Superman is special because he's from another planet and can do things nobody else can. In our network of employees, your Supermen (and superwomen) are the key actors in your network. And as they become known for being both capable and willing to help, they’re involved in projects of growing importance.
But we all know that even Superman is not completely unstoppable. There’s Kryptonite, the one thing that renders him helpless. The Kryptonite of your top collaborators is their limited time and resources. And even if intense collaboration starts as a virtuous circle, it can soon turn vicious. Without proper management, helpful employees might soon become institutional bottlenecks. Worse, they might become so overwhelmed by their network that they’re no longer effective.
Network analytics can help by highlighting the top collaborators that show up too often on the critical paths of too many projects.
The best way to prevent burnout is to proactively redistribute workloads. You can identify critical employees in a company network by plotting employees’ scores for various measures typically used in graph and network theory, for example, Eigenvector centrality versus Betweenness:
- Betweenness measures the number of shortest paths an employee is on. A high score indicates it’s a likely path for information flows. In a directed network, top collaborators who are unreasonably solicited appear as typical bottleneck in the project flow.
- Eigenvector centrality is proportional to the centrality of an employee’s neighbors in the network. A high score indicates the employee is popular among popular employees, which might further fuel the waterfall effect of growing workload.
The following SAS Visual Analytics graphs highlights three employees with a high score on both measures. Their position in our network confirms that those employees might be at risk with the current distribution of the knowledge and of the project tasks.
Opportunities at the managing and hiring level
Everyone has an agenda -- you, me, and every potential candidate in the marketplace. Evolution made us goal-driven with a primary objective: Physical, and social survival. As far as our brain is concerned, without a goal everything is meaningless.
In our flat world, employee’s collaboration, learning and performance goals are most likely to accurately predict success. Analytics that combine demographic employee data, predictor variables (goals) and mediating variables (such as proactive behavior and emotional intelligence) will efficiently assess employees for future performance. Considering the amount of cross-functional interaction and emotional intelligence that’s required for managers in a collaborative ecosystem, this could prove an invaluable metric for hiring the managers you need. As Charles Darwin says: The grain in your balance.
“A grain in the balance will determine which individual shall live and which shall die - which variety or species shall increase in number, and which shall decrease, or finally become extinct.”
― Charles Darwin, The Origin of Species
To learn more about the power of visualizing your data, check out this Harvard Business Review white paper: Visualizing Data