How to Make Better Decisions with Less Data
In companies, employees often struggle to find a solution to the problem suggested by other employees and their higher-ups. To find a solution to the given task in question, employees look through piles of surveys, reports, and statistics which ultimately leads them to a state called “analysis paralysis”. In this state, people start offering the same solutions as everyone else. Furthermore, these solutions are just solutions recycled from prior problems and are therefore not so useful. However, the issue, in most cases, isn’t a lack of data, but the vast amount of them because people fail to prioritize this large amount of data. This is very similar to the process of information cascade we learned in class. Information cascade describes how one’s decision making is influenced by others, and examples given in class like buying a new phone or choosing a restaurant are similar to the situation in this company where employees need to choose the best solution to report.
The author claims that there is a need to think more strategically about how people apply information to people’s decision making and recommended a procedure called data diet. The first step of data diet is to define a problem. People tend to fixate on familiar approaches rather than steeping back to understand the contours of the problem. So before the data hunt begins, one should allow people to see it from different angels like that of a consumer and a supplier to better their understanding and widening the narrow scopes. The second step is to integrate which requires putting the small pieces together. The third step is to explore. Here, individuals are assigned a distinct set of ideas. The purpose is to make people work on each idea independently in silence and collaborate later on. If people work together from the very beginning, then early decisions by first few participants heavily influence the later participants who ignore their own information as in the process of information cascade. And, we also learned in class that there is also a tendency for large crowds to jump to conclusions which leads to a failure of wisdom of crowds. So, it’s important that group collaboration comes after individuals are given sufficient time to think by themselves. The final step of data diet is to test. In order to test, people must first design critical tests and determine if their plans work under the designated tests while disconfirming data that don’t work at the same time.
At the end of this procedure of data diet, people are more likely to develop a better appetite for data. And, this is important because there is an overwhelming amount of information in today’s society and efficiently analyzing those data is going to become challenging as time goes on.
https://hbr.org/2016/11/how-to-make-better-decisions-with-less-data