Make Better Decisions by Making Friends with Machine Learning

Article Type: AI/ML, Decision Making

Published: April 30, 2024

Reading Time: 2 minutes

Author: Lin Classon, SVP, Global Product Management

Overview

Cassie Kozyrkov, Google's Chief Decision Scientist, has devoted her career to demystifying terms such as AI and Machine Learning. She actively advocates for a widespread, shared understanding of data science, decision intelligence, and statistics, particularly for those outside these specific fields.

This article explores key insights from Cassie's "Making Friends with Machine Learning" course developed at Google in 2017, providing a straightforward explanation of everything Machine Learning and its application to intelligent decision-making.

What is Machine Learning?

Machine learning is an approach to making many decisions that involves algorithmically finding patterns in old data and using these patterns to create models (recipes) for dealing correctly with brand new data.

In a nutshell: ML is all about finding and using patterns in data.

Data as the New Oil: Understanding the Analogy

The phrase "Data is the new oil" was first coined in 2006, drawing a parallel between the latent value of raw data and crude oil, as well as the substantial effort needed to refine them into a useful form.

Today, we have access to the tools to transform information into actionable decisions on any scale. Therefore, it is crucial for decision-makers, including marketers, to recognize that:

Data analysis provides control rather than absolute certainty
Simply having more data does not equate to making 'right-er' decisions

The Core Question: Intelligent Decision-Making vs. Right Decisions

In her article "How Should Data Analysis Impact Your Decision-Making Process?", Cassie dives into what statistics and data can and can't tell you.

The Most Important Insight

Do not ask: "Am I making the right decision?"

Instead ask: "Am I making the decision intelligently?"

Key Takeaways on Data-Informed Decision Making

1. Statisticians and Data Scientists Are Not Miracle-Workers

Your statistician and data scientist are not miracle-workers. The elimination of uncertainty is impossible without possessing all possible data, which is often unattainable. They aim to guide the decision-making process informed by data, allowing for better risk management.

2. Understanding Hypothesis Testing

Hypothesis testing involves:

Default action: The course you would take if forced to decide
Alternative action: The path you would take if data analysis disproved your initial choice

This methodology is critical when deciding amidst uncertainty.

3. The True Strength of Statistical Analysis

The true strength of statistical analysis lies not in its ability to guarantee certainty but in its capacity to minimize the probability of wrong decisions. It enables informed decision-making based on the available data, rather than guaranteeing an inherently correct decision.

4. Managing Risks Through Statistical Methods

In the face of uncertainty, the question "Am I making the decision intelligently?" is a lot more useful than "Am I making the right decision?". Statistical methods and data analysis offer control when certainty is unattainable, and they should be used as a control panel for managing risks.

Conclusion

While certainty is not always achievable, intelligent, data-informed decision-making at scale is. The focus should be on making decisions intelligently using available data and statistical methods to manage risks, rather than seeking absolute certainty in decision outcomes.

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