Industry differences in data analysis positions

It is very important for data analysts to choose the industry and business type that suits their abilities and development direction. Let's take a look at the article to find out.

The role of data analysis varies greatly in different businesses. However, there are few courses or articles discussing this topic, so many students rarely consider the choice of industry when they are looking for a job or changing jobs, because they don’t know what the differences are between industries.

For data analysts, if you are in an industry that is difficult to be data-driven, no matter how capable you are, it is difficult for you to exert your value. You cannot get in touch with the core business and cannot drive business growth, so your growth will become very slow. Therefore, if you enter such an industry, you will be very tired, which will also affect your future career.

Therefore, when choosing an industry, we need to have a certain understanding of what kind of industry we should go into.

1. Three different types of business

What are the different types of businesses when it comes to data analytics?

If we rank the demand for data analysis in a business or an industry from strong to weak, we can divide it into three categories: the strongest is strong demand, the weakest is weak demand, and the middle is medium demand.

1. Business with strong demand

Let’s talk about the business with strong demand first, the first one with strong demand.

Strong demand refers to data analysis that can bring clear incremental benefits to the business.

Let me give you a few examples of such businesses, such as ride-hailing platforms, shared bikes, and supply chains. The relationship between the data and the business is very clear. For example, in the supply chain, you can know that the inventory amount of this warehouse has increased, and the inventory amount of another warehouse has decreased. You just need to move the inventory amount of this warehouse to that warehouse.

You will be able to improve your overall inventory stocking efficiency. This is very clear and can even be fully derived using a numerical formula.

For example, a taxi-hailing platform is also in a certain area, and it lacks transportation capacity during the morning rush hour. So, during the morning rush hour, I mobilize more transportation capacity in a certain area, and then these transportation capacities can form new orders. This relationship is relatively clear. Because the user's demand for travel during the morning rush hour is fixed, as long as you have transportation capacity, it can be completed. Therefore, this type of industry or business has a very clear demand for data analysis.

2. Business with weak demand

After talking about strong demand, let’s talk about weak demand.

Weak demand means that the demand for data analysis in this industry or business is very weak, and it is likely to be sales-driven or product-driven.

For example, if sales are driving the business, that is, providing services to customers of large companies, then generally speaking, if you, as a salesperson, have a good relationship with the customer, you may be able to close the deal by making two more trips. However, for data analysis, the decision-making process in the closing process is too long, and it is difficult to analyze the reasons.

This type of business is very dependent on sales. There is very little you can do with data analysis. You may run 100 data analysis conclusions, but it is still not as good as having a salesperson make two more trips.

The same is true for product-driven, that is, you can provide a product experience that others cannot provide. At this stage, data analysis can provide little help, such as the current chatgpt product. It is difficult for other products to provide the same experience, so at this time you only need to continuously improve the product, and your users will continue to grow. This does not involve refined operations, and does not require data analysis.

3. Medium demand business

After talking about strong demand and weak demand, the business in between is medium demand.

This part of the business is relatively clear in the general direction, but unclear in some small details. These small details need to be concluded through data tools and analytical methods, so that the business can find a breakthrough point in a certain direction to save their trial and error costs.

This kind of medium-demand business is actually the most challenging for data analysts, because this kind of analysis does not have a specific direction, it only has a vague general direction. Therefore, the conclusions drawn by good data analysts and bad data analysts may be very different. A good data analyst may be able to help the business achieve higher growth, while a bad data analyst cannot achieve this goal. Therefore, in this kind of business, data analysts can make a difference.

This involves a lot of creativity. You must have a good understanding of the business, understand business logic, understand user psychology, understand the entire transaction chain, etc. This kind of medium-demand business is generally the type of data analyst that everyone thinks of.

2. Their respective characteristics

Next, let’s talk about the characteristics of the three types.

The first type is the business type with strong demand, in which the relationship between various indicators is relatively clear.

It is a bit like doing accounting or solving math problems, because the change of one indicator can clearly deduce which other indicators it affects, so this requires you to have a very good understanding of the relationship between the various indicators of this business.

The second type is weak demand businesses, which have very low demand for data analysis. Therefore, in most of these businesses, all you can do is just collect data. In the eyes of the boss, this is a sales-driven business, and your core value is to help sales to better complete their tasks. In this type of business, the status of data analysts is relatively low, and they are basically just a tool for collecting data. Even if you find a very powerful person to do this, it is difficult to bring value. This is not determined by the person's ability, but because his business model does not require data analysis to help him find incremental growth.

