Analyzing Organizational Structures Using Social Network Analysis

Technological changes have aided modem companies to gather enormous amounts of data electronically. The availability of electronic data has exploded within the past decade as communication technologies and storage capacities have grown tremendously. The need to analyze this collected data for creating business intelligence and value continues to grow rapidly as more and more apparently unbiased information can be extracted from these data sets. In this paper we focus in particular, on email corpuses, from which a great deal of information can be discerned about organization structure and their unique cultures. We hypothesize that a broad based analysis of information exchanges (ex. emails) among a company's employees could give us deep information about their respective roles within the organization, thereby revealing hidden organizational structures that hold immense intrinsic value. Enron email corpus is used as a case study to predict the unknown status of Enron employees and identify homogeneous groups of employees and hierarchy among them within Enron organization. We achieve this by using classification and cluster techniques. As a part of this work, we have also developed a web-based graphical user interface to work with feature extraction and composition.