Mapping the Information Organization—Social Network Analysis
While it is important, and recommended, that emerging companies develop some form of organizational chart to outline the formal reporting and consultative relationships between various business units and the departments include therein, it must also be recognized that all organizations have their own informal network of relationships that can be just as important in getting projects completed, generating new ideas and improving and maintaining overall employee morale. Almost every company can make its own contribution to the large body of anecdotal evidence regarding the existence and influence of the informal organization—middle managers that have been with a company so long that they are far more effective in getting proposals through and accessing necessary resources than new hires who may be higher on the formal organization chart, bottlenecks in communication between two key departments because of personal conflicts between the department heads and the continuous exchange of information between departments that occurs at the side of the building where smokers congregate. Informal organizations are not a complete substitute for the fundamental principles underlying the organization chart—task description, supervision and authority; however, informal organizations do provide clues to who within the company are looked to as leaders and senior managers of emerging companies should know and understand some of the tools that are available for gathering knowledge about the informal organization and be prepared to look for ways to use what is known about the informal organization to improve company performance.
A common tool that companies use for identifying the boundaries of their informal organizational structure is “social network analysis” (“SNA”), which is sometimes promoted to companies by management consultants as “organizational network analysis.” While there are multiple definitions of a “social network,” the consensus seems to be that a network consists of “nodes,” which can be individuals, formal or informal groups or other organizations, that are have developed interdependencies, referred to as “ties,” with one another within the defined network based on certain relationships—friendship, conflicts, values and ideas, or business transactions. SNA is the art and science of attempt to map and measure the relationships within a network and identify how information and knowledge flows back and forth between the nodes using the ties that are identified during the course of the analysis. A significant amount of research has been conducted in this area and social networks have been identified on a number of levels—from families to groups of nations—and the general conclusion in the business area has been that these networks can and do play a crucial role in how companies operate and address opportunities and threats and how the employees within those companies conduct their day-to-day activities and develop perceptions about how the company is managed.
In order to understand how a social network operates, analysts perform certain tests to determine the “location” of each node, measured by its level of “centrality,” and its relationships to other parts of the network. Social network analysts produce maps of the network that show how all the nodes are tied together (“connected”) and identify who is in the core of the network, who is on the periphery of the network, groupings of nodes and their members, and the roles that certain nodes play in the operation of the network (e.g., leaders, connectors, etc.). The first step in creating the map is to chart the connections between nodes, which exist whenever two nodes regularly communicate or interact in some meaningful way. This information is then used to generate various measures, or metrics, that track the centrality of each node and the strength and importance of the connections between the various nodes. The most commonly cited measures of the individual centrality of a node are as follows:
The level of “degree centrality” refers to the number of direct connections associated with a node. Nodes with the most connections are referred to as a “connector” or “hub” in a network; however, the number of connections is just one part of the story and role and importance of the node in the network is also heavily influenced by which nodes are at the other end of these connections and how those nodes are connected to other parts of the network.
The level of “betweenness centrality” focuses on connections with different groups within the network. A node may have a relatively low level of degree centrality due to a small number of direct connections; however, if the node is the sole link between two important groups that are not otherwise directly connection with one another it can play a powerful role as a “broker” of relations between the groups and a conduit of information and knowledge between different parts of the network that otherwise would not communicate.
The level of “closeness centrality” measures the relative distance of a node from all other parts of the network based on the node’s direct and indirect connections. Nodes with high closeness centrality are best positioned to monitor information from all parts of the network through their “grapevine”.
After the data regarding the network is collected and categorized, social network analysts can determine the relative importance of each node in a network by aggregating those measures taken of the node with respect to the number and strength of connections, the degree to which the node connects groups that are not otherwise linked to one another, and the amount and quality of information that actually flows through the node to other parts of the network. In addition, SNA facilitates the development of various conclusions regarding the characteristics of the network as a whole and various clusters of nodes within the network. For example, networks can be characterized as centralized or decentralized depending on the degree to which key links are associated with a relatively few number of nodes (i.e., hubs that have high levels of both degree and betweeness centrality). In addition, the relative cohesiveness of groups of connected nodes can be determined and nodes that are closely linked to one another at the expense of less direct ties to other nodes can be classified as “cliques.” Group cohesiveness is also important in determining the effect that removing members would have on the connectivity within the group—for example, removing one key node may cause communication among the other members to collapse completely due to the unique role that the node played in ensuring that information flowed to each member.