Today, data analysts are undoubtedly one of the most sought after professionals for any business looking to reel in more profit.
In fact, even at a time when job losses are being recorded in droves and company job advertisements are being retracted, COVID-19 hasn’t managed to put a damper on the need for data analysts.
Any business that wants to stay afloat and swim against the tide of this pandemic must know how to capitalize on what’s happening inside their business to make informed decisions.
That said, while companies do know that they need someone who can break down data in a simple and understandable format, some businesses end up lumping different types of professionals within data analysis in the same bunch, often assigning them roles and responsibilities that don’t coincide with their specific skill set.
In this blog post, I’m going to dissect the type of roles and responsibilities that different data analysts undertake in companies and how they are different from other types of similar professions.
Let’s get right into it.
What Is A Data Analyst?
A data analyst is a professional in data analysis who bears the responsibility of gathering, organizing, analyzing, and simplifying batches of data in order to make conclusions about them.
In effect, they scrutinize data in order to understand how it impacts occurrences within a company.
To scrutinize data, an analyst may use either one of four data analysis processes:
- Diagnostic analytics: A diagnostic analytical procedure simply refers to identifying the elements which provoked an event to take place. In essence, the data analyst’s job isn’t centered specifically around describing a particular occurrence, rather, their goal is to figure out what were the catalysts that incited a particular course of action.
- Prescriptive analytics: With prescriptive analysis, a problem has already been identified by the business. It is the analyst’s job to use data to determine a solution to this problem or a way to capitalize on an opportunity that may present itself in the market.
- Descriptive analytics: As the name implies, descriptive analytics is about describing the factors which have led to a particular event. Unlike diagnostic analytics, the focus is not so much on determining what took place, rather presenting an overview of things that occurred that lead to something in the present. This type of analysis is often used by businesses who would like a brief rundown of how their company has performed over a particular period of time.
- Predictive analytics: The focus of predictive analysis is on identifying possible occurrences in the future. To do this, the analyst reviews data to determine trends and tendencies in order to make predictions.
In order to apply these processes, data analysts usually have to combine solid communication skills, technical skills, analytical skills, a knack for decision-making, and general know-how for important specific skills in data collection and market research.
Data Analyst vs Business Analyst vs Data Scientist
Due to the overwhelming importance of data analytics in business operations as well as the various needs of a given company, it is not uncommon for recruiters to mix up specific data analysis profiles.
By that, I make specific reference to the tendency to mix up data analysts with other profiles such as business analysts and data scientists.
Despite the similarity between profiles, they are not synonymous roles.
Here’s a short breakdown of what business analysts and data scientists do:
- Business analysts: A business analyst is an integral professional within a company when it comes to defining business strategies. Unlike business analysts who seek to use data to describe, diagnose, and predict outcomes, business analysts leverage this data to determine the best way for a business to capitalize on this data for its growth. Consequently, since a business analyst assists in defining strategy, they tend to work directly with the executive members of a company by helping them finetune the most rewarding path that the business should take.
- Data scientists: Data scientists are a hybrid role that borrows a bit from both the expectations of a data analyst as well as those of a business analyst. Data science is a multidisciplinary field where one has to find and solve complex problems extracted from company data. On one hand, and in some ways like data analysts, they dig deep into company data to spot problems. On the other hand, like business analysts, they actively seek to find ways to solve the problems that they detect.
Needless to say, the role of a data scientist tends to blur the lines of data and business analysis combined. However, the fundamental difference that separates them from others is that they are problem-oriented. For that reason, certain processes associated with data analysis like diagnostic and descriptive analysis will not be leveraged by a data scientist. Similarly, a business analyst, unlike a data scientist, will not be in charge of the actual process of breaking down data as their responsibilities surround leveraging data that has already been analyzed and simplified in order to make strategic decisions.
Data Analyst Roles
Before diving into their responsibilities, it must be stated that there are three different types of data analysts: entry-level data analysts, mid-level analysts, and senior analysts.
As one can expect, the major difference separating these three types of analysts is the experience which each one has in data analysis.
For example, entry-level data analysts typically do not have prior formal experience in data analysis. Usually, they attain this position either based purely on their academic merit or through a promotion from an internship at a business that forms the beginning of their career path.
Most entry-level data analysts have at least a Master’s degree (in some cases even a Ph.D.), or at least a Bachelor’s degree in a related field such as Computer Science, Big Data, Statistics, or Business Intelligence.
On the other hand, mid-level entry data analysts are those who have already acquired some sort of experience in the field in either the same type of position or a similar one.
Lastly, senior data analysts are those who have amassed several years' worth of experience as a data analyst, leveraging various types of analytical processes to arrive at the conclusions that businesses expect of them.
It is important to separate these three subsets within data analysis as while the responsibilities they undertake are similar, the way in which job descriptions need to be drafted for each one differs slightly.
Without further ado, let’s get into the elements of a data analyst job description.
Data Analyst Job Description: Responsibilities
To create a complete list of all data analyst responsibilities is an impossible feat.
As you know, the exact tasks assigned to a data analyst will inevitably be modified based on the company that has hired them.
However, there are some common responsibilities that all data analysts share, irrespective of whether they are junior, mid-level, or senior in rank.
Let’s take a look at these responsibilities and how they differ slightly depending on the type of data analyst job description:
- Report Generation: All data analysts are supposed to generate reports: that’s a given. After all, they need to deliver the findings of their investigative research in a simple and accessible format so that all interested parties can go through what they have unearthed. In turn, this involves data visualization of complex data sets which implies that the analyst must use some sort of report generation software. That said, the skills that are required to generate reports will vary depending on the type of data analyst. Entry-level analysts may not be required to have formal experience or knowledge with these types of tools, although most should already leave school knowing how to use Microsoft Excel. However, the more senior an analyst gets, the more familiarity they are expected to have with tools such as HubSpot Marketing Analytics, Microsoft Power BI, and Xplenty, or some sort of data system, to name a few.
- Data management: It is standard for businesses to charge data analysts with the responsibility to develop effective data management practices. Depending on the business, this may even include preparing and carrying out investigative research such as surveys and questionnaires or handling machine learning software to prep a batch of data before it's analyzed. However, in most cases, it refers to leveraging technology tools such as cloud data management (Cloud SQL) as well as ETL and data integration software to organize and guarantee data security.
- Pattern recognition: It is a given that businesses require data analysts to display prowess in pattern recognition. Considering that their job involves sifting through data, to a company, it is important that anyone reviewing their data can spot tendencies in how the business has been performing over a period of time. In the case of junior data analysts, less emphasis is placed on the formal training they have had in being able to spot patterns as their academic background suggests that they are familiar with the procedure. However, with senior data analysts. There is a great expectancy on their part to prove with actual results how the patterns they spotted in previous organizations impacted positively on the company.
Data Analyst Roles & Responsibilities: Key Takeaways
All in all, the roles and responsibilities of a data analyst do not veer away from what businesses all across the world expect them to do: analyze data.
Needless to say, each one’s degree of experience and skill will mean that companies expect more from them in terms of their contribution to their business.
No matter how senior your job title may be, to assume that you are all-knowing when it comes to data analytics is a major farce.
It is your duty to actively look to increase your knowledge in key areas that businesses are looking at.
Pay attention to trends and tendencies in the market. If you observe that businesses are looking for particular skill sets be it related to problem-solving, process improvement, programming languages, or even technical skills, take the time to brush up on them.
Set yourself apart from the pack and you’ll be well on your way to securing more than the average salary that your fellow analysts are raking in.