From the course: Lessons from Data Scientists
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When data may not be helpful
From the course: Lessons from Data Scientists
When data may not be helpful
- Kind of two main times where the data is kind of, maybe misleading you or can really kind of obscure the decision making process, and that's when one, you don't have enough data. That's kind of a critical failure of a lot of projects, where simply you're messing around with the data set that's just way too small. So actually, as a data scientist, you should be really critical about the size of your data and the amount of data that you're working with. If it's too small, you need to drive your team as well as your product managers and so on to really enable you to get a bigger dataset. And then another way that data is kind of unhelpful is when it's burden of maintenance gets so high and its ability to acquire, it is so expensive that proposing a project that like, oh, I'm going to be data-driven about my sales performance numbers, but it takes half a million dollars to implement that, that's not really going to help you…
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