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What are the characteristics of good data analysis thinking?

1, strong landing

To give an analogy, an e-commerce company to progress GMV?

A analyst: GMV = the integrated amount of consumption per person, only need to progress in the amount of consumption of each user, you can progress GMV, so exactly how to do it?

B analyst: GMV = customer unit price * number of consumer users, that only need to do 2 assumptions can be made, assuming that the customer unit price is unchanged, we only need to add the number of consumer users will be able to increase the number of consumer users, assuming that the number of consumer users is unchanged, only need to improve the price of the customer unit will be able to improve the GMV income, and then ambitious state is to make progress at the same time, but it is still not exhaustive, just find the direction of the problem

Analyst: GMV = the combination of the amount of money spent by each person, then how to do it? p>

C analyst: GMV = customer unit price * number of consumer users, first through the formula method to find a breakthrough and analyze the direction of the problem, and then with the data to deepen the analysis, such as customer unit price of the distribution is what posture? How much room for improvement? Which people can improve? These people have what characteristics? Next you can

Can tell the operation: through the full reduction to stimulate or through the buy free to stimulate it?

Can tell the product: to which people to buy guidance (recommended) effect will be good?

And so on, all the analysis of ideas are to allow you to find the right direction, to ask what is a good idea, good data analysis, that landing must be the first, the above three analysts have used the formula method, the first one ran off, the second just to find the direction of the third is in fact the other people like the data analysts, and here is just a simple analogy, so that We understand the reasoning behind it.

2, organized

To make the analysis of ideas become organized, we have to introduce a commonly used ideas, called the principle of the pyramid, in fact, we don't have to be scared by this term.

With a scene to analyze the following, let's say we go to the supermarket, you will find similar products will be set together, fruit area, meat area, seafood area, snacks area, etc., that in fact, the analysis is the same, as long as the indicators + dimensions of the good classification, will be able to ensure that the fundamental rationality, but try not to repeat and cross.

3, strong intent

Doing analysis of the most important analysis of the intention to do analysis, do analysis is not to solve the problem of the 2 categories: 1, to find the cause of the disease, the right remedy; 2, to validate the direction of the resolution plan, to provide data to support; that corresponds to the same point of view, the first type of analysis called a posteriori, that is, to make clear the problem, to find the cause. Clearly out of the problem, find the cause, the second category is called a priori analysis, to first assume and then verify that has not yet occurred.

4, measurable

Good analysis of ideas, not only can think clearly, but also to give the grounding of the way to move, and how to measure the role of the move? This is also to consider the work of the world, no wonder, the gap between the plan and the actual is where we grow, and this gap is often ignored by many people.

On the excellent data analytics thinking has what characteristics, Aoto editor will share with you here. If you have a strong interest in big data engineering, I hope this article can help you. If you still want to learn more about data analysts, big data engineers tips and materials, you can click on other articles on this site to learn.