I am sure you will find it somewhat odd that the CEO of a predictive analytics company will agree that the understanding of the past is very important.
Businesses naturally want and need to know why things happened and they use past information to figure that out. But restricting your analysis to that alone can feed into a culture of decision makers who don’t fully grasp the complexity of the processes at their disposal.
With a little coaching, I’ve found that RapidMiner clients can quickly adapt to new technology – and it’s impressive how fast they can change the TYPE of questions they are asking.
How often do you think:
- What will happen next?
- What is my best option at this point?
- What else do I need to plan for?
Previously, companies have just analyzed past data to discover what happened, when it happened, and why it happened. OLAP and visualization tools are doing a great job of this, describing and providing simple explanations for past patterns. Predictive analytics makes those patterns explicit, allowing you to shift your focus to the future instead of the past.
Because of this, more and more companies are moving towards models that not only describe the past, but give insight into the most likely future. Now that we can answer these questions, the way analytics serves businesses has completely changed.
Instead of being reactive, just answering questions in reports and dashboards, companies can now incorporate those predictions into their own business processes and act in real-time. Sometimes this even happens automatically – before the actors realize they’ve been “predicted.” This is great news for businesses!
Good examples of the fruitful use of this paradigm shift are fraud detection, churn prevention, credit scoring, and direct marketing. It’s broader than that, though. Completely different areas are being opened up with real-time predictive analytics. Being able to predict machine failures before they occur, for example, can be a lifesaver in the manufacturing industry.
With advanced predictive analytics, new doors are opening every day for businesses to grow and prosper. How could you use RapidMiner to help you predict the future for your company? Good question, don’t you agree?
Do you have a story about how you use RapidMiner or a particular use case that others might find interesting? We want to hear from you! Please share your story.
Previously, companies have just analyzed past data to discover what happened, when it happened, and why it happened.
Predictive analytics continues to evolve and the ongoing quest is to build computer systems capable of understanding concepts rather than just keywords.
There’s a lot of ways to use analytics in applications, but you won’t be able to do it efficiently unless you operationalize predictive analytics.