Architecture Components - Big Data Analytics: Disruptive Technologies for Changing the Game - page 48

or pixel (see note 29 for web tracking technologies). Under opt-in, if we placed
a coupon on a smartphone and the person opted-in by accepting the coupon,
we may have a fair amount of history about the individual. The analytics engine
maintains a detailed customer proile based on past-identiied history about the
entity. The predictive modeler uses predictive analytics to create a cause-effect
model, including impact of frequency (e.g., saturation in advertisement place-
ment), offer acceptance, and micro-segmentation. The scorer component uses
the models to score an entity for a prospective offer.
While sensor and scorer components may operate in real-time, the analytics
engine and predictive modeler do not need to operate in real-time but work with
historical information to change the models. Returning to our example of online
advertising, a cookie placed on the desktop identiies me as the movie watcher
and can count the number of times an ad has been shown to me. The scorer
decrements an advertisement based on past viewership for that advertisement.
The analytics engine maintains my proile and identiies me as someone search-
ing for a food processor. The predictive modeler provides a model that increases
the score for an advertisement based on past web searches. The scorer picks up
my context for web search and places a food processor advertisement in the next
advertisement placement opportunity. The sensor and scorer work in milli-
seconds while the analytics engine and the modeler work in seconds or minutes.
Without a proper architecture, integration of these components could be
challenging. If we place all of these components in the same software, the diver-
gent requirements for volume and velocity may choke the software. The real-time
components require rapid capabilities to identify an entity and use a number of
models to score the opportunity. The task is extremely memory and CPU inten-
sive and should be as lean as possible. On the other hand, the analytics engine
and predictive modeler may carry as much information as possible to conduct
accurate modeling, including three to six months of past history and the ability
to selectively decay or lower the data priority as time passes by or subsequent
events conirm purchases against previously known events. I may be interested
in purchasing a food processor this week, and would be interested in a couple
of well-placed advertisements, but the need will diminish over time as I either
purchase one or lose interest.