Continue to the volume, a comprehensive inventory of data career development paths

At the beginning of 2024, many students are thinking about how to develop this year. There are a lot of articles on the Internet about how to develop data analysis from 0 to 1, but there are few introductions to the subsequent development paths. Today, I will give you an inventory of the various development paths of data analysis. There is always one that can get you a promotion and a raise, so take your time to read it.

First of all, everyone must be clear: data analysis is not a job that is distributed according to work. When entering the industry, many students are learning SQL and Python, but after really entering the industry, it is not the one who writes more SQL that gets paid more. Even the human data extraction machine who writes 2,000 lines of SQL a day was criticized for "only doing basic work and lacking in-depth insights" when he was promoted.

So don’t just bury your head in work, it’s important to look up and see the road!

1. Internal promotion opportunities

To be promoted within the company, several conditions are required:

• The company's performance is growing
• The data department has room for expansion
• The data department has performance A quota
• My direct supervisor recognizes my performance

Note! Data analysis is not a position that directly generates performance, so unlike sales, the company will not expand the team and increase bonuses in order to boost performance. The company is growing, the team is expanding, and this is the basis for personal promotion. On this basis, if your direct supervisor likes you and supports you, you will have the opportunity to strive for a promotion.

Of course, many students do not have these opportunities, or the company is not good, or the relationship with their direct supervisor is not close. It doesn’t matter, see the next item.

2. Opportunities for job-hopping in the same industry

If you want to increase your salary by changing jobs, the fastest way is to change from one industry to another. Everyone likes to take advantage of what is already available, and it is easy to get a high salary if you have experience in the same industry.

Note! The same industry here does not simply mean the same industry, but the same business analysis experience, for example:

• Sales: Salesperson, telephone sales, online purchase
• Operation: users, products, private domain,
• Production: Discrete and process production
• Supply: Logistics, Warehousing, Distribution
• Products: content, e-commerce, tools
• Commodities: Durable goods, fast-moving consumer goods, fresh food

When looking for a job, you must pay attention to the requirements of the target position! If you find the right direction, it is easy to achieve a comeback, for example:

• Although it is a traditional enterprise, it has built its own APP and has a full set of tracking points + user behavior analysis experience
• Although it is a traditional enterprise, it has mature CRM operation and a full set of sales operation experience
• Although I was laid off by the Internet industry, I have experience in user analysis and refined operations.

This is easy to achieve: from traditional to Internet companies, the salary doubles; from Internet to traditional enterprise digital marketing department, successfully landed. On the contrary, some people called "Internet giants" actually only do marginal businesses such as content review, customer service, and telemarketing, and it is difficult to land.

On the contrary, if you look too superficially, you may fail the interview even if the industries seem similar. For example, the "e-commerce" that people often talk about actually has three types of companies:

• Platform type: large e-commerce platform, serving users (C-end) and merchants (B-end)
• Settled type: companies that sell goods on Tmall, JD.com, and Amazon
• Self-built: Brands build their own apps and sell their own products

The three types have different experience requirements. Platform-based positions often recruit people for the C-end and B-end respectively; the settled-in positions mainly focus on product management and purchase optimization; the self-built positions often require more C-end analysis experience. If you are not prepared accordingly, it is easy to fail the interview.

Some students may ask: I don’t have in-depth business experience, what should I do? See the next one.

3. Opportunities for skilled workers

Many students usually have no experience in business analysis. They just mechanically generate reports and collect a lot of temporary data. This type of students is also the most eager to change jobs. But due to lack of experience, they don't know what to do.

Note! Large companies also have entry-level positions and support positions that do not require deep business experience, but only support data collection. This type of job description generally has the following statement:

• Support daily analysis needs of sales, operations, products, etc. (not limited to types + a bunch of departments)
• Output regular reports and monitor business trends according to business needs
• Diagnose business movements, identify business problems, and provide analysis suggestions

In short, they just talk in general terms and don't consider in-depth user, promotion, and product issues.

This kind of position is very suitable for low-level tool people to land, but some preparation is required:

• At least sort out the needs/reports you have made, and figure out which departments/needs you serve.
• Be familiar with the indicator system of the regular business department and don't stutter during the interview
• For routine abnormal diagnosis and problem solving, you should be familiar with the routine and it is best to prepare in advance.

Only by being well prepared can you pass the interview. Because there are too many similar questions, I directly organized them into two columns, "Nine Methods" and "Data Report Design", and posted them on Knowledge Planet for students' reference.

4. Opportunity to change track

Data analysis is a very useful skill. Being able to do data analysis does not necessarily mean you have to work as a data analyst. Some high-paying business positions also require data analysis skills, such as:

• Operations: User operations, product operations, sales operations
• Product categories: Strategy products, data products
• Management: Business Analysis, Regional Management

These positions are mostly core business positions, usually requiring:

• Monitor business data trends
• Analyze data to find problems
• Calculate strategies based on data

Therefore, it is closely related to data analysis. If you don’t like working with SQL and are interested in business, you can switch to it.

It should be noted that these positions are business positions and have certain requirements for business capabilities, so you need to prepare in advance, such as:

• User operations: common user grouping methods, user rights settings
• Product operation: common product promotion, scheduling methods, product selection
• Sales Operations: Sales Training and Common Motivation Methods

With accumulated business knowledge and data experience, it is easy to change tracks successfully.

Author: Down-to-earth Teacher Chen ; WeChat public account: Down-to-earth Teacher Chen