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What Does a Data Analyst Do and How to Become a Successful Analyst

It’s estimated that by 2025, 463 exabytes of data will be created each day globally – that’s literally the same as 212,765,957 DVDs per day! This is a number that is higher than the number of stars in the observable universe. Hence, we must de-prioritize the relevance of data in our universe at our own peril. However, data by itself is just a cluster of numbers; it does not mean anything unless put in context and interpreted. This brings us to the question of what does a data analyst do? After all, data analysis is one of the hottest jobs in 2022. Quite simply put, data analysts make sense of a massive quantum of data to help businesses make informed decisions that drive growth and profitability. Sometimes, a team of analysts can be the difference between a business surviving a crisis or shutting shop!

Now, before we go further into what a data analyst’s job profile and career path comprise, let’s first understand what data analysis is, which stands at the core of this field. 

What is Data Analysis?

Hubspot defines data analysis as:

A process which transforms raw data into information that is foundational to future growth.

There are different kinds of data analysis that involve different types of data analysts. According Towards Data Science, these are the six main types of data analysis done by analysts:

  1. Exploratory: If you are doing exploratory research, you collect data and then arrive at a question or problem based on what you see. 
  2. Descriptive: The goal of descriptive analysis is to collect data, analyze it, and describe the results.
  3. Inferential: This type of analysis uses a small sample of data to make inferences about the larger data sets of similar kind. 
  4. Predictive: This type of data analysis, historical data is used to draw patterns or make predictions about the future trends.
  5. Causal: In this kind of analysis, cause-effect relationships are determined. It can be used for solving problems within a system. 
  6. Mechanistic: This is a complex kind of data analysis where a change in one variable leads to changes in one or more variables that may be nonlinear in nature. 

One must also rule out errors in data analysis like data bias which comes in when you choose data that supports a preconceived hypothesis while ignoring data that doesn’t support this hypothesis. So, if you are wondering, what does a data analyst do, then this is the gist of it. 

You can read more about the process of data analysis here. But, if you are pursuing a data analyst role as a career path, then you can explore our breakdown of this profile into skills, education, responsibilities etc. 

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What does a data analyst do: Common data analyst roles and responsibilities

As data analysts are required to work in many different kinds of companies, industries, business sectors, some of their specific roles and responsibilities may vary on a pro-rata basis. However, the core answer to the question, what does a data analyst do, remains the same. Hence, we have put down the most basic and critical tasks that data analysts are required to perform on any given day.

1: Data Mining

This is, essentially, the process of collecting data from primary, secondary, and tertiary sources and arranging them into data sets. Data mining is a meticulous process that requires sampling skills and attention to detail.

2: Data Interpretation

In this step, data analysts use an array of statistical techniques and tools to interpret the data they have collected. This is the point at which the raw data is structured. 

3: Context Setting

Data analysts have to look at the data in context to the local, regional, or worldwide scenario. Hence, they have to always be aware of market trends. Data is just numbers taken out of context. 

4: Reporting Insights

Once the data is interpreted and set in context, insights emerge from it. It is the responsibility of data analysts to present these to the team concerned. 

5: Data Visualization

Humans learned to work with shapes and colors much before they learned numbers. So even today, our primordial brain can read images and pictorial representations faster than numbers. Hence, it is not enough to present the finding of an analysis as is; it has to be visualized properly using histograms, bar graphs, pie charts etc. so that nothing is lost in translation. 

6: Data Systems

When analysis has been done on a regular basis, data analysts often set up a system to collect data routinely. Setting up these routine data systems is a part of the data analyst’s responsibility. Also, data analysts usually agree on an attribution model for the data so that it can be sorted into categories based on source. 

Technical skills that will get you hired as a data analyst

Visualization and Storytelling Using DataData analyst as a role does not have stringent requirements in terms of educational degrees. Analysts often come from all kinds of educational backgrounds. The job market for data analysts is open to this varied menagerie of candidates provided they have the right skills. In essence, a data analyst’s role is very skill-driven. That brings us to the obvious next question, what are the data analyst skills that will get you hired?

