Intelligence is like trying to assemble a 1,000-piece puzzle when you only have 250 of the correct pieces. On top of that, you’re surrounded by 5,000 pieces from other puzzles that do nothing but create noise. And yet, based on this incomplete and messy picture, you’re expected to provide an educated opinion on the subject.
A few months ago, I watched a MasterClass on The Art of Intelligence that I found extremely insightful. I took away several valuable “knowledge nuggets” that helped me better structure and challenge my thinking on this topic—and that I’ve since been able to apply to my day-to-day work.
Below are my notes. Hopefully, they may also be of value to you.
1. What is analysis?
Analysis is not speculation or opinion. Analysis means systematically reviewing all available information, identifying what is relevant, and determining how it fits together to provide insight on a subject. In other words: which puzzle pieces matter, and how do they connect.
2. Sources of intelligence
There are two primary sources of intelligence:
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Human (people)
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Technical
3. Human vs. technical intelligence
Technical intelligence is important—mandatory, even. However, a good human source of intelligence will always outperform purely technical sources. The key is not access, but quality: the source must be reliable, informed, and credible.
4. Relationships as a core skill
A defining trait of strong intelligence craftsmanship is the ability to build relationships with key people who can provide relevant and valid insights. This requires trust, which is built by using information consciously, correctly, and responsibly.
5. The importance of listening
Listening is a critical skill. You must:
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Ask the right and most pertinent questions
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Actively listen to what people—or the data—are telling you
Avoid forcing feedback to fit a pre-existing narrative. As a rule of thumb, follow the WAIT principle:
Why Am I Still Talking?
6. Building meaningful relationships with sources
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Build trust through patience
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Listen with undivided attention
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Apply the WAIT principle
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Avoid purely transactional relationships
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Understand the other person’s challenges and motivations
7. Understanding deceit
Establish a baseline behavior for when human sources are telling the truth. As with lie detection techniques, truthful behavior tends to show low variance and consistent patterns. Deception, by contrast, often introduces higher variance, making patterns harder to maintain and identify.
8. Creating a safe analytical environment
Create space for honest opinions within your team by:
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Selecting the right people (avoid “yes-men”)
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Giving your opinion last
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Asking junior colleagues to speak first
9. Bias awareness
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Acknowledge that everyone has biases—and understand their impact
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Be especially alert to anchoring, confirmation, pendulum, groupthink, and mirroring biases
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Always express conclusions with an explicit confidence level
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Beware of compounding bias effects
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Always factor in the motivations of your sources
10. Managing risk and uncertainty
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Always keep the risk/uncertainty matrix in mind
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Identify as many risks and uncertainties as possible
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Develop mitigation plans for each
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For more refer to my note:
11. Presenting your conclusions
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Build personal, timely, and close resonance with the decision-maker
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Use accessible language—even for complex or technical topics
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Tailor your message to the decision-maker’s preferred decision-making process
Conclusion
Intelligence is not about having perfect information—it’s about making sense of imperfection. It requires discipline over instinct, listening over speaking, and humility over certainty. The real craft lies in knowing which puzzle pieces matter, which ones are noise, and how our own biases shape the picture we think we see.
Strong intelligence work is ultimately human work. Trust, relationships, and judgment matter as much as tools and technology. By creating safe spaces for dissent, questioning our assumptions, and clearly communicating uncertainty, we don’t just improve our analysis—we improve the quality of the decisions that follow.
In a world overloaded with data, the advantage belongs not to those who know the most, but to those who think the clearest.
(text revised by a LLM)
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