A data analyst is a professional whose representatives process various data. The result of their activities is necessary for developing business, science, and other areas.

Often, data analysis refers to any profession in which information is processed. However, this approach is only partially correct, and there is a difference between a data analyst and, for example, a marketing analyst.

The essence of the work of a data analyst

The duties of a data analyst include collecting, processing, studying, and interpreting significant amounts of information. The results of his work are used as the basis for decision-making in various fields: business, management, science, and so on.

The data analyst position is mandatory for companies that use a data-driven approach. Its essence lies in developing an organization’s strategy based on a thorough study of data. Suppose a certain business structure is about to launch a new product. The management is not confident that it will be in demand and satisfy consumers’ expectations.

The company sets a corresponding task for the data analyst to minimize risks. He will have to study the target audience’s needs, analyze their behavior, find connections between various processes, conduct A and B tests, and build models to assess the consequences of launching a new product on the market.

Consumer data firm Statista says the world is experiencing exponential growth of information and predicts 181 zettabytes by 2025.

Navigating through this amount of data requires having the necessary knowledge and skills. This explains the demand in the labor market for specialists who can correctly select and investigate data and draw conclusions necessary for making the right decisions for businessmen, government authorities, scientists, and representatives of other fields of activity. Full-time data analysts exist in many large companies, such as banks and advertising holdings.

Such organizations prefer to spend money on a preliminary study of a particular issue, evaluate the prospects for each serious step, and only then make a balanced and informed decision. Small firms cannot afford the maintenance of such a specialist, but their cash flow is much less. Although many business projects could have avoided failure if their leaders were aware of the importance of the work of data analysts and would turn to them for help in a timely manner,

Data Analyst Tasks

Combining and structuring the data available to the customer, collecting missing information, carefully analyzing the entire amount of information received, and formulating conclusions that can be used in the company’s activities to make global decisions are the tasks facing the data analyst.

He uses Sublime Text, Jupyter Notebook, Google Sheets, or Excel in his work. By the way, many consider the last tool outdated, irrelevant, and unsuitable for working with big data.

Spreadsheet-based analytics in Excel is used by a huge number of companies around the world today, despite the ability to use the most advanced technologies. This choice is explained by the simplicity and availability of this tool, which is suitable for solving problems of any complexity. In Excel PivotTables, you can automate information processing, generate forecast sheets necessary for planning business processes, and build 3D maps.

Data analysts also use SQL databases to obtain point-to-point information about processes or customers through queries. Tableau, Power BI, and Looker Studio BI systems help specialists aggregate data from various sources. In addition, the analyst sometimes uses his knowledge of programming languages to set up automated table lookups, segmentation, or a pattern discovery process.

Imagine that a data analyst is tasked with transforming a video hosting service into a streaming platform. To do this, he needs:

• Collect data on user interaction with the video portal. For this purpose, surveys are organized, research is conducted, the information obtained is combined, and duplicate and invalid data are deleted along the way.
• Describe data models and use cases. At this stage, focus groups are assembled, data is processed, and scenarios of possible actions are formulated.
• Form proposals for architecture and data flow.

The result of the data analyst’s work is artifacts with finished results: graphs and tables. These materials are the basis for making this or that decision, but the specialist himself has nothing to do with this process. His area of responsibility includes only the preparation of information, based on which the company’s management will choose a further vector of development.

Pros and Cons of Being a Data Analyst

Let’s start with the benefits:

• High level of income. Experienced professionals can expect to earn more. For example, you can find a vacancy in a foreign company in the United States, where data analysts make more than 60 thousand dollars a year.
• Demand. The position of a data analyst is on the staff list of 45% of domestic companies, and the need for specialists is quite high.
• Ability to work remotely. The data analyst does not need to be present in the office. A laptop and Wi-Fi—that’s all the equipment he needs.
• Choice of a narrow niche If you want, you can focus on one type of data—marketing, gaming, product, and so on. This allows you to focus on the interesting direction and become a specialist who thoroughly understands the topic and therefore is more valuable to the employer.
• Belonging to IT. Today, this area is one of the most promising, and the management of IT companies, as a rule, adheres to Western labor organization standards. Excellent conditions are created for employees in the office; they can choose a convenient schedule and place to work.
• Easy entry into the profession. After studying online courses, a beginner needs two to three years to reach the middle level.

