Advanced Analytics Platform - Big Data Analytics: Disruptive Technologies for Changing the Game - page 57

relection requires predictive modeling or unstructured data correlation capabil-
ities and can best be performed using SPSS or Big Insights. Directed attention
may be provided using a set of conversation tools, such as Unica ® or smartphone
apps (e.g., Worklight™). Management reporting and dashboard may be provided
using Cognos. Depending on the level of sophistication and latency, there are
several components for the box in the middle, which decides on the orchestra-
tion focus, directs various components, and choreographs their participation for
a speciic cause, such as getting Lisa to buy something at the store.
Entity Resolution
Using a variety of data sources, the identity of the customer can be resolved
by IBM's Entity Analytics ® . During the course of the entity resolution, we
may use offers and promotion codes to encourage customer participation, both
to resolve identity as well as to obtain permission to make offers (as in Steps 4
and 5 above).
Model Management
IBM SPSS provides collaboration and deployment services, which are able
to keep track of the performance of a set of models. Depending on the criteria,
the models can be applied to different parts of the population and switched, for
example, by using the champion/challenger approach.
Command Center
A product manager may set up a monitoring function to monitor progress for a
new product launch or promotional campaign. Monitoring may include product
sales, competitive activities, and social media feedback. Velocity from Vivisimo,
a recent IBM acquisition, is capable of providing a mechanism for federated
access to a variety of source data associated with a product or customer. A
dashboard provides access to a set of users monitoring the progress. Alterna-
tively, the information can be packaged in an XML message and shipped to
other organizations or automated agents.
Analytics Engine
An analytics engine may provide a mechanism for accumulating all the
customer proiles, insights, and matches as well as capabilities for analyzing
this data using predictive modeling and reporting tools. This analytics engine
becomes the central hub for all information lows and hence must be able to deal
with high volumes of data. IBM's Netezza product has been successfully used as
an analytics engine in Big Data architectures.