Predictive models are all about “propensities” not guarantees, and companies have to act at the point of impact if they want to see results.
“If I can predict the winning lottery number, but I don’t buy a ticket, it doesn’t matter.”
Predictive is not about absolutes; it doesn’t guarantee an outcome. Rather, it’s about probabilities / propensities.
For example, there is a 76% chance that this person will click on this display ad. Or there is a 63% chance that this customer will buy at a certain price. Or there is an 89% chance that this part will fail.
Good stuff, but it’s hard to understand and harder to do. It’s worth it, though: Organizations that employ predictive analytics can dramatically reduce risk, disrupt competitors, and save tons of dough.
Many are doing it now. More want to.
Predicting versus sensing and acting in real time based upon facts, not intuition.
In this “new” world, analytical expertise is also no longer the work of select back-room professionals; it is part of everybody’s job and it happens everywhere.
The demand for analytics in organizations continue to increase however the tools that you have are fragmented, gaining insight takes a lot of skill and experimentation, and buy-in for analytic driven decisions from managers can sometimes be hard to get.