Predictive analytics is used in actuarial science, financial services, insurance, telecommunications, retail, travel, healthcare, pharmaceuticals and other fields (Wikipedia). But operations – manufacturing, processing, etc., have been a little slower to encompass the concept. A drilling engineer friend of mine says “just put my hand on the brake lever and I’ll drill that well”. He probably can, but few of the rest of us can, or want to.
We want to see operating parameters, performance metrics, and process trends. All this because we want to have the necessary information and knowledge to assimilate understanding and invoke our skill set (wisdom). In this scenario, we are responding to stimulus, we are applying “reactive analytics”. But systems get more complex, operations becomes more intertwined, performance expectations become razor-thin. And with this complexity grows demand for better assistance from technology. In this case, the software performs the integrated analysis and the results is “predictive analytics”. And with predictive analytics comes the close cousin: decision models and decision trees.
Sullivan McIntyre, in his article From Reactive to Predictive Analytics, makes an observation about predictive analytics in social media that is mirrored in operations:
There are three key criteria for making social data useful for making predictive inferences:
- Is it real-time? (Or as close to real-time as possible)
- Is it metadata rich?
- Is it integrated?
Having established these criteria, the nature of the real-time data and the migration of historical data into real-time, predictive analytics becomes achievable.