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 analyticsWhen economies are down every one want to accelerate growthUtilizing 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.