Key Takeaways

• Business decisions should rely on data analysis, not just intuition or experience.
• We all have unconscious biases that cloud judgment. Recognising them is crucial.
• Correlation doesn’t equal causation. Data can be misleading, but it’s still better than pure opinion.
• Subjective judgment can’t be eliminated entirely, but we can make it more objective.
• Develop rating scales with weighted attributes to guide decisions when data is limited.
• Our brains use mental models to make sense of the world. Identify and refine them to avoid bias.

Introduction

In recent years, we have been bombarded with an explosion of data. Now everyone is expected to make decisions based on data rather than relying solely on intuition or insights shared by experienced individuals. The transition towards data-driven decision-making is indeed more objective than past approaches, which relied on individual subjective assessments. In this blog post, I am sharing my journey on how I integrate these two approaches into my current decision-making process.

Awakening to Bias: A Personal Journey

During my MBA studies at ISB, I came across the book “Predictably Irrational” by Dan Ariely during my course on People Management. The book introduced to me the world of biases that every one of us has within us. This subject captured my imagination, leading me to dig deeper into the realm of behavioural economics. As I came across more research into this domain, I was amazed at the subtle biases we unknowingly hold onto daily. It soon struck me the importance of recognising and navigating these biases in our daily lives

Why our Intuition fails us

In our current business landscape, data serves as the foundation for every decision. During my Statistics course at ISB, the professor always emphasised that “Correlation does not mean causation”. Reflecting on my professional journey, I can recall numerous occasions where my team’s data-driven insights proved me wrong. Over time, I accepted the notion that opinions arrived without any data backing are just opinions and not insights.

We need to acknowledge that everything around us is changing much faster than we can comprehend, a pace that continues to accelerate. Many commonly held beliefs are based on limited observations rather than absolute truths, as evident in the replacement of Newton’s Laws of Motion with Einstein’s theory of relativity in our understanding of motion

Do Subjective Opinions even Matter

If you’ve ever conducted interviews for a position, interviewing multiple candidates, the final decision arrives after an extensive discussion. Despite our best efforts, the final hiring decision rests is a subjective judgment. To overcome any e biases, many organisations undertake multiple interview rounds. However, not everyone has the resources at their disposal for such a process. Additionally, each interviewer will have different rating criteria, leading to variability in hiring standard

A simpler approach would be to list the attributes you want to assess, assign weights to them, and then score each candidate on a scale of 10. Finally, the candidate with the highest score is hired (assuming they are within budget). This approach allows for a more analytical rigour to your intuitions

Let’s consider an example: You’re hiring a sales manager, evaluating candidates based on the following:

• Prior Sales Experience: 30%
• Industry Knowledge: 20%
• Communication Skills: 20%
• Experience with Digital Tools: 15%
• Team Management Experience: 15%

You score each candidate against each attribute, compute the final score and make an offer to the candidate with the highest score

The beauty of this easy-to-implement model is its adaptability. You can develop simple models for key decisions, allowing for a more analytical rigour to your decisions when lacking data.

This approach is inspired by the article Models and Managers: The Concept of a Decision Calculus by John D. C. Little. Explicitly stating our assumptions upfront and building simple models lets us build rigour in our decisions. Just building simple mental models helps us avoid biases we are surrounded with.

Further Reading

This model-driven decision process has been discussed in multiple books in various forms. For those keen to explore more on this, you can explore the below books that I enjoyed reading

1. Thinking, Fast and Slow” by Daniel Kahneman
2. Predictably Irrational” by Dan Ariely
3. Superforecasting: The Art and Science of Prediction” by Philip E. Tetlock and Dan Gardner
4. Nudge by Richard H. Thaler and Cass R. Sunstein
5. The Signal and the Noise: Why So Many Predictions Fail – But Some Don’t” by Nate Silver

Conclusion

Unconsciously we use mental models daily to simplify our complex world. Our brains based on our past experiences develop such models, filtering out information not needed to navigate our daily lives. Most biases arise from faulty mental models, so rather than falling prey to such faulty models, we can avoid them by putting a conscious effort into building our model. Constant refinement of such models allows us to improve the quality of the decisions we take