Bayesian Data-Driven Decision Making

In his book Data Driven: Profiting from Your Most Important Business Asset, Thomas Redman recounts the story of economist John Maynard Keynes, who, when asked what he does when new data is presented that does not support his earlier decision, responded: “I change my opinion.  What do you do?”

“This is the way good decision makers behave,” Redman explained.  “They know that a newly made decision is but the first step in its execution.  They regularly and systematically evaluate how well a decision is proving itself in practice by acquiring new data.  They are not afraid to modify their decisions, even admitting they are wrong and reversing course if the facts demand it.”

Since he has a PhD in statistics, it’s not surprising that Redman explained effective data-driven decision making using Bayesian statistics, which is “an important branch of statistics that differs from classic statistics in the way it makes inferences based on data.  One of its advantages is that it provides an explicit means to quantify uncertainty, both a priori, that is, in advance of the data, and a posteriori, in light of the data.”

Good decision makers, Redman explained, follow at least three Bayesian principles:

1. They bring as much of their prior experience as possible to bear in formulating their initial decision spaces and determining the sorts of data they will consider in making the decision.
2. For big, important decisions, they adopt decision criteria that minimize the maximum risk.
3. They constantly evaluate new data to determine how well a decision is working out, and they do not hesitate to modify the decision as needed.

A key concept of statistical process control and continuous improvement is the importance of closing the feedback loop that allows a process to monitor itself, learn from its mistakes, and adjust when necessary.

The importance of building feedback loops into data-driven decision making is too often ignored.

I discuss this, and other aspects of data-driven decision making, in my DataFlux white paper, which is available for download (registration required) using the following link: Decision-Driven Data Management

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Comments

[Jim Harris]

From the LinkedIn Group for the IAIDQ Open Community, David McFarland commented:

“I would agree, since most decision makers (read business leaders) do not understand concepts of risk, assuring that the probability of a good outcome in the decision is directly proportional to the quality of the information and data, understanding the value of a decision that is made and the need to revisit or retest that decision based on arrival on new information and data.”