WH Y P ROJ E C T S FA I L
Think about what information do I
collect today … and what analytics should
I perform that can benefit me and others.
New security and compliance
procedures to protect extreme-scale
data. In order to succeed with big data,
new processes must be developed that
recognize and protect the special nature
of extreme-scale data that may be largely unexplored.
Be ready to support rapid growth.
Big data solutions can grow fast and exponentially. They can start as a pilot with
a few terabytes of data, then becomes
a petabyte very quickly. Since the same
data can be used different ways and reanalyzed for new insights easily, nothing
ever gets deleted.
Funding must move out of IT for
big data success. Funding for these
projects should come from outside of the
CIO organization and move to a marketing or sales organization, for instance,
so that the business has a vested stake
in the game.
Create a road map that gradually
builds the skills of your organization.
It’s important to create a road map that
allows you to gradually build the required
skills within your staff, minimize risk and
capitalize on previous successes to gain
more support. In the organization, there
will be new roles and responsibilities such
as the data scientist, who possesses a
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blend of skills that includes statistics, applied mathematics and computer science.
This is different than any current
decision support solution. With big
data, organizations should look for new
capabilities, such as: using advanced
analytics to uncover patterns previously
hidden; visualization and exploration to
help the business find more complete
answers, with new types and greater
volumes of d ata to best represent the
data to the user and highlight important
patterns to the human eye; enable operational decision-making with on-demand
stream data by making floor employees
into analytic consumers; and turn insight
into action to drive a decision – either
with a manual step or an automated process. And most important be ready for
rapidly increasing benefits and complexities from the six Vs.
WHAT IS NEXT IN THE DATA
ECONOMY?
Organizations have access to a
wealth of information, but they can’t get
value out of it because it is sitting in its
most raw form or in a semi-structured
or unstructured format [3]. As a result,
they don’t even know whether it’s worth
keeping.
So where is deep analytics for
deep learning headed in the next few
years? The exciting news is that many
W W W. I N F O R M S . O R G