ANALY TIC S I N T H E C LO U D
historically been both very high ROI and
very high cost. There has been constant
pressure on the market to deliver ROI
solutions more cost-effectively, and this
is clearly driving cloud deployments of
predictive analytics. The typical obstacles
to predictive analytics also came through
in the survey: data security and privacy,
along with regulatory and compliance
concerns, remain the primary obstacles
reported. As one respondent said, “Cloud
based solutions mean either storing or
transmitting our proprietary data to the
cloud. Although there are safe ways to do
this, our management is not convinced.”
Predictive analytics has a strong
history in credit risk and fraud detection.
Recently, much of the market’s energy
has been directed toward the use of
predictive analytics for maximizing the
opportunity from customer interactions,
often positioned as cross-sell/up-sell.
The big focus area for predictive
analytics among respondents is in
customer interaction; however, the
particular focus of respondents was
on customer satisfaction, customer
retention and customer management
rather than on increased sales. Many
respondents use predictive analytics
in marketing and cross-sell/up-sell,
but the number one focus is using
predictive analytics to improve customer
engagement.
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A N A LY T I C S - M A G A Z I N E . O R G
Given the importance of the cloud to
big data, with so many new data sources
being cloud based, it seemed appropriate
to investigate the impact of big data on
predictive analytics in the cloud. In particular, the survey explored the degree
to which new data types (the variety aspect of big data) and “recent-cy” (the velocity aspect of big data) were impacting
respondents.
When asked what data matters most
to predictive analytic models, the vast
majority of most respondents indicated
it was what you might call traditional
data types, and structured data from
their own internal systems was by far
the most important. The survey also
revealed a definite sense that unstructured data from internal systems was
becoming mainstream, while no other
data types were deemed particularly
important.
When more experienced analytic
teams were separated out, however,
and only those with existing deployments or significant impact were considered, the picture was quite different.
These more experienced teams show
much higher usage of new data types
than in 2011. Social media, sensor,
weblog, audio and image data types
are all rated as much more important
in analytic models among those with
successful analytic deployments as
W W W. I N F O R M S . O R G