Career planning of data analysts -- career anxiety and future development of data analysts

Data analysts around them often have a sense of professional anxiety and fatigue , Especially data analysts in their 30s . Why does it feel like this ？ How can we avoid this kind of professional anxiety ？

One 、 The confusion of data analysts

The professional anxiety and fatigue of data analysts come from chore confusion ： Everything you do is chores , Not data retrieval , Is to make reports and charts , I feel that what I do has no technical content . It's normal for data analysts to have this confusion , Because now many data analysts do simple analysis , Count , Calculate the click through rate 、 permeability 、 Conversion rate 、 growth rate 、 Horizontal proportion , wait .

Such jobs will be replaced by fresh graduates every minute , This feeling will be stronger for 30-year-old data analysts . The more confused the analyst is , Most of the companies have no data products . Data analysts have done part of the work of data warehouse , I also did some work on data products , Also did some data analysis work , But after so much work, there is no achievement . If you're still at this stage , Please think carefully about the future development path as a data analyst .

Two 、 Future development of data analysts

In terms of the ease of data analysis , It can be divided into simple analysis 、 Complex analysis . The purpose of simple analysis is to help everyone in the company see the current situation of the company's data , Need to tell the management separately 、 What is the current situation of the executive level . Complex analysis is to help enterprises find the causes of the current situation and the future direction , Answer the question of why and what the future is .

1. Simple analysis , Please transfer to simple analysis of data products

What needs to be done is the data analysis with serious clutter and confusion mentioned in the previous single article , There is no technical content , Data products can basically replace these jobs . Simple analysis of the corresponding decision is also relatively simple , It's basically a decision at the tactical execution level , It's not about complex decisions and Strategies .

However, the existence of data products must not be ignored , No data products , Enterprises can't see their current situation . A person who doesn't know where he is now , It is impossible to know where the future should go . Simple data analysis will not disappear in a short time , Because data products have a process of summarizing and refining , Before forming data products , Some simple data analysis is needed .

But not necessarily in the future , Because with the operation 、 More and more students of products understand data , They can talk to data warehouse Engineers 、 Data product manager or AI Directly put forward the construction requirements of simple data products . The buffer for simple data analysis will become narrower and narrower .

2. Data visualization

Many students who did internships in data analysis before , The main work is in a commercial software ( such as Tableau) On , Make some statistical reports . In this way, you can use these data reports , You can easily view some key indicators of your business . These commercial software are usually difficult to use , For example, you may need to pre calculate before you can output the results ; And it's not easy to develop custom functions .

A slightly more complex demand scenario , You may need a special classmate to make trouble for a while , To output the final statistical report . Now there are more advanced routines . First visualization . Many companies have opened up front-end and back-end data , In this way, you can query the original database through the web page to get the data results .

Now there are many excellent front-end visualization plug-ins , It has been able to provide very rich support for statistical graphics . And because the code is open source , Targeted development can be carried out according to the company's demand scenarios , The company can configure some more user-friendly operation interfaces , Such complex requirements also have the possibility of simple drag and drop implementation . And these front ends js The code is free ！

For the company, it can also save the purchase cost of a commercial company . Secondly, a lot of commercial software , They are all designed for small dataset scenarios . In some scenarios with large data sets , Generally, you need to pre calculate some intermediate tables . If the front-end display results of customized development of your company , You can set up computing logic and configure computing resources according to your needs , Perform pre calculation on the back end first , The front end is only used as a result display module , Decouple the result display from the required pre calculation .

This saves a lot of the output of the intermediate table , It will also get the desired business indicators more quickly , Fast iteration . Therefore, the workload of visualizing data will be greatly reduced . And it will become one that everyone can operate , The scene of getting results quickly .

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