“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 Ph.D. 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:
- 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.
- For big, important decisions, they adopt decision criteria that minimize the maximum risk.
- 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
This post originally appeared at OCDQ Blog.
Some of us must be living in a less utopian universe where opinions are formed first and statistical evidence is compiled to support those opinions. There are few examples of companies or individuals admitting they were wrong and modifying their decisions. Most obfuscate and rationalize their decisions or add yet more biased equations to the already flawed results.
Unfortunately the universe described above is idealized. We have become a society of "opinion makers" based mostly on unsubstantiated data. Therein is the problem which Bayesian principles do not help. It's a behavioral problem not a statistical problem.