Despite the worldwide recession and massive job cuts that the COVID-19 pandemic has provoked, the hunt for entry-level data analysts is still very much on.
After all, it’s logical. In fact, for companies all around the world, there’s no better time to invest in a data analyst than now.
With the catastrophic impact of Coronavirus affecting just about every single business across the United States, either for better or for worse, it is imperative that companies understand how they can study the data their company is producing to make decisions moving forward.
And who better than to hire an entry-level data analyst, a professional who may not have the experience of their senior counterparts, but costs the company less while still coming with the possibility of growth within the organization.
Having said that, some industry heads either misjudge their company’s needs or aren’t well informed about the roles and responsibilities of entry-level data analysts.
If this is you, you’ve come to the right place.
In this article, I’ll clear the air about what entry-level data analysts really do and how job descriptions should be tailored to match those expectations.
Let’s get right into it.
What Is An Entry-Level Data Analyst?
An entry-level data analyst is a professional who bears the responsibility of examining and deconstructing complex company data sets based on a set criterion with the sole purpose of presenting their findings in a simplified format to the business’ interested parties.
As the job title suggests, an entry-level data analyst is one who does not have much prior work experience. However, despite that, the person brought on board as a data analyst has the responsibility of going through a company’s data in order to make conclusions about it so that the board of directors can make decisions about their business’ future.
In terms of the criteria that is used, data analysts can use either one of four data analysis processes:
- Predictive analytics: With this type of data process, the analyst’s goal is to forecast possibilities that may occur based on possible patterns that may present themselves in a batch of data.
- Diagnostic analytics: When it comes to diagnostic analysis, the analyst’s purpose is not to make any predictions about what may occur in the future. Rather, they are focused on identifying what were the agents that caused a chain-reaction behind a particular action.
- Descriptive analytics: The name says it all: descriptive analytics seeks to describe how a particular scenario at a given point in time. This is quite a common request for business leaders who wish to get an overview of how their company has been performing over a set of time.
- Prescriptive analytics: Unlike any of the previous types of data analysis processes, any type of prescriptive analysis seeks to find a solution to a given problem. In such cases, it is not the job of the data analyst to identify the problem. Rather, they are given a problem that they must solve.
Entry Level Data Analyst: How They Differ From The Rest
Despite the general consensus among companies that data analysis is crucial to their business operations, it is not uncommon for recruiters to confuse entry-level data analysts with other types of specific profiles within the niche.
This goes past a mix-up between drafting a job description for an entry-level data analyst versus that of a mid-level analyst. Instead, what I’ve seen is that recruiters are addressing their job postings to a data scientist when in fact what they want is a data analyst.
In all honesty, the difficulty is understandable as there are several professionals within data analysis who perform similar roles.
Let’s take a closer look at some of the more common ones and how they differ:
- Data analyst intern: A data analyst intern is a temporary staff member who has been brought into a company to offer assistance to its data analytics department. This job type cannot be considered as in the same league as that of entry-level staff members because interns do not enjoy the same compensation plans and benefits packages that regular staff members do. These types of interns are usually graduate students who have recently completed a Master’s degree in a field such as Computer Science, Big Data, Business Intelligence, Machine Learning Software, or Information Technology. Consequently, as part of their program, they are required to participate in internship programs.
- Business analyst: A business analyst is responsible for defining the strategic plan that will guide the company moving forward. They do not participate in data mining or data collection nor do they actively analyze batches of data. Instead, they use the conclusions that a data analyst has extracted from data analysis to help board directors develop a strategy for the business’ future.
- Data scientist: A professional who works within data science (also called a data engineer) is one who borrows from both the responsibilities of a data analyst as well as those of a business analyst. Essentially, they go through data that has been compiled by a data analyst to unearth possible plans that a business can implement.
That said, as has been mentioned previously, the natural career path of an entry-level data analyst usually begins at an internship.
Then, once a company is satisfied with how the intern has adapted to the work environment, they usually promote them from a part-time role to a full-time entry-level position.
