Choosing the right path among different options is a challenge we all face every day. Most of the time, we do it quite well. We’re often familiar with the variables and can reliably predict the outcomes of Choice A vs. Choice B or C.
But what happens when we’re faced with an overwhelming number of choices and unfamiliar decision criteria? What if multiple people, each with their own unique perspectives, have to agree on the decision? And what if the stakes are really high?
These are some of the questions tackled by Christine Anderson-Cook, Research Scientist at the Los Alamos National Laboratory, in her plenary presentation at Discovery Summit 2017 in St. Louis.
In her talk titled "Leveraging Data: Your Ally in Decision Making," Anderson-Cook highlighted some of the many reasons why complex decision making is difficult, particularly when groups of people are involved.
For one thing, agreeing on the ideal outcome of the decision in not a slam dunk. Group members may not share the same priorities and will likely bring widely different experiences and personalities to the table.
While this “cognitive diversity” actually has the potential to result in better decisions, without the right decision-making framework, Anderson-Cook argued, it can make reaching consensus and achieving optimal outcomes a struggle. Other pitfalls include hidden biases, unhealthy power dynamics and group think.
So what are some strategies to improve the ability of your team to consistently make good decisions? The key, according to Anderson-Cook, is a structured process – with data at its foundation – designed to facilitate defensible, reproducible decisions.
Anderson-Cook advocates a five-step process – Define-Measure-Reduce-Combine-Select (DMRCS):
What attributes of our options does each member of the team value most and why? (e.g., cost? quality? speed? ease of implementation? etc.)
What criteria – i.e., data -- will be used to measure the important attributes of each option?
How can we apply our data-driven criteria to eliminate some of the contenders and arrive at a more manageable list of options?
When we consider different weightings of our criteria, can we identify the handful of choices that seem to best balance the trade-offs?
Which solution is best given the priorities we identified in the Define stage?
The value of this process is that not only does it bring transparency to the team’s collective priorities, but it also offers mutually agreed upon metrics with which to assess the performance of each option. In the Combine step, it also provides a way to evaluate each option using different weightings for each criterion, thereby acknowledging the fact that different players will likely value things differently.
The Measure, Reduce and Combine phases are the most data dependent. This is where visual data analysis techniques, like those supported by JMP, have the greatest potential to unearth hidden insights. Anderson-Cook showed how a JMP add-in could be used to visualize trade-offs using Pareto fronts as well as compare the effects of different criteria weightings.
Among the key benefits of a structured process like DMRCS is that is allows the team to defuse some of the emotion that can creep into high-stakes decisions. The Define and Measure steps establish a focus and allow data to drive the conversation from the outset.
Another advantage of the DMRCS approach is that it gives each team member the chance to articulate what matters most to them and why. It allows the team to isolate the subjective elements in their assessments and helps everyone clearly see how those elements affect the decision.
When executed correctly, a structured, data-driven approach to decision making can help teams not just survive cognitive diversity, but to unleash the wisdom and creativity inherent in multiple perspectives.
Anderson-Cook summed it up in a quote from a team member who experienced the DMRCS process: “For the first time, it didn’t feel like it was about winning and losing. It was about making the right choice.”
You can watch the recording of Christine Anderson-Cook's talk here in the JMP User Community.
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