By studying data from email archives and other sources, managers can gain surprising insights about how groups should be organized and led — as well as about optimal participation and communications patterns.
What if — far enough in advance that you could do something about it — you could gauge the likelihood that some of your company’s top performers were thinking about leaving? Or if you could see from the email interactions between your best salespeople and customers which communication behaviors had the most chance of generating successful results? It so happens that by sifting through data from such sources as email archives, Twitter feeds, and Facebook group pages, managers can in fact learn a lot about how to manage these and other areas of their organizations. They can then use this information to generate better results.
Over the last 15 years, I have worked with researchers at the MIT Center for Collective Intelligence, Wayne State University, the University of Cologne, and the University of Applied Sciences Northwestern Switzerland (FHNW), studying hundreds of organizations through the lens of their social networks as portrayed by email and other electronic archives. The high-level goal is to develop metrics and software tools that make measuring informal communication within organizations as straightforward as handling payroll and accounting. Much as enterprise software companies such as SAP SE specialize in helping companies monitor financial information and structured business processes, we seek to enable organizations to track informal knowledge flows.
By studying widely available internal data, we have mapped social networks in a variety of settings: R&D organizations at auto companies; bank marketing departments; sales teams at high-tech manufacturers; hospitals; and large consulting and process-outsourcing firms. In addition, we have studied open-source organizations such as software developers, Wikipedia editors, and online communities of patients with chronic diseases. Studying all of these organizations has helped us identify key criteria that distinguish high-performing organizations from underperforming ones and highly innovative teams from less creative ones. (See “Related Research.”)
I first noticed the distinctive communication behaviors of collaborative innovators back in the early 1990s while working as a postdoctoral fellow at the MIT Lab for Computer Science.