This is the last week for our 2013 survey on Predictive Analytics in the Cloud – Use Cases, Trends and Big Data. Please go right now and take the survey here – it’s quick , anonymous and you have a chance to win a $100 Amazon gift card.
There’s been tremendous growth cloud and predictive analytics both separately and in combination. Plus the combination is particularly powerful when exploiting Big Data. Taking the survey will help us understand where your organization is today, where you plan to go tomorrow, and what’s changed since the last survey. The results will yield valuable insights into emerging best practices, the kinds of technologies you should adopt at different stages, how fast you should move and what your competitors might be doing.
So go ahead, take the survey.
Three main use cases drive Predictive Analytics in the Cloud and we want to know how you see them:
- Pre-packaged, cloud based decision making systems, also called “Decisions as a Service”. In this use case, pre-packaged, cloud-based solutions are purchased and used to provide decision-making based on predictive analytics. For example, packaged solutions offering next best action, marketing offer selection, fraud detection or instant credit decisions as a service.
- Cloud-based solutions to define and build predictive analytic models. These solutions can handle data from both cloud and on-premise solutions. They add value by moving analytic modeling closer to the data available in the cloud and by taking advantage of the elastic nature of cloud solutions – efficiently support demanding analytic algorithms by assigning compute resources as necessary. For instance, building complex predictive analytic models in the cloud using many compute cores and data stored in a cloud-based system, uploaded from an on-premise solution or available from a cloud API.
- Cloud-based deployment to embed predictive analytics. This use case is the use of cloud-based deployment to embed predictive analytics, no matter how developed, in existing systems that don’t have their own predictive analytics. The target systems might be custom or packaged solutions and be delivered in the cloud or on-premise. For example, using cloud-based deployment to embed ‘customer churn’ predictions in a cloud CRM solution or using cloud-based deployment to link internally developed ‘propensity to buy’ models to multiple customer-facing systems.
We’re delighted to have the support of FICO, Lityx and SAP for this year’s survey.
You can take the survey here for the rest of the week. I hope you will.