Gather Data and Insights
Sift through data to distinguish useful from useless, avoiding confirmation bias by examining data purely to learn, not to confirm preconceptions.
THE IDEA
Step 3. Gather Data and Insights
It’s easy to be distracted by useless data, so sift through it and understand it. And then determine if the information is helpful or immaterial. If it’s just anecdotal, for example, it could even be misleading. If you have access to technical resources, have them analyze the data with you.
A litmus test for evaluating data: “Does this data help us think differently? Will it take us in a different direction or lead to questions we haven’t thought of asking?” That’s the kind of information to look for. Fight the urge to look for data that support your preconceptions; they will just reinforce how you already think and won’t help you think of other, perhaps better solutions. If instead, you examine data purely to learn, you’ll move closer to a good solution.
When to use it
Evaluating a new business strategy: gather data from competitors, market trends, and customer feedback rather than only internal projections.
Analyzing a marketing campaign: compare performance metrics against industry benchmarks instead of just internal targets.
Making a hiring decision: review all application materials uniformly without favoring candidates based on first impressions.
Conducting a scientific study: define data-quality and exclusion criteria, investigate anomalies, and report how exclusions affect the conclusions.
FROM THEORY TO EVERYDAY LIFE
In practice
Evaluating Employee Performance
A manager reviewing performance data might unconsciously favor metrics that support a preconceived opinion about an employee. Instead, they should objectively examine all relevant data—including peer reviews and quantitative outputs—to avoid confirmation bias and make a fair assessment.
Scientific Experiment Analysis
A scientist notices outliers that contradict a hypothesis. They investigate whether the points reflect valid observations, measurement errors, or another cause, and document any exclusion. Disagreement with the hypothesis alone is not a reason to discard them.
A MOMENT TO REFLECT
Put it to work.
In your last major decision, did you actively seek out data that challenged your initial assumptions?
How can you structure your data-gathering process to minimize the influence of preconceptions?
What data might you be ignoring that could fundamentally change your understanding of a situation?
Often confused with
CONTINUE THE CONVERSATION
Source texts
- Thinking in Systems and Mental Models Think Like a Super Thinker (Dawson, Marcus P)