The third type is medium-demand business, and this type of data analysis is a bit mixed.

There are good businesses and bad businesses. This type of business can widen the gap in analysts' levels. The upper limit is very high, and the lower limit is also very low.

Some people may also be doing data collection work in such businesses. Because they don't have good business ideas and can't organize analysis, the things they provide are actually similar to those given by businesses with weak demand, which are all data collection content.

But for those who are capable, in this medium-sized business background, they can dig out a lot of valuable information, and if you can prove your value, then your salary can be greatly improved.

3. Growth Path

Next, let's talk about the growth paths of these three different business types. We can use a pyramid diagram to represent the relationship between these three business types.

The base of the pyramid is the business type with weak demand. This type of business is mainly about collecting data, and reporting work rarely involves analysis. It must be at the base of the pyramid.

At the top of the pyramid are businesses with strong and medium demand, it is on the same level, so I put it on the left and right, they are equal.

Both types of people can have a good growth path. You can become a supply chain analysis expert, a transportation capacity analysis expert, and similarly, in medium-demand businesses, you can also become a business analysis expert, a user growth analysis expert, a commodity analysis expert, and so on.

The growth path we hope for must be from the bottom to the top, but when going up, you must consider clearly whether you are going to the left or the right, because it will be more difficult for you to transform to the left or the right.

On the left side of the pyramid, there are businesses with strong demand, and the business logic of this type of business is relatively fixed, while on the right side of the pyramid, there are businesses with medium demand, and their business complexity is higher. They will be exposed to more problems with unclear analysis directions, so these two types of businesses are difficult to transform, and the business logic they need is different.

So how do you choose? Left or right?

Let’s talk about the advantages and disadvantages of each side.

First, the business type with strong demand on the left is stable but less adaptable. It is stable because of strong demand. Its analysis logic is relatively fixed. And there are many business details. For example, each company in the supply chain, its warehouse, its logistics distribution, etc. are all different. So after you stay in a company for a long time, there are many business details. After you master them,

The disadvantage is that you have poor adaptability. If the company itself fails and you have to look for another job, it will be difficult for you to find a suitable job because your experience is only valid for that company. If you want to continue to use it in other companies, you have to accumulate a lot of business details about that company again, and your choices will be narrower.

For example, if you analyze the capacity of shared bikes, what direction can you choose? You can probably only look for industries related to capacity, and what about these positions? They account for a relatively small proportion of the total job pool.

Another point is that the business with strong demand is a bit like accounting, so this job is more suitable for those who are more meticulous. He likes to calculate the relationship between various data, and its growth curve is also relatively linear, because if you learn a concept about inventory today and a concept about turnover tomorrow, then you can use this knowledge to solve some specific problems.

Now let’s talk about the medium-demand business type. This type of business is relatively less stable, but more adaptable.

The stability is relatively weak because this type of analyst is good at digging for incremental business. If the industry as a whole is going up, it is easier for data analysts to find incremental business. But if the entire industry is in decline, then the value of data analysts will be very embarrassing. At this time, data analysts are easily laid off, so the stability is relatively poor. However, on the other hand, its adaptability is relatively strong.

Because this type of business analysis relies more on some business logic, and this business logic is universal among different companies. So even if you are laid off, when you choose a company again, you have a wider range of choices. You can choose from many companies, and your choices are wider than those with strong demand.

In addition, what about medium-demand businesses? Their growth curve is relatively exponential, which means that they grow very slowly at the beginning, and this kind of growth is uncertain, because when you use business logic to solve business problems, you may understand the business concept today and tomorrow.

There is no knowledge system between these business concepts. It is difficult for you to solve a specific problem because when you solve one problem, there may be problems on the other side. Only after a few years, when you have accumulated enough experience to combine your theoretical knowledge and understand your own ideas, you may suddenly feel enlightened one day. Then, you suddenly transform into an advanced data analyst. Therefore, this growth path is relatively unclear. It is more suitable for students who are more interested in business.

Then, the salary distribution shows the 80/20 rule. The best people will have significantly higher salaries than other analysts. And what about those in the business with strong demand? Although the salary distribution is also high and low, the gap is not that big.

IV. Conclusion

Okay, this is the end of this article. I hope this article can help you understand the differences in data analysis in different industries and help you better choose the direction you want to go.

Author: Jason;

Source public account: Ternary Variance (ID: 686668)