness of relationship that has been found out this can be used to create valuable intelligence. There is a branch of SNA called Organization Network Analysis or ONA. ONA is mainly used to carry out the studies related to different informal social groups and networks that are working in the similar enterprises. Here the VNA or value network analysis is carried out that focuses on the examination of tangible or intangible exchanges among people of groups present in multiple organizations.
SNA is generally used to carry out the analysis of organizations and different collaborative environments like R&D teams, supplier networks and organizational units. Many organizations are using SNA to understand the flow of knowledge and information, to identify key knowledge brokers, to highlight all the opportunities that favor an increased knowledge flow, and to improve the performance. An other reason most companies are making use of organization related network maps is that they can manage changes effectively. SNA can also be used to target all the key opinion leaders for a specific marketing campaign or product launch. Beside commercial companies also innovative governments and financial institutes are using social network analysis software today for crime and fraud protection. Investigating relations, communication and transactions flows to detect suspicious and fraudulent behavior.
SNA is also used to carry out the process of mining data from different applications like major online social networks as LinkedIn, MySpace and Facebook. In my opinion a large part of the monetary value of these social networks are hidden in the rich information and (hidden) insights these networks contain about the types of interdependency, such as friendship, kinship, financial exchange, dislike, (sexual) relationships, relationships of beliefs, knowledge or prestige.
Some metrics that social network analysis tools offer beside interesting graphs are; betweenness, bridge, closeness, cohesion, path length, radiality, reach and many more which go beyond my knowledge and vocabulary 
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There are many different commercial products available in the market that can simplify the process of creating network diagrams and network visualizations for survey data or segmentation purposes. The implementation and use of SNA is however not that easy as it might looks because it is still very conceptual and the information (intelligence?) that is collected using SNA is quite difficult to translate to some pragmatic actionable insights.
The future of social network analysis software and technologies lies in the ability of collaboration with other SNA technologies, improving the algorithms that enable recognizing all the hidden patterns present in immense distributed data collections. This makes the task of interaction explicit where social networks and media is all about.
The above picture is a very cool representation of the blogosphere done with a similar kind of technology.
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