Entry-Level, Mid-Level, and Senior Data Analysts
Prior to addressing the types of responsibilities that an entry-level data analyst undertakes, it’s important to highlight that these responsibilities are also assumed by senior types of data.
That said, there is a fundamental difference that separates entry-level analysts from mid-level and senior professionals: experience.
Needless to say, when drafting a data analyst job description, while responsibilities may be similar irrespective of the analyst’s rank, the language and wording used will vary.
For example, when drafting an entry-level data analyst job description, little-to-no emphasis will be placed throughout the posting on the need for previous work experience. Similarly, the responsibilities will not lean towards the need to know how to use data management software.
However, the more senior a data analyst becomes, the more businesses expect them to have dominated certain technical skills, demonstrating, in turn, high proficiency in select types of software.
Let’s take a closer look at how this can pan out when drafting up the responsibilities for an entry-level data analyst.
Entry-Level Data Analyst Job Description: Responsibilities
Just like all other job titles, it is nearly impossible to identify every single responsibility that an entry-level data analyst has.
Their day-to-day activities will naturally vary based on the company where they are employed as well as the specific type of data analysis that they are expected to perform.
Needless to say, despite the fact that tasks will vary, since this is an entry-level job description and, in turn, candidates are not expected to be dextrous in advanced data management software, drafting up one will be fairly standard.
Let’s examine some of the more common responsibilities that entry-level data analysts are expected to take charge of:
- Report Generation: As a data analyst, it is a given that you will have to generate reports. After all, the conclusions that you draw from your data mining efforts need to be delivered to the relevant personnel in a simple and easily accessible format. In the case of an entry-level data analyst, while job descriptions do not necessarily call for the need to know specific report generation tools such as Microsoft Power BI, Xplenty, Microsoft Excel, and HubSpot Marketing Analytics, they will underscore the need to be tech-savvy to handle data visualizations. In some cases, an entry-level data analyst job description may make mention of training that the company offers in report generation as part of their onboarding package.
- Data management: Another common responsibility that junior data analysts are expected to accomplish has to do with the management of data. This specifically refers to the storage, accessibility, and security of sensitive company data by way of special data management software such as Oracle RDBMS, MySQL, IBM DB2, and SQL Lite. As mentioned previously, these job descriptions will not explicitly state that the candidate needs to know how to use these tools as they are just junior-level data analysts. However, it may state that they will be given the training in these tools to complete it.
- Pattern recognition: Junior data analysts are required to exercise their fresh minds by applying the skills they would have learned at school in data analysis. This involves using analytical skills, technical skill, and problem-solving skills, among others, to spot tendencies in complex data sets and make conclusions about them. While previous experience in pattern recognition will not be required, data analysts may be asked to show proof that the program they studied instructed them on how to spot patterns in batches of data.
Entry-Level Data Analyst Roles & Responsibilities: Key Takeaways
By and large, a junior data analyst shares the same responsibilities and role within a company as that of their senior counterparts.
The main difference that you will see with their job descriptions is that less emphasis is placed on high experience levels.
Instead, job offers will make mention of any onboarding training that is offered by the company, in turn highlighting the types of software tools that the candidate will be taught how to use so that they can leverage them throughout their stay at the company.
In short, despite the lack of professional formal experience in the niche, entry-level data analysts are still sought after by several companies in the industry.
When reviewing applications from their job alerts, recruiters will place heavy value on the quality of your academic background, especially if the highest academic qualification you have is a Bachelor's degree. They will also evaluate your communication skills by way of how you have positioned yourself as the ideal candidate for the job.
However, even though your training will begin when you start working at the company, that doesn’t mean that you can’t start from now.
In your free time, make an effort to learn how to use the most in-demand software programs for data analysis or even the most common programming languages that are used. Try your best to master them before you begin offering your services to a given company.
Not only will you sharpen your competitive edge as an employee, but it will also show the hiring company that you are eager to learn and that for all they know, you may also very well have what it takes to make a meaningful impact on their business development.
Josh Fechter is the founder of The Product Company and a partner at Product Manager HQ.