Analytics and Decision Making

By Andrea Espíndola Pedro Olaya


There has been a outburst of attempts to frame decision making with action in consulting environments. We present in this post some of these ideas. Our main references are The Mckinsey Engagement by Friga and personal communications with professor P. Noonan of Goizueta Business School.

In these methodologies typically you start with a main problem or key question on which you eighter want to make a decision or a recommendation to a decision maker. You start using  issue tree analysis (here represented and refered to as pyramid) and the MECE criteria, as explained in  The Minto Pyramid Principle or else, you decompose your problem/question in a pyramidal structure (see figures below) whose last level of  analysis consist of leaves that are “atomic” or minimal propositions. Ideas at any level of the pyramid must be neccesary and sufficient to logically imply the idea in the node at the level above.

Sin título

Most of the conclusions we derive from analytical models belong to the lowest (atomic) level, from the point of view of the issue pyramid.  This view explains the logical connection between analytics and optimal decision making.

Through analytics, it is possible to make relevant decisions from empirical data in order to give solution to a specific problem or to improve the conditions of a certain situation.

Analytics typically lies at the intersection of Minto Pyramid Principle and an invert pyramid that encodes the solution/recommendation to the question we started with as shown in the figure below. First the MPP identifies the main problem, its implications and sub problems. Then, using the information available and through a far and wide analysis it might generate solutions to the different questions, which in turn might give rise to different alternatives, allowing the decision maker to reach a tangible solution/decision.

Sin título

In real life, the situation is way more complex and part of this complexity is captured by the DELTA model introduced in  Analytics at Work. But this will be the topic of a later post.

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