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⚖️ Strategy + Business

In a Data-Led World, Intuition Still Matters

Striking the critical balance between modern analytics and human judgment. Because the challenge today is not the lack of information, but the judgment to use it.

Based on Decisions Over Decimals by Christopher Frank, Paul Magnone, and Oded Netzer
Written by Daniel Akst • Read the original article
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The Age of Analytics vs. Human Choice

For as long as there have been decisions, people have used facts (data) alongside reason and intuition. Today, the balance has shifted drastically.

Data often seems to be making decisions for us. Just as analytics changed the face of sports, business data now prompts computers to auto-order products, cut prices, or execute actions that historically required human thought.

However, data needs people just as much as people need data. Algorithms cannot decide whether to launch a new product line, expand into a new continent, buy/sell a business, or rebrand a venerable logo.

⚠️ The Risk of Certainty

“Data and numbers tend to provide the comfortable feeling of accuracy and certainty, but they rarely tell us the full story. Numbers alone can never provide a perfect solution or answer, and they will never immunize decision-makers from faltering.”

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The Solution: Quantitative Intuition (QI)

To combat data-intoxication, a trio of business veterans from Columbia University (with ties to American Express, Google, and Amazon) developed a learnable approach.

At first glance, “Quantitative Intuition” sounds like an oxymoron. Yet, it forms the bedrock of modern executive leadership. It is the structured practice of combining hard quantitative information with instinct—human judgment that has been developed through years of experience and close observation.

While QI looks similar to what any sensible person would do, breaking down the process reveals powerful, granular insights for navigating a torrent of analytics.

“Combining quantitative information with intuition... is indispensable.”
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Framing: The Problem with Problems

“We fail more often because we solve the wrong problem than because we get the wrong solution to the right problem.”
— Russell Ackoff, Organizational Theorist

Define the Question First

You cannot know what data you truly need until you properly frame the decision. Data cannot provide both the questions and the answers. It is the leader's responsibility to home in on the essential question, leveraging their view of the broader business landscape and complexities.

The “Why?” Technique

Senior management must invest time upfront to clarify the problem, saving massive time and error down the line. Ask “Why?” of subordinates in a near childlike fashion.

Example:
- “Why conduct this study?” → “To know customers better.”
- “Why know them better just now?” → ...leading eventually to the real issue at stake.

The Amazon Approach

Defining the problem first and working backward puts you in good company. At Amazon, when a team has an idea for a new product or service, they cannot just look at data. They must first write up:

  • A Press Release: Explaining what it is and how it will work to the public.
  • Detailed FAQs: Anticipating how various contingencies will be handled.

This upfront process helps all parties gain insight into what they really need to know to determine if the scheme is actually a good idea, focusing them on the right data.

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Confronting & Interrogating the Data

Once the right problem is framed, you must ensure the data is right. This requires asking powerful, probing questions and refusing to take dashboards at face value.

The “IWIK” Framework

Develop “I Wish I Knew” (IWIK) questions. These are specifically designed to elicit data that is actually relevant to making a decision, cutting through vanity metrics.


Cultivating Intuition

Managers are exhorted to cultivate intuition by using back-of-the-envelope calculations. This provides a ready, instinctual feel for magnitude and plausibility. If a metric doesn't pass the “envelope” test, it requires deeper scrutiny.

📋 The Interrogation Checklist

All data, however obtained, must be rigorously interrogated:

  • Is the data accurate?
  • Do means and medians mask explosive outliers?
  • Is the period covered by the data the relevant one?
  • Were the numbers you saw the result of posing the right questions?
  • What biases might have influenced how data was harvested and presented?
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Synthesis, Meaning, & The Final Call

Meaning Over Parroting

Data must be accurate, but it needn't be too precise. All parties must learn to synthesize rather than merely parroting or summarizing. What matters is meaning.

In presentations, don't bury the lede: make the bottom line the top line. Encourage teams not to stop at what the analyses say, but to define what it means for the organization.

1. What does it mean?
2. So what? (Implications)
3. Now what? (Action Plan)

The Structured Decision Process

Sooner or later—and better sooner—a decision has to be made. The corporate executive requires a structured process:

  • Begin with agreement from stakeholders on the objective, timeline, and who must participate.
  • Consider whether the decision is reversible. If it is not reversible, can it become reversible?
  • Focus on being vaguely right rather than precisely wrong.
  • Get the best data you can, even if imperfect. With or without perfect data, a decision must be made.
“Successful decision-makers balance data, experience, and intuition. They know there is more to decision-making than just the data. They resist being intoxicated by information.”