Companies are now looking exclusively at business outcomes

Are we seeing the emergence of converged solutions with
BI, analytics and DSS meshed together?
One of the big areas we are focusing on is making it very easy to insert
scoring, predictive models and decision making into real-time business
processes. For example, to have a process to open a new credit card account or
file an insurance claim, that enables you to get real-time risk scores on those
accounts or claims based on predictive models. We want to make it extremely
straightforward to insert that piece into the business processes so you make
more intelligent decisions. It is really where we are going from a technology
standpoint. The point is, to make the decision support technology pervasive, so
it is no longer just the realm of statisticians and IT analysts.

Tell us about your approach in the business analytics market and its key
We provide the most sophisticated businesses and IT capabilities to clients
that help them manage, capture, integrate, analyze and predict on the widest
range of information within and outside their organization. We have done
fourteen acquisitions since 2005 to further extend our business analytics
portfolio. Moreover, a significant part of IBMs $6 bn investment in R&D is
focused on driving innovation around business analytics. As per our estimates,
the opportunity for business analytics today is $100 bn (hardware, software and
services) and growing at 8% annually, faster than the overall IT marketplace.

How does one arrive at an optimized business outcome?
You could make decisions just in the context of information available from
ERP or CRM systems; but IBM believes relevant data resides in a lot of different
places. Social networking and web 2.0 style applications, for example, are
leaving tonnes of clues behind as to what people are thinking. You can also do
surveys to develop attitudinal data. In the consumer packaged goods space, they
do focus groups and scientific research on emotional reactions to products. IBM
can pull data together from a lot of sourcesstructured and unstructured, within
the enterprise and outside the enterpriseto make the model even more accurate.
That offers advantages that are greater than what you can learn by focusing on
data within a particular application silo.

Is recession acting as a catalyst for the business analytics market?
A lot of companies are now looking exclusively at business outcomes. Yes,
you need technology and speed and feed to deliver a business outcome; but
companies are very focused on the end game. They will say, "Were a telco and we
need to reduce customer churn. How do we do that?" Or "Im a police chief and I
need to do smart things to reduce crime in my area." IBMs Smarter Planet
strategy is all about taking the petabytes of new data that gets created every
day and mining that for insights to build competitive advantages. Here SPSS fits
in really well because it comes at that challenge from the angle of
understanding social behavior. If we have a data-rich environment, we can build
a model that can predict what the outcome is likely to be.

Is business analytics getting verticalized, specifically for the needs of
verticals like financial services, manufacturing, etc?
We are headed in that direction, and we believe that you can have
accelerators and prebuilt models that can significantly reduce the time to
value. We see models as something that companies will view as the source of
their competitive advantage. There are certain things that are common to all
companies within a specific industry, but the company-specific insights that you
can add to a model can be a differentiator. A model built for one insurance
client may not apply to another. You have to look at the customers data and
then build multiple models using multiple statistical techniques. You also need
an on-going feedback loop to ensure the accuracy of your model. That means you
keep a track of accuracy using champion and challenger models.

Shrikanth G 

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