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Systems & Complexity

Garbage In Garbage Out

Model #1015Category: Systems & ComplexityDepth to apply:

By Updated

4 min read

Contents

  1. 1. Core Idea
  2. 2. How to See It
  3. 3. How to Use It
  4. 4. Founders & Leaders
  5. 5. Connected Models
  6. 6. One Key Quote
  7. 7. Summary & Further Reading
Systems & Complexity
Section 1

Core Idea

Garbage in, garbage out (GIGO) states that the quality of a system's output is fundamentally limited by the quality of its inputs. No amount of processing, analysis, or sophistication can produce good results from bad data. A flawed customer survey produces flawed product decisions. Biased hiring criteria produce biased teams. Inaccurate financial assumptions produce worthless forecasts. The model is deceptively simple but widely violated — organisations invest heavily in analysis frameworks, dashboards, and decision processes while neglecting the quality of the data feeding them. GIGO teaches that input quality is the first and most important optimisation in any system.

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Section 2

How to See It

Analytics
You're seeing it when a company builds an elaborate forecasting model but feeds it self-reported sales pipeline data that reps inflate to look busy. The model is sophisticated; the inputs are garbage; the forecasts are worthless.
Strategy
You're seeing it when a strategic plan is built on market research that sampled the wrong customer segment. Every conclusion downstream inherits the input error, and no amount of rigorous analysis fixes the flawed foundation.
Section 3

How to Use It

Before trusting any output — report, model, recommendation, dashboard — trace back to the inputs. How was the data collected? What are the known biases? What's missing? Invest at least as much effort in validating inputs as in building the processing layer. The highest-leverage improvement in most systems is cleaning up the data that feeds them.
Decision filter
"How confident am I in the quality of the inputs driving this analysis? If the inputs are flawed, does the sophistication of my process even matter?"
As a founder
Audit the data sources that inform your biggest decisions. Customer feedback channels, financial reporting, product analytics — trace each back to collection methods. Most decision-making errors don't come from bad reasoning; they come from reasoning well on bad data.
Section 5

Founders & Leaders

Warren BuffettChairman and CEO, Berkshire Hathaway
Buffett's investment discipline is built on input quality. He famously ignores Wall Street analyst estimates, macroeconomic forecasts, and market sentiment — all inputs he considers garbage for long-term investing decisions. Instead, he insists on reading primary sources: annual reports, 10-Ks, direct conversations with management. His circle of competence framework is essentially a GIGO filter — stay where your inputs are reliable. When Buffett can't get clean inputs on a business, he doesn't invest, no matter how attractive the surface numbers look. Founders should apply the same rigour: the quality of your decisions is capped by the quality of your information. Invest in getting the inputs right before building elaborate analysis on top of them.
Section 7

Connected Models

Reinforces
Signal vs Noise
Signal vs noise separates meaningful data from irrelevant data. GIGO addresses a prior step: even the "signal" is worthless if the underlying data collection is flawed. Clean signal requires clean inputs.
Pairs-with
Analytical Honesty
Analytical honesty ensures you don't distort data during processing. GIGO ensures the data wasn't distorted before processing began. Together they protect the full chain — from collection through analysis to conclusion.
Tension
Quality Control
Quality control typically focuses on outputs — checking finished products against standards. GIGO argues that the highest-leverage quality control happens at the input stage. The tension: most organisations over-invest in output inspection and under-invest in input validation.
Section 8

One Key Quote

"It is better to be approximately right than precisely wrong."
— Warren Buffett
Section 11

Summary & Further Reading

Garbage in, garbage out: no system can produce quality outputs from flawed inputs. The highest-leverage improvement in most organisations is not better analysis but better data. Validate inputs before investing in processing, and trace every conclusion back to the quality of the information that produced it.

Why this matters next

mental modelsLeverage

Garbage In Garbage Out applied the Leverage mental model

mental modelsQuality

Garbage In Garbage Out applied the Quality mental model

mental modelsMeasurement

Garbage In Garbage Out applied the Measurement mental model

mental modelsFeedback

Garbage In Garbage Out applied the Feedback mental model

mental modelsSignal vs Noise

Garbage In Garbage Out applied the Signal vs Noise mental model

mental modelsCircle of Competence

Garbage In Garbage Out applied the Circle of Competence mental model

Frequently asked questions

What is Garbage In Garbage Out?

Garbage In Garbage Out is a mental model used for better thinking and decision-making.

How do you apply Garbage In Garbage Out?

To apply Garbage In Garbage Out, identify situations where this framework is relevant, then use it as a lens to evaluate your options and decisions. The model is most useful when combined with other complementary mental models.

What category does Garbage In Garbage Out fall under?

Garbage In Garbage Out falls under the Systems & Complexity category of mental models. Other models in this category can be found on the Systems & Complexity hub page.

Why is Garbage In Garbage Out important?

Garbage In Garbage Out is important because it provides a structured way to think about problems that would otherwise be approached with intuition alone. Understanding this model helps you avoid common reasoning errors and make better decisions.

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