What Does A Data Analyst Do?

What Does A Data Analyst Do?


A data analyst is a person who is responsible for collecting, processing, and performing statistical analyses of data. He translates statistics and data into simple English language to be understood by different companies or organizations so they can make better work decisions.


Every business association collects data to let it be the research about the market, logistics, sales information, or transportation costs. A data analyst will take all of that data and process it to perform tasks that revolve around the financial decisions of the company.  He helps companies to make better work decisions, mainly related to cost. He also helps the organizations how to reduce the budget, how many employees do they required and how they can entitle a new price to the product.

According to a very famous writer Ms. McKenzie, successful data analysts possess all of these skills, which are:

  • Mathematical skills
  • Statistical skills
  • Analytical skills
  • Numerical skills
  • Technical skills


When theory and practice are brought together for identifying and communicating data-driven perceptions, it is called Analytics. It allows data analysts to inform executives, stakeholders, and managers in the organization, so they take more structured decisions. Data analysts with a certain amount of experience always consider external factors for the safe side and take a broader picture of their work within their organization. They are also accountable for making recommendations to the stakeholders in situations like; competitive environment, external and internal business interests, and the shortage of any data in the database.

Data analyst is considered to be an expert in professional studies in analytics. He has knowledge about the concepts of statistical modeling, probability theory, data visualization, risk management and predictive analytics of any business environment. He is well equipped with other talents, such as comprehensive information about the programming languages, coding, software programs and database languages that are significantly used in everyday work.  


Following are the four types of data analytics that built are on one another to bring collective worth to an organization:

  1. Descriptive analytics:

It is used to inspect the past, such as monthly, quarterly and yearly information regarding sales, revenue, and website traffic, and much more. This type of analytics helps in spotting trends.

  1. Diagnostic analytics:

Using diagnostic analytics, the data analysts identify dependencies and patterns by comparing descriptive data sets. These analytics compute the causes of good and bad outcomes in an organization.

  • Predictive analytics:

Predictive analytics is used to determine the probable consequences after the determination of different tendencies in descriptive and diagnostic analyses. It allows the organization to make practical decisions.

  1. Prescriptive analytics:

While identifying the type of business resolutions, prescriptive analytics comes into use.  This analytics allows staying ahead of industry trends, addressing the potential problems before they happen, and takes the help of machine learning or any other type of advanced technology and sophisticated algorithms.  

A survey was conducted by Consultancy PwC on more than 2000 business executives in 2016, where they established that descriptive analytics is considered to be insufficient for informed, data-driven decision making by many organizations. While diagnostic and predictive analytics can be used for critical decision making sometimes.


Data analysts use a combination of different tools to give adequate sense to their data. These tools are used to gather information and data from news sites, social sites, and even magazines. The collected data is then categorized, sorted out, and then visualized for making reports and presentations. Some standard tools used by data analysts are:

  • SQL
  • Tableau™
  • SAS® software
  • Microsoft Excel®
  • Google Analytics ™
  • Google AdWords™
  • Google Tag Manager


Data analysis is a highly valuable skill, and it can open new doors for a data analyst to get fantastic jobs both in the private and public sectors. Nowadays, almost every industry presumable is looking for a skilled data analyst such as in the fields of sales, finance, marketing, and healthcare, and even in the cyber world.  

Data analysts always need his computer to work, and they mostly work in teams. They can even work from home, remote office or even in some kind of practical office. They place of their work mostly depends on the type of data being gathered. Data analysts usually work as 9-5 employees; however, the duration of their work can be changed depending on the project deadlines and emergency. We have made a shortlist of typical organizations, companies, and employers who need data analysts:

  • Banks
  • Consultancies
  • Manufacturers
  • Social media specialists
  • Colleges and universities
  • Public sector organizations
  • Pharmaceutical companies
  • Telecommunications companies
  • Software development companies


Along with the personal skills, a data analyst must have these technical job skills as it will help him to get the best job possible. Following are some of those technical job skills required:

  • Data analysts must possess analytical skills, such as working with a hefty amount of data that includes number-crunching facts and figures. He must be able to see through the data and conclude results.
  • Data analysts should also possess technical communication skills that include the translation of data into comprehensible data.
  • He must be able to compute results on the basis of trends and patterns observed in the analysis.
  • Drawing out data from major and minor sources and organizing said data that can be read by both humans and machines easily is also a task of a data analyst.
  • He is required to interpret data sets, and for that, he must be able to use statistical tools, which will be valued for diagnostic and predictive analytics.
  • He must be able to demonstrate the significance of data analysis both in organization and industry related to all the local, national, and global trends.
  • A data analyst has to collaborate with engineers, programmers, and organizational heads to discover new chances for system modifications, process improvements, and development of policies for data integration.
  • Another responsibility of a data analyst is to keep stakeholders updated with every step of the work by creating appropriate documentation.


The salary of a data analyst depends upon his experience in the field and the type of data analyst he is. In the United States, the average annual salary for a professional data analyst was found to be $67,377. Other related fields, such as analytics data scientists, made an average salary of $72k and $117k, respectively. While an entry-level data analyst made $63k and data analyst intern made a total of $68k.


As we know that, a bachelor's degree is necessary for any entry-level job as a data analyst, while a master's degree will be extremely beneficial for upper-level jobs. Most of the data analysts will have a bachelor’s degree in fields like economics, mathematics, statistics or computer science, or any other field that is closely related to data analysis. Excellent analysis and math skills are very much appreciated.


For the role of a data analyst, the person must have some built-in personal skills to flourish in this field, such as:

  • Leadership skills
  • Attention to small details
  • Analytical mindset
  • Problem-solving abilities
  • Excellent communication skills
  • Ability to write meticulous reports


The job of data analyst may seem simple as it requires only collecting and analyzing the data to meet the customer's needs, identifying new sources of data, and finding methods to improve the analysis, but the competition is very tough as many people are trying to become a data analyst. Therefore, if you are going to pursue this field, we suggest you read thoroughly all the information we have provided. We also think that you should join a data science boot camp to gain more information and skills before stepping into the field.