We would really like your input for our 2013 survey on Predictive Analytics in the Cloud – Use Cases, Trends and Big Data. Back in 2011 we did a survey and associated research on the use of predictive analytics in the cloud. While it may not seem like that long ago, back then predictive analytics was just beginning to explode onto the scene. Now everyone is talking about Big Data and the potential for cloud-based predictive analytic solutions is clear. It’s been an exciting 18 months so it’s time to find out what’s changed.
Please go ahead and take the survey here – it should only take about 10 minutes, its completely anonymous and you have a chance to win a $100 Amazon gift card.
So why take the survey? Well there’s been tremendous growth in the use of cloud infrastructure to enhance and expand the use of predictive analytics. The intersection of predictive analytics and cloud is also creating new ways to exploit Big Data. This survey will provide insight as to where organizations are today, where they plan to go tomorrow, and how the perception of predictive analytics in the cloud has changed in the last 18 months. 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.
It will be interesting to see what’s changed and what trends we can discern. The different use cases and their adoption will be central to this. When we first looked into predictive analytics in the cloud we identified five use cases but it’s clear now that we can simplify this down to three:
- 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.
Obviously individual products often combine elements of multiple use cases but it’s clear that these are the drivers of value for predictive analytics in the cloud. The changes in the adoption and perception of these use cases, as well as the role Big Data is playing, will be central to the research. This year’s survey asks about your experience with predictive analytics, what you think about the main use cases for predictive analytics in the cloud and also about drivers, obstacles, and the role of Big Data.
We’re delighted to have the support of FICO, Lityx and SAP for this year’s survey.
You can take the survey here. I hope you will. Thank you in advance for your time.