Proactive vs. Predictive vs. Reactive

Updated: Jan 31

Predictive analytics is a topic of much discussion these days and is considered by some to be a proactive measure against safety, quality, environmental, and regulatory failure.


Predictive analytics can help to prevent a total failure if controls can respond fast enough and if the failure mode is predictive in the first place.


However, when uncertainty (the root cause of risk) is connected with natural variation (aleatory uncertainty) we cannot predict outcomes. Also, when uncertainty is due to a lack of knowledge (epistemic uncertainty) prediction is limited based on the strength of our models, experimentation, and the study of cause and effect.


Predictive analytics is not a substitute for effective risk management.

To properly contend with risk we must be proactive rather than only predictive. We need to estimate uncertainty (both aleatory and epistemic), its impacts, and the effectiveness of the controls we have put in place either to guard against failure (margins) or reduce its likelihood and severity (risk buy-down).


Proactive vs. Predictive vs. Reactive

Lean Compliance helps companies adopt and improve compliance systems to better meet performance and outcome-based obligations.

We offer specialized programs and training tailored to fit each company's size and capabilities. 

Schedule a call with us today to find out which programs are best for you.  You can book your appointment here.

© 2020 Lean Compliance™

All rights reserved.