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Editor's note: Want
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up on April 7, 2010.
The assessment of what went wrong in the recent Christmas Day airliner attack
over Detroit is clear: we should have done a better job identifying the warning
signs of a pending attack by connecting the dots across a series of data sources.
Because of the severity of the potential outcome, this incident takes on a
level of scrutiny that is unparalleled. But terrorism is just one example where
the ability to "connect the dots across data sources" and share the
results can have tremendous benefits.
Criminal activity is climbing as individuals attempt to conceal their identity
in the darkest reaches of the Internet. Countless situations such as fraud,
money laundering, cyber attacks, data breaches, illegal gambling, and child
pornography exist where the ability to obtain relevant, timely analytical results
could prevent serious harm from happening to people and organizations.
The need to connect the dots is not limited to law enforcement and counter
terrorism. Data analysis plays an important role in virtually every industry.
Businesses, for example, need to better understand financial performance. Manufacturers
need to be aware of the dependencies between orders, inventory, shipments, and
delivery timetables. Pharmaceutical companies need to understand the results
of new, vitally important drug trials and the resulting interactions on patients.
Energy companies need to explore sources of energy, to name a few.
Unfortunately, traditional business intelligence (BI) and data mining applications
provide only a modicum of analysis. Imagine what the result would be if the
analyst could visualize disparate data in rich pictures and draw linkages between
people, places, and events? How much better could the result be if the same
analyst could quickly connect to new data sources and uncover meaningful insight
in minutes? How beneficial would it be if results could be shared in real time
with someone halfway around the world?
Traditional BI solutions do not allow for this level of exploration. Rigid
in nature, they can be hard to install and difficult to learn. Most existing
BI tools rely on underlying data models used in the analysis. If the data needed
for the analysis is not present, long cycle times and missed opportunities follow.
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