essential decision making steps
Observe Interpret Evaluate Decide

Classify visible phenomena  by severity  using decision attributes.

Know the risk taken applying a risk based approach. Generate decision options considering datarisk and expected results. Opt for the best option considering  previous experience and risk policy.
Preserve the organization’s corporate memory and demonstrate knowledge during decision making.

Decision marks your finest hour and highest responsibility!

Risk Assessment. This is the moment that you need to select the best alternative for your case considering the importance of contribution to the organization’s overall risk and best expected results.  Traditionally, decision risk management has been viewed as “a check the box” activity, and has been approached from a compliance and due-diligence perspective only and not value creation.  The larger the business, or the more regulated the industry, the greater the potential cost of such failures. Risk management is not just about regulatory compliance. It is about your ability to quantify, model and understand your risk better in order to make better-informed decisions.

Corporate Memory & Previous Experience. Most professionals have been in a situation where they had to examine decisions made by a group of individuals no longer with the company, or who had moved to other departments. There was no supporting data to be had, nor any documentation of the decision process with dire consequences. Corporate memory is critical in data-driven decision-making. Unless you intend to rely on “matriarchs” who have been around for 30+ years! 

Single Decision Repository. The most important thing, when it comes to capturing and learning from corporate memory, is the creation of a single repository of decision assets that can be mined, searched and used to predict new decisions. This inventory of decision assets needs to hold information at all levels of granularity with information on the decision attributes applied, the knowledge and predictive models applied, decisions and documentation.

Decision Prediction. Recent developments in AI are about lowering the cost of prediction. Better predictions matter when you make decisions in the face of uncertainty, as every business does, constantly. But how do you think through what it would take to incorporate a prediction machine into your decision-making process and how can you decide whether employing a prediction machine will improve matters? ArrowMiner© is the tool that helps you organize what you need to know in four essential steps in order to systematically make that right assessment.