shown in Figure 3. This
probably reflects the
use of new data types
to improve the predictive power of existing
models. Teams are still
beginning largely with
traditional data types,
but they see increased
value from new data
types once they have Figure 3: More experienced practitioners use more data types.
some success. With
more successful, more established
stored back into a database for later use
teams using big data more broadly, it
to real-time scoring. This shift is reflected
seems likely that there will soon be a
in the increased use of intra-day and realrapid and significant growth in the use
time data in predictive models. As one surof new data types in building predictive
vey respondent put it: “Intra-day data will
analytics. More traditional structured
be the most valuable to our company since
data will likely remain broadly central
we are open 24 hours.”
to effective predictive analytic modScoring streaming data is not yet a
els. One survey respondent put it this
mainstream use case though it seems
way: “Big data is a misnomer as data
likely that the general shift to a more eventhas always been big. The challenge
centric, real-time world will bring it squarely
is making use of semi-structured and
into the mainstream before too long.
unstructured data in solutions. This
Back in 2011 it was clear that early
will be the next giant leap forward in
adopters were going to get an edge.
using data.”
They were more likely to have plans for
The velocity of data also matters. Prebroader deployment and saw predictive
dictive analytics is increasingly focused on
analytics in the cloud as more valuable.
near real-time, op erational data. This kind
This trend strongly repeated in 2013.
of data grew the most in importance beOnce again, early adopters with one or
tween 2011 and 2013. This corresponds
more use cases deployed were signifito the general shift in predictive analytcantly more likely to have plans to exics from batch scoring where results are
pand deployment. Similarly those with
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