Therefore, first of all, the garbage such as SWOT, PEST, 4P and the like must be swept out of the "model" team. Because these things have logic and goals, but it is difficult to argue with data. If you don't believe me, look at those SWOT and PEST reports. There are not many figures in the four boxes. Even if there are figures, it is difficult to prove: in the end, the reduction of 50 million by the post-90s compared with the post-80s will have millions of effects on our performance. Those that cannot be calculated quantitatively are not considered analytical models. They are just used to beautify the ppt.
The focus of the business analysis model is mobile number list the word "business". The business analysis model must be allowed to participate in, understand, and apply. Obviously, we can't expect product managers, sales, operations, after-sales, and logistics people to learn "Machine Learning", "Mathematical Modeling", "Statistics", and "Python Programming", so the algorithm models that data analysts often deal with are not here. Used - the business can't understand, can't participate, can't solve the problem, of course it's useless to spray.
Some students will be puzzled: but my leader will only mention "build a model", and can't tell whether it is a business model or an algorithm model. How can I tell the difference? One of the simplest principles is that more than 90% of non-technical leaders are talking about business models (the remaining 10% is that he saw names such as collaborative filtering and neural networks in the circle of friends, and then temporarily wanted to do it. a bit).