Flaws:

• The monotony of tasks From the data analyst, the same thing is always expected. After some time, the work becomes routine, which many get rid of by writing scripts. This allows you to shift monotonous processes to the machine.
• The high degree of responsibility. The analysis results become the basis for important company management decisions. The specialist must be confident in his conclusions to satisfy the customer.
• Moral exhaustion Like other representatives of mental professions, data analysts suffer from overexertion, burnout, and fatigue. As a rule, the cause of exhaustion is the incorrect distribution of work tasks, rush jobs, and a lack of days off.

Differences between a data analyst and similar professions

Applied analysis is the basis of several other specialties besides the one under consideration. We chose five similar professions for comparison in terms of tasks and sets of skills.

• Systems analyst. His responsibilities include formulating software requirements, drawing up a pool of tasks for developers, adjusting the company’s business processes, and solving the problem of implementing functionality from a technical point of view.
• Business analyst. This specialist determines customers’ needs and identifies the company’s current problems. After reviewing the collected information, he decides which should be integrated into the software to optimize the final product.
• Marketing analyst. His responsibilities include collecting information and further studying it to adjust the company’s marketing strategy.
• Data scientist. Like others on this list, he collects and analyzes data, using the results of his research to make predictions and determine the likelihood of their implementation.
• Product Analyst The main task of a representative of this profession is to study information about the behavior of consumers and their interactions with the company’s product. As a result of his opinion, management is taking steps to improve the situation.

At first glance, these specialists perform completely different functions. However, the basic skills of all these professions are similar: the ability to select the necessary data, compare, draw conclusions, and formulate recommendations for practical application. This allows you to change your specialization if you wish easily.

Data analyst requirements

The main hard skills for mastering this specialty are the following knowledge and skills:

• Fundamentals of Mathematical Statistics Most analysis methods are based on the laws of statistics. To draw the right conclusions, it is necessary to use the tools inherent in this science: calculate the mean or median, cut off outliers, and test hypotheses.
• Ability to develop programs for data analysis. As a rule, Python is used for this: its simple and logical syntax and sufficient ready-made libraries allow you to assemble the necessary software from the available blocks and functions.
• Familiarization with the principles of operation of relational (tabular) databases. They most often serve as a place to store arrays of information. To extract information from such sources, you need knowledge of the SQL language and the ability to write queries.

In addition to these technical skills, some character traits will help you become a good data analyst. The list of soft skills looks like this:

• The desire to get to the bottom of the truth could be better when a data analyst does his work indifferently without delving into the essence of the problem. The results of such an analysis can be useless or even harmful to the business.
• Out-of-the-box thinking In many IT specialties, the desire of an employee to deviate from the template, put forward hypotheses that seem strange at first glance, and try something completely new is appreciated.
• Ready for bold decisions. It is not enough to put forward an idea; one must be ready to test it, despite its seeming absurdity, without fear of colleague ridicule.
• Ability to formulate questions correctly. The final result of the work largely depends on how exactly you will search for the necessary information.

Levels of data analyst development

Trainee data analyst

The requirements described in the previous section are the minimum requirements for applicants applying for a data analyst position. As a rule, these are university graduates who do not have specialized experience but, at the same time, possess the necessary knowledge base and relevant human qualities.

To some, it may seem that too much is expected of a beginner. This is basic knowledge without which it won’t be easy to become a professional in data analytics.

In addition, for a three-month internship, the applicant becomes a ward, requiring constant attention from the mentor and management. Companies are not ready to spend precious time on someone who is not ready to perceive a large amount of information and is incapable of performing elementary independent actions.

Factors favoring the candidacy’s approval are the profile’s university education, way of thinking, and programming experience.

The trainee is entrusted with performing formalized tasks assigned to him by a senior analyst or head of the department. The mentor checks the result before the data is sent for further processing. The trainee is not required to comprehend the collected information, visualize it, or make predictions. To begin with, he must consolidate the skill of searching and selecting data for further research.

Junior Data Analyst

Having mastered the basic data processing tools, the trainee moves to the junior analyst position. At this stage, the employee can already transform the collected data into the required form. His workflows could be better, and solving simple tasks takes him much more time than it does for experienced data analysts.

A junior analyst differs from a trainee in the ability to determine the degree of reliability of data. He can understand whether the collected information corresponds to its nature and check it to ensure he is right. For example, he can figure out whether the indicators are within the acceptable range of values, whether they reflect the real picture, and whether any suspicious outliers will distort the analysis result.

Lack of experience in business and a real product dictates to a junior analyst a special procedure for solving the tasks assigned to him:

• Need to upload an Excel spreadsheet with multiple columns? – Please.
• Need to make a dashboard? Show which one you need.

In other words, the junior analyst acts according to a detailed algorithm that specifies what data to use, how to transform it, and in what form the analysis result should be presented.

