Risk Awareness Week, October 9-13, 2023
This annual virtual event delivers real-life case studies and hands-on workshops focused on seamlessly integrating risk management into corporate decision-making, planning, budgeting, project management, and risk-adjusted performance management.
On behalf of Claridec , Craig Mitchell and Diana Del Bel Belluz will present a session on Conducting a Risk Appetite Workshop. (Claridec is a joint venture between Risk Wise, NavIncerta, and 6DQ formed in 2020 to offer training and advisory services in leading practices for decision-making under uncertainty and risk.)
“Since taking the Masterclass on Decision Clarity, I am now thinking of ways to integrate Decision Clarity approaches within my organization, starting with the program’s Apply-in-Practice project.”
Free ‘Light’ Virtual Learning Bites:
A ‘light’ bite gives you free access to online (self-study) lesson content (includes videos, readings, and examples).
- Decision Clarity. This ‘light’ Virtual Learning Bite covers how to get clarity on the decision context to help you come up with far better solutions and save you from wasting time and money on hasty, ill-considered options. It introduces fundamental concepts to improve the quality of organizational decision making including: What are the key attributes of a decision? When can a decision be considered sound? How do we address underlying uncertainty and risk? What do we mean by decision quality? What are simple ways to improve the quality and speed of decisions?
- Italian Flag. This light Virtual Learning Bite introduces a technique for quantifying the probability of a future event. This evidence-based process helps to de-bias risk quantification and can be applied by an individual or in a group setting. It is especially effective for understanding new risks or ones that you haven’t previously quantified.
- Range Assessment. This light Virtual Learning Bite introduces a technique used to quantify the potential range of a risk impact. This helps to give a more realistic risk estimate by removing the bias inherent in using a single point estimate to represent the size of a risk impact when it is actually a probability distribution (i.e., a range of possible impact magnitudes).