Database Reference
In-Depth Information
being undertaken. In the case of the GINA, the team discovered that many of
the names of the researchers and people interacting with the universities were
misspelled or had leading and trailing spaces in the datastore. Seemingly small
problems such as these in the data had to be addressed in this phase to enable
better analysis and data aggregation in subsequent phases.
2.8.3 Phase 3: Model Planning
In the GINA project, for much of the dataset, it seemed feasible to use social
network analysis techniques to look at the networks of innovators within EMC. In
other cases, it was difficult to come up with appropriate ways to test hypotheses
due to the lack of data. In one case (IH9), the team made a decision to initiate
a longitudinal study to begin tracking data points over time regarding people
developing new intellectual property. This data collection would enable the team
to test the following two ideas in the future:
•
IH8:
Frequent knowledge expansion and transfer events reduce the
amount of time it takes to generate a corporate asset from an idea.
•
IH9:
Lineage maps can reveal when knowledge expansion and transfer
did not (or has not) result(ed) in a corporate asset.
For the longitudinal study being proposed, the team needed to establish goal
criteria for the study. Specifically, it needed to determine the end goal of a
successful idea that had traversed the entire journey. The parameters related to the
scope of the study included the following considerations:
• Identify the right milestones to achieve this goal.
• Trace how people move ideas from each milestone toward the goal.
• Once this is done, trace ideas that die, and trace others that reach the goal.
Compare the journeys of ideas that make it and those that do not.
• Compare the times and the outcomes using a few different methods
(depending on how the data is collected and assembled). These could be as
simple as t-tests or perhaps involve different types of classification
algorithms.
2.8.4 Phase 4: Model Building
In Phase 4, the GINA team employed several analytical methods. This included
work by the data scientist using Natural Language Processing (NLP) techniques
