Several weeks ago I published a blog post (Different Minds Think Greatly) that explored the topic of cognitive diversity and innovation. At the time, I had read a Techonomy article by John Hagel and John Seely Brown, who basically asserted that “too much like-mindedness hurts companies”, and I quoted the following:
Organizations that host a diverse and broad range of members have a resilience that results from cross pollination.
As part of the article I echoed my agreement with this assertion and referred to some Social Network Analysis from my own company (EMC). The data that we modeled highlighted that a good degree of geographic diversity can result in higher quality ideas. “Higher quality ideas” are typically defined as ideas that receive a high score from judges in our yearly Innovation Roadmap program (especially ideas that reach finalist or funded status).
Our data shows that when diverse minds from different cultures collaborate on new approaches, good ideas result. The conclusions we drew from this analysis have resulted in behavioral change at EMC. Most notably we’ve formed a global “Innovation Best Practices Community” in order to intentionally stimulate this behavior.
After publishing some of our findings, we were approached by two universities on an interesting joint research project. They asked if we wouldn’t mind sharing a filtered view of our employee idea activity in order to correlate it against the public Twitter profiles of these same people. Professors Eoin Whelan (NUI Galway) and Salvatore Parise (Babson) invited us to focus on an offshoot of cognitive diversity known as “structural holes”. Eoin explains structural holes in the following manner.
Ron Burt’s theory of structural holes has proven to be influential in explaining how innovation transpires. Burt proposes that gaps in a social network, structural holes, create brokerage opportunities. A structural hole indicates that the people on either side of the hole circulate in different flows of information and advantages accrue to those individuals whose relationships span the structural hole. In his best selling book The Tipping Point, Malcolm Gladwell argues that the success of Paul Revere’s midnight ride was due to his quite diverse social networks – ranging from hunting and card playing to theatre and business. Therefore, he knew which doors to knock on when arriving in a town. Network brokers like Revere not only disseminate information more broadly, they also benefit by receiving a greater novelty of information from their diverse social contacts. Indeed, studies within organizations have shown that employees, teams, and even entire companies with more diverse network connections tend to be more innovative.
As a result of our conversation, we polled our global Innovation Best Practices community and asked idea submitters to voluntarily share their Twitter handles with Eoin and Sal. We packaged up the Twitter handles with the employees’ corresponding level of innovation activity over a period of several years.
This research has been ongoing for several months, and in an upcoming post I plan to share some of the results and what it might mean for our organization. Before I do, however, I’d like to discuss this Data Science project in the context of Phase 1 of the Data Analytics Life Cycle: Hypothesis Generation.
Our hypothesis could be stated as follows:
EMC employees with diverse external Twitter networks submit higher quality ideas.
By “diverse” we mean “disconnected” or “fragmented”. In other words, employees that follow people that are not “like-minded” tend to submit better ideas due to the diversity of their network.
If this hypothesis proves to be true, we can brainstorm ways of stimulating additional behavioral change via encouraging our employees to fragment their Twitter networks. Proving the hypothesis involves iterating through the additional phases of the Life Cycle (e.g. Data Prep, Data Modeling, etc).
In future posts I will share the results of the modeling exercise conducted by Eoin and Sal.