In previous posts I described the first three phases of the Data Analytics Lifecycle. The fourth phase is where the rubber meets the road.
Data Scientists begin running their models and trying to prove the hypotheses established in Phase 1.
In Phase 3 I mentioned that the use of Social Network Analysis (SNA) could help prove the following hypothesis: H5: Knowledge transfer activity can identify research-specific boundary spanners in disparate regions. The visualization below is described as follows:
The input for this graph is from EMC’s Innovation Showcase (an idea submission contest). Each circle in the graph represents an idea submitter that was part of a team (i.e. more than one submitter on an entry). Gray lines between circles represent team relationships – two circles connected by a line indicate that those participants submitted an entry together. The size of each circle represents the associated participant’s number of contest entries. Orange circles represent contest participants with an entry selected as a finalist.
I’ve highlighted five “clusters” of idea submitters/inventors. In a previous post, I drilled down into one of these clusters and discovered that each submitter was Irish. I followed up with several of these individuals and discovered that the cluster had formed as a direct result of targeted innovation training that had occured at EMC’s facility in Ireland. The analytics run by EMC Data Scientist John Cardente turns our employee idea database into numerical representations, and these values are then displayed visually to help prove or disprove the hypotheses. Does the knowledge transfer activity that occurs as part of our Innovation Showcase contest identify knowledge-specific geographic boundary spanners? I asked John to drill down into the lower central cluster. He put together this color-coded visualization:
Red dots represent EMC employees from Israel. Purple dots represent employees from the United States. The two blue dots located towards the upper right are French EMC employees. The lone orange dot on the left represents an inventor from Australia. The yellow dots, representing one of the largest clusters in the entire experiment, are Chinese innovators. Any large dot with a red outline represents a “hub”. A hub has a large number of connections and high betweenness (hub discussed in more detail in a previous post).
When looking at my co-workers from China I saw two values that jumped out at me. I’ve listed the top five Chinese “betweenness” rankings within the graph above. Two out of the five Chinese employees had betweenness scores that were much, much higher than the rest. I asked John to elaborate on what betweenness means in this context:
The social network analysis metric of betweenness is a measure of a node’s importance to the connectivity of a graph. In the case of our Innovation contest, if a person has a high betweenness score, this means that they have a high degree of influence on the other inventors that are submitting ideas.
Has betweenness identified boundary spanners in China? In my next post I will explore who some of these people are, and whether or not they truly serve as boundary spanners for geographic knowledge.
image credit: di.dk
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Steve Todd is Director at EMC Innovation Network, and a high-tech inventor and book author “Innovate With Global Influence“. An EMC Intrapreneur with over 180 patent applications and billions in product revenue, he writes about innovation on his personal blog, the Information Playground. Twitter: @SteveTodd
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