The following are the top skills that make data analysts employable:

1: Data Visualization

As we have explained in the section about a data analyst’s roles and responsibilities, data visualization is a process of presenting findings of a data analysis with graphics. This is a very important skill for data analysts as they have to present complex business insights in the easiest way possible to the business teams. You might have found gold, but the gold cannot be mined if no one understands your map!

2: Data Cleaning

The hygiene or cleanliness of data used in the analysis is the analyst’s responsibility. What does a data analyst do with raw data? He/she cleans it up and picks the right sample from it. Data collected from different sources will have discrepancies. Also, sometimes there might be a problem with data collection as well. In addition, this data needs to be free of biases. Thus, data analysts need to know how to arrive at the cleanest ‘source of truth’ when it comes to data. 

3: Technological Skills: MATLAB, R, Python as well as SQL and NoSQL

Data analysts need a basic understanding of programming languages like MATLAB, Python, and R to extract and interpret data. SQL and NoSQL are programming languages that help data analysts to skim through large databases quickly. 

4: Machine Learning

What does a data analyst do with Machine Learning knowledge? He/she uses it to clean data and glean useful insights from it. Analysts typically do not need in-depth knowledge of Machine Learning. A basic understanding of the same suffices for their roles. 

5: Linear Algebra and Calculus

Advanced skills in mathematics is a good-to-have for data analysts. Algebra helps them understand the inter-relationship between variables. They need to be able to understand formulae and derive usable equations from the data they analyze. Calculus, on the other hand, helps to understand correlations in objective/ cost/ price etc and have a deeper knowledge of algorithms.  

6: Microsoft Excel

Most of us do not know the extent to which an Excel workbook can be used as an analysis tool. The things that you can do with Excel including linear regressions, propensity models etc are limitless. And as a data analyst, you have to be a pro at using this platform with all its formulae and functions. 

7: Soft Skills: Critical Thinking and Communication

But just doing the math will not suffice for a data analyst. They have to communicate with marketing, sales, leadership and other business teams within an organization. In fact, using critical thinking to inform business decisions and conveying the same to the teams that will action these insights is 40% of their job. 

Data analysts vs. data engineers vs. data scientists: What is the difference?

Data analytics or science is a rather hot topic of discussion on the internet and off it. So, we have all heard of the terms data analyst, data engineer and data scientist used in similar contexts, sometimes interchangeably. However, there is a fine line of difference among them. And it this difference is presented in the table below:

Data Engineer Data Analyst Data Scientist
Scope of Work Builds and optimizes database systems Analyses the data  from the database for insights Solves future business problems using data 
Skills Required Programming, cloud computing and Big Data Domain knowledge, data visualization, and basic SQL tools Statistics, mathematics, programming and Big Data
Impact Area Supports the other two functions  Informs business decisions Solves business problems

As is clear from this comparison, all three roles are geared to solve the same problem. However, their scope of work and approach to the problem is different. For instance, if data science is an assembly line and data is running on it, here is what each of the roles will do.

  1. The data engineers source the data and place it as items on on the assembly line
  2. Data analysts will tag them with unique identification numbers so that they can be sorted and studied
  3. Finally, data scientists will study the patterns of different data sets on the assembly line by taking a holistic approach to the same. 

What are the tools that data analysts use?

Data analysts use a host of business intelligence and statistical tools to do their job of managing huge chunks of data. There are several such tools of varying price, efficacy, and precision available online. Like most tools available online, some of these are free while others offer advanced paid versions. The decision to choose between them depends on the scope of work and organization’s allotted budget. You can explore and compare the top 24 data analysis tools here. 

To know more about data analysis tools and gain the right skills for a data analyst, you can explore data analytics courses on Emeritus.

By Anwesha Barari

For feedback or collaboration, write to us at content@emeritus.org

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