In which area are analysts more in demand?
It seems to me that the most popular industry right now is product marketing analytics. And maybe even product analytics, which is less common in companies. Financial analytics is very old, it has a lot of people who work traditionally, you can even meet those who have dozens of years of experience. Marketing has also been developing for a long time, but it is younger than financial analytics. Product analytics is becoming more and more in demand in the context of IT product development. This topic is also starting to migrate offline - product management concepts appear in offline products as well. That is why it seems to me that this is the hottest area. Ultimately though, in each of these areas, you should be able to find a good job with a good income if you are a professional.
How important is it for an analyst to build his brand and, for example, write articles about his cases on Habré, VC or somewhere else?
Here you need to decide what you want to achieve. Analysts are not very sociable people and there are not many communities of data analysts, but this does not prevent them from building a career. If you want to share something with the world, then, of course, it is worth doing all this. And when you become an open person, build your personal brand, it becomes easier for you to make acquaintances, you are better known and it is easier for you to find work and some opportunities. If there is such a goal, then go ahead! But if you just want to find a job, then a resume with the results of past work will be enough. Let's just say it's useful, but not required.
Where to get ideas for process automation?
It is worth looking at which things require the most work and in which more people are involved in order to understand whether they can be replaced with some kind of automatic things, for example, through ETL processes. And the cost of automation should be covered by reducing manual labor, so that these people are either completely freed, or would be engaged in some other things. We take the process, see how labor-intensive it is, how much it makes people nervous and how many mistakes it makes, we rank all the basic processes that take place in the company according to these criteria. Usually, when you do such work, it becomes clear what can be done and what is more important to do.
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The question here is what is more attracted to. In companies with less order, a person gains more managerial experience. He needs to assemble different engineers to understand what is worth doing, he needs to react quickly and keep a lot of things in mind. This is a cool experience, especially if you aim at management and you are interested in the breadth of knowledge. But, in such companies, as a rule, there is not enough time for the depth of knowledge. If you want to do complex tasks, apply some interesting methods, then it is better to go to a company with built processes. Therefore, it all depends on which direction you want to develop. And there, and there there is a way to build a career and the level of income is comparable.
How to switch from QA to analytics?
I don't think there are any special rules. If you do not have experience, then in the resume you have a blank sheet. In this case, you need to act like a junior: look for places where you would be hired when you have little or no experience. At the same time, if you, as QA, understand things related to the structure of the database, you can write SQL queries, then you already have a certain database when entering analytics. It will be immediately clear to those who hire you for what tasks you can be abandoned and not require analytical skills from you, immediately benefiting, gradually broadening the tasks where you would develop your analytical skills.
What skills should you include on your analyst resume? What will be a plus, what will be appreciated?
The first thing that will be appreciated in any resume is a description of the real problems that you solved. Not a sad story in which there were some strange processes that constantly fell on you, and you thought this matter thoroughly, it is not clear why. It is best to mention specific achievements, invented metrics, solved problems, when problems were solved with the help of analytics and your work in business. This, first of all, suggests that you can be assigned a task and you can find a solution. For an employer, the most important thing is to understand what will benefit you as an analyst.
In addition, it will also be a plus if you indicate skills related to technical things: SQL, BI-systems, Python ARL (it depends on the company, since not all companies require it).
Is SQL and Python knowledge analytics enough to start a career, without statistics and matanalysis?
This is probably a good set to start a career. But this is clearly not enough to be an analyst. You will need to gain the necessary knowledge. Knowing SQL and Python, you can go to the data engineering specialization, where you will be more involved in the preparation of data transformation. This is also a very popular profession, very important for analysts, since analysts cannot work with unprepared data. Here you won't need statistics and calculus. It is possible that this will be an even more interesting area for you. But, if you are still interested in the subject area of business, it is interesting to get into statistics, then you need to get these skills at work, be interested in this topic and understand how you can apply these methods in your work.
Often Junior is a person with experience up to a year, but how to start if there is no experience? Looking for related jobs?
My personal experience is this: I was invited to work as an analyst, because I asked too many questions inside the company. I worked in a company and pestered the analytics department with all sorts of questions about the client base, about what happens to them and how it works, and one day they made me an offer to work for them. So I got into analytics. Quite strange and random.
And if I purposefully looked for work in analytics now, then I would have acted by way of “forehead”. One needs to write a resume and look for companies that accept people with little experience or interns to gain this experience. There are a certain number of companies that are ready to pay you a little less, but you can gain experience that you can use in your future resume. This is the main way to start. If you look for related vacancies, this may become too roundabout. As a result, you will work in several places and still you will have to explain to the employer how all these roundabouts are related to the vacancy that you are trying to get into. Chances are, you won’t win by wasting time on related work.
How can you motivate a cool analyst to become a mentor? What can a newbie mentor be useful for?
Being a mentor is a character trait. A person should like to tell something, share knowledge, and to some extent satisfy self-esteem and self-importance through teaching others. This is the motive for a good mentor to be interested in the people with whom he works. Such mentoring provides ways to fulfill oneself. That your knowledge lives in other people, that you develop other people, that you can make some kind of change in the analyst community, thereby spreading your approaches and methods in this community. And also, mentoring is one of the stages to management. If a person wants to control other people, then leadership in many ways consists in developing the people with whom you work. This is what it is useful to be a mentor for.If a person just wants to solve problems and is not interested in teaching other people, he is absolutely not worried that his colleagues do not know how to do something, he does not have such an itch to teach them this, he believes that there is his clearing on which if he needs to work, it’s better to use this person for his intended purpose, so that he works in this clearing and is effective, loading him with more complex tasks. And take someone else as a mentor.
Wrike ?
At Wrike, the work is structured so that we have product teams that have a product manager and, usually, a product analyst is assigned to him, who together with him understands what is happening with the product, and together they try to understand what metrics are needed, how to apply them, how to understand the existence of success. The analyst finds out what data is needed and how to calculate it all.
Our toolkit is Tableau, ARL Python and SQL queries. We collect a lot of data on customer behavior, aggregate data on marketing, finance, and all this is in our centralized data warehouse. This is our main toolkit. If you remember about putting things in order, then we have a significant part of the basic order already established and we need to dig deeper into the products themselves, to understand what is happening in them.
Data Scientist, Data Analyst, Business Analyst: , , ? , , ?
It seems to me that you cannot mix these three directions. It's like taking a person who is both front-end and back-end, and knows 20 languages, writes all the requirements and manages projects at the same time. Nothing good will come of this, there will be no specialization and depth in any of the directions. Accordingly, it is better not to mix these directions.
Data analysts use methods from business intelligence and data science, but specialize slightly in other issues. Like data scientists, they need to understand certain techniques from business and data analytics in order to do their job effectively. If a company cannot hire all three specialists, the question is: "Why would it want to hire these or those specialists?" These specialists solve a certain range of tasks.
Business analysts formalize what we want to do so that the developers understand what they need to do so that the requirements are in their normal form, with which the business customer agrees and which the developers understand. If a company specializes in software development, then they either need a separate person for the position of a business analyst, or this will be in the competence of a product manager. If a company wants to analyze the data that it has, make decisions based on this data, then they need data analytics. And if a company wants to engage in machine learning, if it has a subject area for this, if there are some tasks that can be solved using ML, then it needs to hire a data scientist or outsource them.
When you give a data scientist tasks a date analyst, he starts to feel sad, as he will find that he hardly does machine learning. And it's just crazy to try and screw machine learning into a lot of data analysts' tasks. Accordingly, a business analyst may simply not be able to work with data, this is not his direction. Therefore, this practice does not make sense.