BIG DATA will be the next frontier for innovation, competition and productivity and analytics will play an key role in adding value to data bases.

BIIA finds the recent FICO Banking Analytics Blog written by James Taylor, CEO of Decision Management Solutions of particular interest: 

Individually, predictive analytics and cloud computing are hot topics in business today. But what’s the potential for the intersection of these two exciting technologies?

Based on new research and a recent survey of over 200 professionals, cloud-based predictive analytics are indeed poised for rapid growth. While industries vary in their maturity, the use of cloud-based predictive analytics to improve an organization’s focus on customers is particularly powerful. As early adopters look likely to build a sustained competitive advantage, organizations should have a plan to rapidly adopt cloud-based predictive analytics to avoid being left behind.

There were a number of interesting implications from the survey results: 

  • Business solutions are what organizations need.  Most potential buyers of predictive analytics in the cloud are not really looking for “cloud” solutions. Many organizations are not looking for “predictive analytic” solutions either. What the vast majority of organizations seek is a solution to a specific business challenge.

  • Predictive analytics are showing real strength.  82% of those surveyed work at companies that either have specific plans to adopt or are already using predictive analytics. More than one in 10 of those responding said the impact of predictive analytics has already been transformative.

  • Customers are the focus. It perhaps comes as no surprise that the core focus for predictive analytics, and for predictive analytics in the cloud, is improved targeting and development of customers.

  • Cloud-based predictive analytic scenarios are gaining momentum.  We presented five areas of opportunity: cloud-based predictive analytic solutions (decisions as a service), cloud-based deployment of predictive analytics into SaaS applications, cloud-based deployment of predictive analytics to on-premise applications, using cloud-based data in modeling and pushing modeling to the cloud. All five scenarios were seen as potentially powerful solutions with over 2/3 of respondents reporting that each of them has real potential. None of them are that widely adopted yet, but pre-packaged analytic applications have the greatest penetration.

  • Early adopters are gaining a competitive advantage. Organizations with the most experience with predictive analytics were more likely to have plans to adopt more cloud-based predictive analytic solutions.

  • Decision Management matters to predictive analytic success.  Among those transformed by predictive analytics, a whopping 2/3 (64%) said they tightly integrate their predictive analytics with day-to-day operations.

  • Decision Management was clearly an important element for successful analytic adopters. We asked companies how they used predictive analytics; overall, people were split between predictive analytics providing occasional insight and predictive analytics being tightly integrated in operational systems (the basis of Decision Management). But when you focus in on those who have already seen significant positive results from predictive analytics, the percentage tightly integrating predictive analytics into operations rose, while occasional use dropped. Among those transformed by predictive analytics, a whopping two-thirds tightly integrate their predictive analytics with day-to-day operations! 

  • These more successful companies also valued different types of data for building models. Near real-time and real-time data were seen as more important by the respondents overall. Among those with more experience, both batch and static data scored much higher. Experience clearly shows that less volatile data can be valuable too. 

  • Finally, a couple of surprising results. I really thought that more experience with predictive analytics would make people more tolerant of “black box” models. In fact, the percentage who really wanted transparency in their models started high (well over half) and climbed to 80% among those with the most positive results so far. 

  • Even success does not make people comfortable with black box models, it seems. On the cloud front, I really thought that transaction-based pricing—pay as you go—would be a big driver, but it did poorly across the board. Reducing the demands on IT and empowering the business were what people were looking for from cloud. I think transaction pricing has a lot to offer folks with decisions as a service cloud-based solutions in particular, but it’s not apparent that the survey-takers agree with me.

BIIA believes that analytics will be the place where the next battle will be fought amongst business information services providers.  Without extreme analytics businesses cannot retrieve value form internal or external databases and make informed decisions just in time. 
Skills in analytics and statistical science are in short supply and may become unaffordable for smaller companies.   The next shake-out of the b2b business information industry may be around the corner.

Source:  FICO Banking Analytics Blog  Predictive Analytics in the Cloud