A junior analyst can already receive tasks directly from the customer, but the best option would be to work under the supervision of a manager.

Over time, a junior data analyst gains enough experience to avoid difficulties when working with familiar data and does not need a detailed explanation of how to solve the problem. The ability to collect and explore unfamiliar data indicates readiness to move to the next stage.

Analyst 1 (middle data analyst, 1 step)

At this stage of professional growth, the specialist confidently masters the skills of data selection, their transformation into the required form, and the initial analysis of the information received.

A Level 1 analyst can independently solve problems received from a team leader or directly from a customer.

The transition to this position occurs when the junior analyst feels strong enough to impact the business or product more. He has accumulated enough experience to act confidently and calmly, not being afraid to take responsibility for the result of his work.

For most tasks, he does not need outside help. But since the experience of such a specialist is limited to standard situations in which he acted according to a clear algorithm, he may need recalculations to the specifics of a particular business.

Typical cases that cause difficulties for first-level analysts:

• Lack of clear wording when setting goals
• The complexity and multifactorial nature of the business process
• A problematic customer, dealing with whom it is necessary to show attention and tact.

In the presence of one of these conditions, analyst 1 needs the support of a more experienced colleague who will help understand the essence of the problem, decompose it into components, and correctly present the work results so that they can be used in practice.

For a Level 1 analyst, workflows that develop according to an understandable algorithm are easy, for example, preparing and analyzing a standard A/B test. He is expected to answer a direct question: should changes be made or not? If the data analyst is not limited to an unambiguous answer but identifies problems, the correction of which will help improve the product, he is ready to move on to the next grade.

In addition to being proactive, for further professional growth, Analyst 1 needs strong time management skills, including setting expectations and predicting completion dates. In addition, he must be prepared to take personal responsibility for the results of the analysis presented to the customer.

Analyst 2 (middle data analyst, two steps)

At this stage, the specialist has all the necessary knowledge and skills to solve problems independently and help the business. Analyst-2 differs from the previous grade in richer experience and understanding of the context and having a high level of reflection.

Plunging to the product or part of the business being studied, the middle data analyst is not limited to formally presenting the analysis results. He puts forward ideas and proposes solutions on his initiative, bringing much more value to the business than in previous grades.

The high level of reflection inherent in Analyst 2 means his tendency to doubt and critically rethink information. This quality is inherent in all serious scientists, and for successful analysts, it is no less important than for representatives of fundamental science.

A Level 2 Analyst is ready to work independently with a product or line of business. If necessary, he will turn to the leader for help, but the situation must be really difficult. The chief analyst does not interfere in the daily routine, and problems of higher order are discussed at regular meetings.

Analyst-2 often becomes a mentor for interns or juniors. The skills of training and control, and delegation of authority are other important skills inherent in a professional at this stage.

Senior Data Analyst

A top-level specialist who influences the key indicators of the product or the business process for which he is responsible. The tasks to be solved are becoming increasingly difficult, and the immersion in the context is getting deeper. This stage is characterized by absolute autonomy and high activity by the data analyst. Only some of those who reached the previous grade can become such a Superman.

The value of a senior analyst for a business cannot be overestimated. Companies listen to such specialists’ suggestions and often receive positive changes that exceed their wildest expectations. If the management is in doubt, a caring senior will do everything possible to convey to the decision-makers the need for certain steps to optimize business processes.

To do this, a senior analyst needs well-developed communication skills, the ability to communicate with people, and the ability to choose intelligible information presentation methods.

The scope of responsibility of the senior analyst includes the independent determination of the company’s development goals, with which he agrees with the head. Their communication at regular meetings touches upon issues of strategy, recruitment, and the psychological comfort of employees.

Fields of activity data analysis

Medium and large businesses are interested in specialists with this profile. The purpose of a data analyst’s work is to increase a company’s efficiency and optimize its products or business processes.

Data analysts are required, and it is difficult for business structure management to make unambiguous decisions due to the huge array of information related to the product, customers, consumer behavior, competitor actions, etc. Such professionals are in demand in all areas where digital marketing is used: IT, retail, telecom, and healthcare. Data analysts help businesses understand where their money is spent and what needs to be changed to maximize profits at a minimum cost.

In some industries, data analysts play a particularly important role. For example, they are vital for banks, where a huge amount of information is used in the course of work—information about customers and their financial transactions. Any mistake in the banking sector can be very costly. For example, the incorrect development of a scoring model, that is, an algorithm that determines the creditworthiness of a person, can lead to a loss of funds and a deterioration in a financial institution’s reputation.