data analyst It is mainly concerned with exploiting the information collected through various channels with the aim of facilitating Make decision at the executive level. To do this, he is responsible for creating the necessary databases for the company and then ensuring its smooth functioning.
So his business consists of creating, managing andBig data analysis that is, the company’s data, in order to promote its operation and development.
By analyzing this raw data, the data analyst has the opportunity to generate highly useful information to aid and support decision making and gain a competitive advantage. This analyst plays an important role within the information department as he is the guarantor of the quality and consistency of the structured data. In addition, it is directed to data analysis with a marketing eye, which gives him an advisory role.
must enjoy Data Analyst with specific skills , particularly in computer engineering. Computer language should not have any secrets for him. It also uses many statistical tools and methods that will help it identify trends that can lead to recommendations on strategies to be adopted.
The data analyst is responsible for analyzing data from the company’s activity. It collects and processes data in order to provide relevant recommendations. Its missions are to bring data to life by interpreting it.
It uses information collected through various channels aimed at facilitating decision-making by managers.
For this purpose, the data analyst uses many tools and languages, including Excel but also SAS, SQL, VBA, ACCES or even R.
His skills in statistical techniques and mastery of numbers facilitate this task. He also understands and practices database languages SQL different. He can also, depending on the company he joins and the tasks assigned to him, use tools such as Hadoop or Spark To convert raw data into useful information. But in general the more technical data scientists use it.
Its role responds to the challenge of exploiting the mass of data companies collect. His ease of relationships allows him to interact with trades as well as simplify technical issues. Thus, he is able to present a coherent vision of his company’s activities.
Often, a data analyst works with a type of company from various sectors of activity where data analysis creates added value (banking, insurance, e-commerce, automotive industry, etc.).
Given the significant increase in the amount of data collected, it is very likely that the data analyst will see his position evolve over the next few years. There is a lot of data that is being collected waiting to be analyzed and therefore not used. The job of a data analyst is to address this situation of increasingly large and diverse data. Its role is to find new ways to process data with the support of new tools.
This profession requires a bachelor’s or master’s degree in the field of statistical studies. A data analyst could hold a master’s degree in statistics/econometrics or a specialized master’s degree in big data, for example.
Business knowledge, particularly in marketing and client relations would be appreciated. After gaining significant experience, a data analyst may eventually progress to a data scientist position.
As a junior data analyst, he can expect to earn at least €45,000 per year. Obviously, this pay is variable. However, with so many important tasks in charge, a Chief Data Analyst can earn up to €65,000 per year.
Also, if he later becomes a data scientist, he will be able to claim a larger bonus (up to €180,000 per year).
junior: 45 thousand – 55 thousand dollars per year,
professional: 55 thousand – 65/70 thousand dollars per year,
expert: 70 thousand – 80/90 thousand dollars per year.
A typical day for a data analyst depends on the organization he works for and the tools he uses. Some use programming languages, while others prefer statistical software combined with Excel. Depending on the problems to be solved, the method used will also not be the same.
More experienced analysts can be considered “junior data scientists.” They will then be required to write queries, develop custom solutions, and explore and work with relational databases Hadoop And NoSQL on the same day.
Generally, during a data analyst’s day, he or she will collect, organize, and analyze data to discover valuable information that his company can use. To do this, he will have to develop systems for collecting data and compiling his findings into reports.
A lot of organizations have invested money in the tools. Having the right tools is required but not sufficient. Investing in people who use tools is more important! The challenge is that good analysts are hard to come by.
Here are the eight indicators every analyst should strive to develop:
“If you can’t explain it simply, you don’t understand it well enough.” – Albert Einstein.
simple-lego Why do I need to be a good storyteller? Because people respond well, with information, within a story. They may not be able to remember the whole story. Data needs someone to articulate and simplify it in a way that the line of business, project managers, and project team will understand.
A good analyst should be able to communicate or present ideas clearly and confidently so that a non-technical audience can easily grasp the topic and also, this can help influence decision makers towards the right decisions. Spend less time preparing data
A good analyst must pay attention to detail, and this can help him ask questions or manage suspicious events during any data analysis project to avoid making a costly mistake in the future. As the saying goes, “the devil is in the details”.
A good data analyst should have a solid understanding of business operations. In any organization, the analyst must be commercially familiar with the client, the people within his team, the different departments, and the line of business. The analyst must understand and discern the true impact of his analysis and how it may influence organizations’ decisions commercially.
There is a difference between “insight” and “actionable insight”. Insight is an accurate and deep understanding while actionable insight is information that can be acted upon, with the implication being that action must be taken. Actionable insights measurably improve performance resulting in increased revenue and/or efficiencies. A good analyst is a champion of change.
The ability to demonstrate data competence, flair, and proficiency in handling data to solve or answer enterprise questions should be a habit of a good analyst. This quality will help the analyst develop the ability to detect when things or a data problem are very wrong.
I’ve seen an analyst produce analysis and make recommendations based on dirty data. The analyst must be comfortable with large volumes of data from disparate data sources, and be able to identify relevant patterns and trends.
“It is all about finding calm in the midst of chaos.” — Belinda Davison.
It is important to be able to look at the discrete thought or action, to see the pattern that ordinary people ignore, and also to translate those less obvious patterns into business meanings. We rarely see people born with this skill, but it can be developed.
Communication and collaboration skills are vital to the success of business analysts. So they must be people.
A good analyst should be comfortable communicating and liaising with a whole range of people across various areas of the business. A good analyst should be friendly as he will be the man/woman who is the mediator between the people and the line of work. This will enhance his interaction with people in order to easily understand the business requirements and deliver an outstanding data analysis project.
According to Ed Parker, “An intelligent man is one who has accomplished many things successfully and is willing to learn more.” A good analyst should not rest on his laurels; He or she must strive to become better either in data, tools, presentation styles, communication, etc. The entire world of data analytics is very dynamic and changes often. Hence, to stand out from the crowd, you have to keep developing yourself and building capabilities in terms of technical skills.
A good analyst should never be content with doing the same things the same way every time. They should aim to select the right tool for the job rather than relying on their go-to tools and making them work for every situation.
A good analyst should not be afraid of making mistakes. Once you make mistakes, admit them and learn from them. Experience is what you get when you don’t get it right the first time. If you are not afraid of making mistakes, it will help to try a new method or a new challenge that will be part of your experience. Remember, this habit can be developed for an analyst who wants to become better in his field or a business manager who is looking to have a group of good analysts on his or her team.
Finally, a good analyst must be able to discern when good is good enough when presenting a data analytics project. For example, if the solution you provided during the project is already 80% satisfactory.
If trying to add the remaining 20% will cost you an additional 50% of your time or energy and resources, a good data analyst should be able to decide to hand over only the 80% because it is good enough, thus reinvesting any additional savings or time on other project priorities.