Predictive analytic's usage and challenge

1. Predictive Analytics usage and challenges.
First thing, in this economy every business want to leverage value from
everything in the form of data. There is data everywhere and companies want
to tap value using analytics. The value of predictive analytics comes from
growing revenue, lowering costs, Customer retention through analytics, cross-sell
and up-sell, Fraud risk, Customer interactions, process improvement for
compliance & governance.
Predictive analytics is useful in every sector. Some companies are large and
matured and other growing. Mature companies have matured model to
leverage the untapped business by using predictive analytics
When economies are down every one want to accelerate growth
Utilizing the data and predictive analytics and finding the valuable in data and
information.
Companies considering implementing Predictive models have to consider
whether the depth of the data available is good enough for the financial
investment. There may be huge initial investment due to business process
around this. Which includes Technology workers, right technology, right
knowledge, vision, data management & exploration, best technique
deployment, deployment, maintenance and monitoring. Human skills are major
part of this process which should be aligned with the technology usage. People
with business understanding is also one of the important skills that is required. Big
data being explored by many companies to leverage the value of data residing
in huge quantities and the challenge for these companies is to make the data
qualitative for the analytics. So statistics, visualization and right modeling are
part of the challenge too.
Vendor in this area coming out with software that is easy to use with graphical
capabilities and features than using model builders with programming and
scripting. This is going to help in this area because statistical and data mining
skills are in short supply.