How do you eat an elephant?
This age-old adage resonates deeply with the process of turning a company into a data-driven entity. Just as consuming an elephant is a monumental task… Read More »How do you eat an elephant?
3 Books That Expose The Flaws Of The Human Mind
The work of Daniel Kahneman, Richard Thaler and Dan Ariely have inspired many of the the frameworks we at Xabit. In this post, we share 3 books that we think are essential reads for anyone trying to do the same.
The Analyst’s Dilemma: To Analyze or Not To Analyze, That is The Question
The Analyst’s dilemma is this: On the one hand, our brains are wired to rationalise the reasons for the current state of affairs all the time. We think “You can’t manage what you can’t measure” is a not a quote by a fellow analyst but rather gospel. On the other, 43% of Analysts [in the workforce] say they would be better than their boss at their boss’s job, which is classic Dunning–Kruger Effect at play. The boss is the boss, and we’re not them, for a reason, right?
How Our Minds Mislead Us in the Face of Probability
The world around us is too vast and unpredictable for us to fully comprehend its complexity. To try and make sense of the complexity, our minds often rely on shortcuts and heuristics to make sense of complex information. Understanding the role of probability, and how our minds can be mislead in the face of it, is crucial in making better decisions.
The ONE Variable That Can Completely Derail Your Data-Decision Efforts
Human behavior can completely derail your data modeling efforts, no matter how advanced your model or skilled your team. Understanding and accepting this unpredictability is the key to making better decisions. Learn why human behavior is the X-factor in data modeling, and how it can impact even the most sophisticated models.
3 Cognitive Barriers To Better Decision-Making
Over the past two years, our team has been working with companies in Nepal to connect the dots between their data and decisions. In our efforts, we’ve seen three cognitive biases that appear consistently across enterprises and hinder their ability to make better decisions.
I’m Bayesed and I know it
If you’re too young to realize where the title reference comes from, I’m gonna make you lose your mind. It has something to do with parties and rocks and anthems. Actually, no, I just want you to have a good time so I’ll instead ask you to take a look at the title picture. What did you notice?
Why you need Mental Models in Data Science
I am back to the usual routine: morning coffee with Python, lunch with Machine Learning and dinner with Spark. If you are expecting me to talk about either one of these topics, you should stop reading because this week I am talking about Charlie Munger.