Three leaps in the growth of a data analyst

This article explores the key stages of career development for data analysts, from mastering basic data acquisition tools to deepening business understanding, to organizing and leading data projects. Each step is a test and improvement of the analyst's ability. The article not only provides practical growth paths and methods, but also points out possible career development directions for data analysts.

Search: "How to quickly become a data analyst" and you will find a lot of articles online. However, what happens after becoming a data analyst? Where is the future? What is the end of the career? Few people have discussed this in detail. Now, let's seriously discuss this issue: the three leaps in the growth of data analysts.

The first leap: mastering data acquisition tools

The first truth is: Data analysis positions seem to have high salaries, but they are all due to the IT department. Data analysis positions under the IT department are paid according to programmer standards. Therefore, if you want to be a professional data analyst, mastering SQL data acquisition capabilities, being familiar with at least one BI tool such as Tableau/Power BI/Fine BI, and having an understanding of Python data processing and data science packages are the entry tickets.

This is the first leap that must be achieved: mastering the data acquisition tools.

Some students in school often have fantasies: I want to find a "business analyst", "operation analyst" or "business analyst" who doesn't write code. First of all, as a fresh graduate, you don't understand business/business, and you don't have the tools to retrieve data. You can only do the lowest level of organizing Excel cousins, and you get paid as a clerk, which is shockingly low.

Therefore, I often advise students to:

• Read "SQL Must Know", if you are not interested, you can just give up
• Go to Niuke.com → Online Programming → SQL and go through the questions
• Find a data analysis internship writing SQL for three months

This can effectively avoid "Ye Gonghaolong"! In fact, if you don't want to retrieve data, you can be an operation, product manager, or planner who understands data. There is no need to be a full-time data analyst. When you can skillfully retrieve data from the database, you have achieved the first leap.

The second leap: reflecting business value

The growth of data analysts is not linear. It is not that junior data analysts write 500 lines of SQL every day, intermediate data analysts write 1000 lines of SQL every day, and senior data analysts write 2000 lines of SQL every day. The kind of person who writes 2000 lines of SQL every day is also called: data checker, SQL boy, human data extraction machine, data tool person... In short, it is not a good state.

The second truth is that data analysis is essentially a service position that supports business work. Therefore, you need to find ways to reflect your business value, so that you can take the initiative in your work and avoid passive data collection; this can better reflect your performance and lead to promotion and salary increase.

There are many ways to demonstrate business value:

• Output indicator system to comprehensively monitor business
• Proactively discover problems and alert business concerns
• Proactively identify opportunities and improve business performance
• Conduct scientific experiments to validate business ideas
• Output analysis report and provide useful suggestions

This step is difficult to achieve because:

• Lack of reference books, my industry is too unique
• The company lacks a communication atmosphere and the salesperson is indifferent to you.
• A lack of guidance from your direct supervisor will only make you “Think more!”
• I haven't seen any successful cases, so I don't know how far it can go.

Therefore, students often get stuck at this stage.

There are ways to break through this stage. The core of this stage is to change your mindset from "learning answers from tutorials" to "training your own logical ability and finding answers yourself".

Generally recommended to everyone:

• Communicate more with peers to understand the situation of each company
• Communicate more with business partners to understand business processes/customary practices
• Train your logical ability and think more about how to quantitatively describe the business
• Participate in data competitions and learn about analysis practices in various industries and businesses

These are more about the growth of knowledge. There is no standard answer, but the more you accumulate, the more you can improve your ability to deal with problems. When you can skillfully convert the words of the business into an analytical logic tree, you have successfully passed this stage.

The third leap: organizing data projects

The third truth is: like other IT teams, if you can only work alone, it is difficult to expand the department and get a promotion and salary increase. In fact, data projects are more difficult to do than other IT projects because the business expectations are often very high. Under various external propaganda, people always think: "As long as there is data, you can make accurate predictions and know everything..." The contradiction between the business side's overly high expectations and poor infrastructure has always been the number one contradiction in the field of data analysis.

To do a good data project, you need:

• Technically, you should be familiar with data acquisition/model/BI tools, and even if you don’t do it yourself, you should know how to select high-quality partners/reliable suppliers.
• In terms of business, you should be familiar with common business problems, be able to keenly identify demand gaps, grasp the valuable parts, and reflect the value of data.
• In terms of management, he is familiar with project progress management, and will urge the business to clarify requirements/urge the front-end to do a good job of tracking points/urge the data warehouse team to cooperate/urge the leadership to confirm the design of the dashboard.
• In terms of reporting, be familiar with the reporting routine, paint a big picture before the project starts, control expectations after the project starts, and make a fancy ending after the project ends to reflect the credit.

It can be said that good project organization is the comprehensive application of one's own technical/business experience in the past few years. If the project is done well, the leaders will pay attention to the data team and give you more staff. If you can expand the team, you will successfully achieve the transition to management level.

This stage will hold many students back, because many companies don't have project opportunities at all. Students who are lucky can start with small projects (usually small special reports) and gradually improve their abilities.

Of course, not all students will make it to the end. Many students get stuck in the second step and think that running data is meaningless and change careers. In fact, data analysis skills are applicable to many jobs. For example, business positions such as strategy products, user operations, risk control, product management, sales operations, etc., and development positions such as data analysis warehouses and algorithms also have certain opportunities.