If you've read our or someone else's description of graph theory, you may have guessed how we use it in our analytical methodology.
Since graph theory is useful in analyzing situations that can be modeled as a set of points connected by links, we use it to better understand what is going on in organizational workforces. We view every employee as a point in the network of all employees (even past employees if you have that data) who are linked to other employees when they work together to complete an organizational task. We can even create networks that contain employees who no longer work for your organization. (See our datasets for sale that include every U.S. Major League Baseball player who played in any league-sanctioned regular season game since 1914. That network includes almost 20,000 players!)
Given any type of internal organizational system that contains connection-based data, we can calculate every single link that any employee has ever made to any other employee. We can do this even if the only data you have contains groups of people, such as project teams or workgroups, that came together to work on a task or project. Email systems also contain information about employees who made connections to each other so we can mine that data as well to do our analysis.
Our analytical methodology is much more realistic and precise than employee surveys that ask questions such as "Which co-worker do you think knows the most about this topic?" This is so because research shows that human beings have internal and sometimes subconscious biases for or against other people or topics. Research also shows that questionnaires often miss these biases, whereas our analytical process examines data based on what employees DO, not on what they SAY. So, you can move forward assured in the knowledge that you have results based on your employees actions, not their possibly biased statements.