Clarify key terms

- [Instructor] George Carlin once joked that he put a dollar in a change machine and nothing changed. It makes you wonder what kind of change he was hoping for. You'll never know because these words he used could have meant several different things. That's true with a lot of our language. The context in which we use words impacts their meaning. That's why looking at key words and phrases is one of the best ways to gather interesting questions. You've already seen that your data science discussions include critical thinking. You need to carefully look at the reasoning behind your ideas and then question it. That way you'll have a better understanding of everyone's ideas. One of the best ways to do this is by questioning key terms and phrases. Let's go back to our running shoe website. Let's say that someone on the data science team came up with an interesting question. The question was, what makes someone who runs often happy? This is an open-ended essential question. That means that there probably won't be a concrete answer. Instead, you'll have to make strong arguments that are backed up by the data. If you want a data science team, then what would be some of the key terms and phrases that you'd like to question? Think about words that might have ambiguity, or have multiple meanings. These are often words that are abstract and need to be interpreted. In this case, there are a few words you might want to explore. Think about the word often. What does the word often mean to you? It usually depends on the person. My wife likes going to restaurants. We try to go at least once a week. If you ask me, I'd say that we go to restaurants very often. If you ask her, she would say we never go to restaurants, only once a week. You need more clarity for the term often. You might want to ask a closed question like, how many times does our average customer run each week? Then you could put this question underneath the earlier question on your question tree. There's also another key word that you might want to explore. What does the word happiness mean to you? Are your customers running because they like to run? Maybe they actually like being finished. They're happiest when they come home from the run. Maybe they don't like running, but it's the only way they know how to relieve stress. In a sense, they are happier being a runner. This is another area where you could ask further questions. You could go for broad essential questions like, what makes all of our customers happy? You could also try to slice happiness into segments. Maybe ask a question like, do our customers run because they feel they have to? You could even get more specific like, are our customers happiest when they're done running? You can see how asking questions about key words and phrases can quickly produce more questions. Remember, it's the research leads job to pan for gold as your team goes through questions. Just because your data science team asks these questions, it doesn't mean they're obligated to follow through with results. The research lead is the one listens to these questions, and picks out the nuggets that sound the most interesting. With this one question, you now have five or six other questions that might be more interesting. You started by asking, do people run often because it makes them happier? Now your team is asking essential open questions, which might tie to business value. Let's think about the essential open question, what makes our customers happy? The question might sound simple, almost trivial. Still, if your data science team is even able to gain some insight, then it's certain that these results will tie into real business value. If you think about it, can you answer this question about your customer? If you're working for an organization, what would you say makes your customers happy? Most organizations don't approach this topic at the team level. It's the answers to these questions that will help drive business value. These insights will be the gold nuggets that your data science team will deliver. Yet many data science teams don't go after these questions because they feel that the terms are too obvious. Remember, what's obvious to you is usually not always obvious to everyone else. That's why taking the time to ask these questions about key words and phrases will make your data science team more insightful.