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[Figure]: What are the key issues in defining a data
environment? It sounds easy until you
have to do it. Then it turns out to be
harder.
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There is a lot of
data out there. What data do we
capture? The easy answer is that we
can capture all of it and then figure out what to do with it. But in some cases it is not the best idea
to capture every single data element because of communications requirements.
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The other issue is
why are we capturing the data? How do
we use the data? One size does not fit
all. There isn’t just one data
environment that addresses all applications.
We might have one data environment that supports tactical movements at
an intersection and another that supports national level freight
movement. So, data environments can
support a specific application or multiple applications, and there can be
multiple data environments.
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The 3rd
issue is what data do we keep? We may
not be able to keep all of the data because of privacy issues, IP rights
issues. We have to make decisions about what data to keep and what data to
leave out.
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Finally, how do we
structure the data? What are the
rules? Do we need guidelines? And how
do we deal with structuring data that have IP rights or privacy issues
associated with them?
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