Lessons I’ve Learned in Data Science: Learn to Play the Role of “Translator” for Yourself


As somebody who enjoys using data in my daily work, I’ve come across a bunch of lessons in my journey while working which I didn’t have an opportunity to learn in school. These lessons are vital to grown from to be a good employee.

Lesson 2.Learn to play the role of “translator” for yourself

If you take the time to read the suggested blueprint for companies’ data organizations from McKinsey, it puts a spotlight on the significance of a role called “translator” that must function as a communication bridge between the data science team and the business team by translating analytical insights into actionable ones.

As a data science professional, you probably have been asked by someone who isn’t technically savvy in data science to “provide an explanation of a concept like you are giving that explanation to a kindergartner” or to “put it in simple Eglish”.

Data science professionals who can effectively communicate by translating themselves to non-technically savvy people are appreciated for the these reasons:

  1. It’s difficult to find “translators” from people who are non-analytical. McKinsey attempted to train non-analytical people to be “translators,” but they never succeeded in translating technical info from analytics to non-analytical people. The reason for that is simple: to adequately summarize the main points of complex analyses and properly reflect the caveats, you must have an analytical mind and understand the analytics, which doesn’t happen in a couple of weeks of analytics training. As a data scientist, the time you use trying unsuccessfully to teach an analytics crash course to non-analytical people is probably best used creating your own style of communication and learning to translate yourself to non-analytical people.
  2. A lack of precise detail can be avoided if data scientists learn how to effectively explain the results of their work to non-analytical people: Most people have probably played something referred to as the “telephone” game or some form of it. The longer the info is passed from person to person, the more difficult it will be to maintain precision in the message. Now imagine something like this happening to the results of your analytical work; if you depend on other people to translate your results for you, the message might lose details by the time it gets to the end-user.

The way to put it in practice:

Practice translating yourself to a friend who is not analytically-minded. With this friend, try explaining the result of your model/analysis (avoid disclosing any confidential info). This method is also helpful for discovering gaps in your explanation. If you can’t find a way to explain something in simple terms, it’s typically because you don’t understand the concept/results well yourself.

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