It used to be that analytics were applied in batch, updating the database with a score or customer segment based on yesterday’s data.
It used to be that these models could take months to implement, so that the models themselves were based on data that might be months out of date.
It used to be that analytic teams could just produce scores or other predictive models and leave it to someone else to worry about how they might be used and integrated into operational systems.
But today’s world is real-time. Customers want answers in real-time. This means companies must decide in real-time using up to the minute data. And the world is changing fast so models need to be built with up to date data and rapidly deployed to keep them fresh and accurate.
Organizations need a decision management approach and a real-time infrastructure to take full advantage of predictive analytics:
- Discover the operational decisions that make a difference to business objectives
- Rapidly build analytic models that can influence these decisions
- Wrap these analytics with the rules of their business – regulations, policies, best practices
- Deploy these analytics quickly and accurately to their operational infrasturcture and hook them up to real-time data