Technology or business? A comprehensive review of the development path of data analysis

There are still many advantages and opportunities for data people in future development. Today, let us take stock and analyze the development path of data analysis work.

It’s the end of the year again. At the end of the year and the beginning of the next year, everyone will be particularly concerned about future development. Today, I will review the development path of data analysis work with you.

Whether you are standing outside the door with aspirations, or have just entered the industry and are full of confidence, or are exhausted from running numbers every day, you can seriously review the advantages and opportunities of us data people.

1. Three major advantages of data analysis positions

Advantage 1: Large growth space

For pure business positions, growth is greatly restricted by leadership:

• For example, in operations, no matter how good your ideas are, your boss just doesn’t agree with you, and you can only be a brainless tool.
• For example, in sales, no matter how capable you are, if your leader doesn’t provide you with good customer resources (or even snatches them away openly), you will still not be able to achieve results.

But data analysis is different. As long as you have access to data, you can analyze it and draw conclusions. Excellent data analysts can not only analyze what is feasible, but also what is not feasible. Therefore, the growth of analytical ability is completely unlimited (as shown in the figure below).

Even data analysts trained in small companies with chaotic management, poor data foundation, and irregular processes have a deeper understanding of data collection and more knowledge of the business, so they have stronger practical capabilities than small flower buds grown in greenhouses. So don't lose confidence easily, keep thinking: "How can we do better?", and you will make progress.

Advantage 2: Wide range of applications

In itself, data analysis is a basic skill that is in demand in all kinds of positions and companies. In particular, the recent wave of digital transformation in all walks of life has given data students many more companies and industries to choose from in terms of business and technology.

On the other hand, the 996 work schedule is common in the Internet industry, and the digital transformation of large traditional enterprises is deepening. The decline and rise of the other have led to the fact that joining an Internet company to participate in internal competition is no longer the only way out. Instead of being oppressed in a small factory and waiting for the opportunity favored by the first party, it is better to consider the opportunity of Party A (large traditional enterprise)/Party B (toB service enterprise).

Advantage 3: Wide choice of future

In recent years, there are many positions with the title of "data analysis" or "data XX", which confuses many students. Strip away all the confusing concepts. The essence of data work is to focus on business and technology:

Business-oriented: Generally under the management of the operations, marketing, and sales departments, they operate existing data products or write SQL statements based on large wide tables to extract data. They spend more time writing PPTs than writing code.

Technical: Usually under the IT department, or with an independent data group/data department. They all write code and occasionally present PPT. In large companies, data warehouse, data governance, BI, analysis, and modeling are clearly separated. In small companies, it is very likely that you do everything yourself.

Data analysis work is right at the intersection of business and technology, so there are many possibilities for choice (as shown in the figure below).

So, theoretically, if a data analyst wants to change careers, he or she can succeed in any direction. The worst thing is that he or she is indecisive, has no firm direction, knows only the basics of the business, and is unwilling to go deep into the technology. Then even the gods can't help. Or if you want to take care of both, you end up with no specialties and become a test-taker. If you want to change, just move firmly in one direction.

2. Opportunities to switch business lines

Can data analysis be transformed into business?

Answer: Yes!

If you don't want to develop in the direction of programmers, and want to use analytical skills to seek a better business position, you can consider this route. But please note: business departments are also divided into four types (as shown below).

Among the four types of business departments, the most likely to achieve success and have the most power are strategy positions. If a membership system or annual promotion project is completed well, promotion and salary increase are just around the corner. Strategy positions are the closest to data analysis. As long as data analysts have additional business knowledge of strategy work, it is easy to transfer to other positions and achieve results.

The core capabilities of the other three categories are far from data analysis, so they are not very advantageous if they are transferred to other fields. But what is interesting is that as the proportion of online sales increases, positions such as channel operations, sales operations, and traffic buyers also need analytical capabilities.

The biggest benefit of switching to another business is that you can reap the company's growth dividend. During the company's high-growth period, the salary and bonuses given to strategy and execution positions are very generous. There are quite a few students who have switched to these two types of business and have made a lot of money as the company grows. It is worth looking forward to it.

In recent years, there are some new terms, but they are essentially new wine in old bottles, such as:

As long as you understand the essence of the position, it will be easy to see through the mystery and find opportunities for promotion.

3. Opportunities to switch to a technical line

Many students know that data work is shifting towards technology.

• Switch to big data development direction: Big data engineer
• Switch to algorithm direction: Algorithm Engineer
• Change product direction: Data product/BI engineer

The technology stack in each direction is also relatively clear.

What is uncertain is: in the current market environment, should we switch? The most typical example is: the Internet finance industry has been swept by regulators, and a lot of algorithm/product opportunities have been lost; at the same time, a large number of fresh graduates have poured into algorithm/data development positions, resulting in unprecedented internal competition and higher recruitment thresholds.

For algorithm positions, purely in terms of technical difficulty, it is still risk control ≤ recommendation ≤ CV/NLP. Although small mutual finance has been swept away, many platform companies/e-commerce companies have added anti-fraud positions, which can also exercise risk control capabilities; the path of traditional credit card centers turning into large financial institutions has not been broken. So if your development ability/theoretical knowledge is not strong enough, you can still look for risk control/anti-fraud directions first. As for whether your technical ability has reached a higher threshold, you can challenge it yourself.

As long as large enterprises exist, big data development and data products will never fall behind, especially for enterprises undergoing digital transformation. Talents with technical strength are in short supply (people are all in the Internet industry, and there is little attention paid to opportunities outside). The technical direction is definitely promising. The so-called "involution" is purely caused by too many fresh graduates. People with 3-5 years of development experience and practical experience can still find a place.

4. Pure Data Opportunities

Can Pure Data continue to work? Of course. But please note that the growth of Pure Data has little to do with personal ability, but a lot to do with organizational structure.

• The organizational structure determines the growth space of pure data
• The organizational structure determines the growth space of pure data
• The organizational structure determines the growth space of pure data

These three sentences must be remembered firmly!

Because although companies are talking about "digital transformation" and "data-driven business", they don't even have an independent data department. And having an independent department is the basic guarantee for promotion and salary increase in a company.

• If there is no data department, no matter how much you do, you will just be a senior soldier.
• If there is a data team, there is an opportunity to become a management level and a team leader.
• If there is a data department with n groups, then there is a high probability that you will have the opportunity to become a team leader or even a director.

Therefore, students who want to delve deeper into pure data lines should remember that the current situation is just a stepping stone. The goal is to find a company that is large enough and has a formal structure, so that subsequent development is guaranteed. Of course, if you can find a company that is in a bonus period, ranks high in the market, and has a good cultural atmosphere, it will be icing on the cake.

5. Methods for assessing opportunities

At this point, all three paths have been introduced. I guess many students can’t wait to ask: Which path should I take?

Note that when choosing a development path, you should not only consider the growth space of the position and skills, but also:

• Personal qualifications, abilities, interests.
• Personal family, environment and other constraints.
• Personal ambition, discover your goals.
• Under existing conditions, individuals can match industries and companies.
• The gap between personal expectations of the company + job requirements and the current situation.
• The feasibility and timing of closing the gap.

Therefore, how to choose requires a specific analysis of the problem. Just listening to other people's experience sharing, but not being able to copy their background, opportunities, and abilities, is just amusing yourself, and cannot solve your own problems.

Author: Down-to-earth Teacher Chen

WeChat public account: Down-to-earth Teacher Chen