McKinsey & Co. has recently written an article about decision models named “The benefits and limits of decision models”.
The article touches upon how big data and models help overcome biases that typically cloud common judgment. In this post, I will touch upon how we advocate for decision models and why they can be a powerful aid in supporting important and critical decision making for selecting and executing the right projects.
The main advantage of combining vast amounts of data and increasingly sophisticated algorithms is that we can create very accurate predictions or help guide knotty optimization choices which in turn help companies to avoid some of the common biases that at times undermine leaders' judgments.
Examples of successful decision models are numerous and growing. Retailers gather real-time information about customer behavior by monitoring preferences and spending patterns. They can also run experiments to test the impact of changes in pricing or packaging and then rapidly observe the quantities sold. Banks approve loans and insurance companies extend coverage, basing their decisions on models that are continually updated, factoring in the most information to make the best decisions.
Models can show remarkable power in fields that are usually considered the domain of experts such as our narrow field of business intelligence (BI), which is project cost and effort estimation and execution.
Our decision model, which we call the Automated Learning Algorithm (ALA), works mainly because it automatically gathers vast quantities of data that can help avoid common biases. Within our field people tend to be overly precise, believing that their estimates will be more accurate than they really are. And if in doubt, just multiply the estimate with pi to get to a reasonable number. Using a decision model such as ours aids in weighing all data objectively and evenly instead. This is why it over time will perform better than humans. Not to say that the human factor can be eliminated completely as the model, to an extent of course, only performs well based on the input that is provided and as such only will aid and guide decision making.
The decisions themselves must obviously be taken by the executives responsible. And the forecasts and predicted outcomes must go hand in hand with proper project execution and control. This means that getting things done and having the right leadership to mobilize people to achieve the desired outcome is essential. A combination of both data and people is the winning answer.
As the author nicely mentions, the use of data analysis was the key insight of Michael Lewis’s 2003 bestseller, Moneyball: The Art of Winning an Unfair Game.
Analyzing an entire season of major-league games revealed that, on average, making an out to advance the runner leads to fewer runs. The sacrifice bunt is just one example of how conventional wisdom in baseball can be wrong. James concluded, “A very, very large percentage of the things that the experts all knew to be true turned out, on examination, not to be true at all.”
The use of decision models can help discover what truly leads to a winning performance in baseball. In the same manner, applying decision model insight to project estimation and execution can lead to large positive gains. In “decision speak”, we are trying to optimize successfully completed projects and value earned per dollar spent.
Why our technology (algorithm) works so well, is that it takes advantage of all aspects of automated learning, meaning the ability to make predictions, to compare those predictions with what actually happens, and then to evolve so as to improve and make more accurate predictions in subsequent projects. Thus over time the forecasts become more and more accurate and less and less biased.
Imagine how comforting it would be if you always had proven statistical evidence to inject more trust and confidence into the successful execution of your company's projects.
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