2020 has just begun, it means the arrival of left wave peak, around me there are a lot of people holding a year-end awards left, but the most surprised me, was a decade of work data analyst left, is different from the others to resign, he was fired the company.
Many people say data analysis is a good job, work is not tired of high salaries, entry is simple and easy to learn. But this year he's 34, but really tasted the taste of mid-life crisis, usually there are a lot of people will ask me private messages:
Data analysts have midlife crisis? Like programmers are young rice to eat it? How to ensure that they will not be eliminated company?
In the data analysis were removed from my industry for nearly a decade, recently also thinking about how to break through the ceiling of the data analysis of the industry, how the industry actually planned career path?
First, the data analyzed do not eat rice youth
Admittedly, data analysis industry is a midlife crisis, but the Internet industry is different from midlife crisis, this crisis nor that crisis, why? Listen to me slowly come.
1, analysis of industry data limit is too high
It simply focuses on the data itself is not young rice industry analysis, said the industry or are they with the age, size does not matter, but also advise you not to be too optimistic, three hundred and sixty, trekking there are difficulties.
With programmer like, data analysis, occupational watershed are about 30-35 years old, because most people do data analysis, less than a decade will encounter problems ceiling profession, the industry's ceiling limit is too low, a lot of after bottoming people will choose to change jobs or turn to do anything else, it may also have in other industries.
I do data analysis for nearly a decade, we have been also considering this issue, industry data ceilings in fact one can see, but in fact, from another angle, the upper limit of the data analysis ceilings are high.
What does that mean? Business data analysis requires a very high demand for business understanding, enabling the value of the business is very large:
Technology is really not the most important (although it is quite difficult to say head to do, and a great pit depth data), the business is more important, then fast hardware technology, the business is not energized, was cut off entirely possible, because companies rely on to support their business, it is to look at return on investment, whether short-term or long-term.
But we know that the value of business analysis is to bind in the industry, the better development in your industry, you accumulated value is greater, the experience every time you analyze will continue to add value, this is not it young rice species can bring.
2, analysis of industry data limit is too low
说的有点远了，关于数据分析行业不是青春饭的原因，其实还有一个，数据分析行业的门槛太低。
因为这几年涌进来的人实在是太多了，但是很多人都只是停留在了技术（工具）层面：差一点的变成取数机器，机械性太强；好一点的学个python什么的，往技术岗的方向发展，技术岗最大的问题就是容易被取代抛弃。
数据分析工具
3、需要终身学习
先说加班吧，这个行业的加班情况因人而异、因公司而异，总体来说不会很严重，如果是中层分析管理岗的话会比较忙一些，周六周天无休都有可能；
再说终身学习，这一点是肯定的，具体学习量大小要看你的发展方向，偏向于业务的话，技术层面不需要深入太多，主要是业务和管理两个方面的提升；
二、数据分析行业还能进吗？
想要转行进入数据分析行业的，你知道自媒体运营吗？数据分析的情况跟这个差不多，尤其是偏业务的，也都是火了没几年，门槛又不高，不少人趋之若鹜，挤破头也要挤进这个行业，直到现在，整个数据分析行业（只说国内）表面上供小于求，实则水分特别高。
水分是什么？
一是企业，表面上看好像什么企业都想要数据分析，你要明白，你们趋之若鹜的同时国内企业也在盲目随众，现在哪个企业不搞数据化改革、不搞数分平台建设？其实你要问企业真的很需要吗？并不是。
很多企业就是招了一堆人天天做报表，当取数机器。如果你想做的是业务分析师，情况就更惨一些了，在大多数中小型企业和部分传统企业中，业务分析经常是被老板说没价值的，时间久了你自己都会怀疑自己的工作是否有价值。
二是求职者，主要是这个行业门槛太低了，换句说话，门槛不明显。
可能很多人觉得学个r语言、学个python、学个BI就行了，其实用excel做统计都算是数据分析，所以数分的人多而不精。别看行业里人这么多，真正达到分析师高度的人很少很少，大厂企业争得抢的是这样的人。
三、数据分析行业如何规划职业发展？
说了这么多，冷水也泼完了——其实也是为了让你能保持清醒——我再接着说点职业发展的：
If you want to operate classes, data analysis has provided objective analysis of the effect of the conversion of a marketing campaign, activation analysis users to download registration conversion rate, an analysis of downloads advertising channels, each activation cost, user retention conditions, etc. these are generally no special post, often ceo, coo, product, this part of the work of their classmates operations undertaken. Of course, if a large company organizational structure, does not rule out the establishment of a separate, this time you need to have proficiency in data analysis tools, such as mySQL, spss, python, FineBI, or even report rendering.
Another is that research-based data analyst, the general is buried, according to business requirements for data points, monitoring, data processing, report rendering. Profound thing is that big data analytics, BI engineers, machine learning, personalized recommended.
In fact, the main reason for Data Analyst position is not high, is not lack of recognition and value .
We always say data-driven business, you can usually see, but always chasing data business department ass to data, data analysis and the value is difficult to actually show it, do not agree with the leadership, colleagues do not agree, not even their own identity, even suspect what they are doing is not really valuable, this is very common in the enterprise, people basically do the data analysis will shift to management and operations.
to sum up
In fact, the data analysis will be done to a certain extent feel the bottleneck, technology has done a head, but for data analysis, technology is really not the most important (although it is quite difficult to say head to do, and a great pit depth data) , the business is more important, then fast hardware technology, the business is not energized, was cut off entirely possible, as companies rely on to support their business, is to look at return on investment, whether short-term or long-term.
