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What Does a Data Analyst Do?

By Will Ellis
Last Updated on November 16, 2023
Data Analyst

Firms in all industries increasingly depend on data to make key business decisions like the chances of a cybersecurity hiring boom in Australia – new products to create, new markets to expand to, new investment opportunities, and the best (new or current) leads to focus on. 

They also use data to pinpoint suboptimal processes and other business issues that should be handled.  

In these companies, the task of data analysts is to rank how crucial each of these key business capabilities are to performance across time versus one another.

But the role involves more than just assigning numbers: An analyst also must dig deep into how data can be handled to let organisations make better-informed choices.  

These roles are highly sought after. Entry-level data analysts get an average annual salary starting from £45,000. If you make the necessary accomplishments, this starting position can grow to senior roles that climb to over £100,000.

If the job of a data analyst sounds ideal for you, here’s an overview of: What does a data analyst do?

Table of Contents:

First, What is Analytics? 🤖

Analytics amalgamates practice and theory in order to give executives, managers and stakeholders in organisations better ways of making decisions. To do this, insights are mined from data. The more experienced the data analyst is, the broader their organisational context – in short, they’re going to review more external factors.

Business analysts also focus heavily on competitive factors, both internal and external to the business’ interests, in order to make data-driven recommendations to key stakeholders. A Master of Professional Studies in Analytics trains students for careers in data analytics by going through theoretic hall concepts for probability, visualisation, statistical models, risk management, and predictive data is applied to the business or government landscape. 

Elon surely used this when considering how buying Twitter shares might affect the social culture. 

Whereas masters degrees in analytics prepare students for various practical languages of databases and programming including software programming. 

What Does a Data Analyst Do? ⚙️ According to Type

We can cover four main types of data analysts based on how they provide value to companies 

☑️ Descriptive analysts evaluate historical data: these are things like financials for website traffic, monthly income, quarterly earnings, and so on. They’re looking for trends that can help the organisation understand its performance. 

☑️ Diagnostic analysts then look to figure out the reasons behind why the organisation is performing the way it is by making comparisons with descriptive findings to other patterns and dependencies. Which can help the organisation to pinpoint reasons for good performance or poor performance. 

☑️ Predictive analysts look to the probability of results by spotting tendencies in the two above types of data. This is focused on the organisation to take assertive action – such as knowing which customers are the most likely to not renew their subscription, for instance. 

☑️ Prescriptive analysts specify the exact action that the organisation should take. This type of data is usually the most important for spotting industry trends or hazards but actually depends a lot on complicated algorithms and technology such as machine learning.

Can you guess which type of data analysts would have been involved in figuring out that 30% of businesses had to close down, as a result of Omicron ‘shadow lockdowns’ in Australia? 

Another interesting fact is that a 2016 survey concluded that descriptive analytics were not adequately up to the task – after reviewing 2,000 different businesses. For this reason, both predictive and diagnostic analysts are increasingly being sought after (this is a clue to the question above by the way – wink wink). 

What Does a Data Analyst Do? Key Responsibilities

Let’s answer another question: “What does a data analyst do?” which itself will depend on the nature of the business or organisation and how proactive data-driven its decision-making needs to be. But generally, the main role of data analysts includes some of the following:

  • Formulating and updating databases and data networks; which includes troubleshooting programming errors and other data issues. 
  • Investigating the quality of data from first hand and external sources, then sorting this into a format that is easily understandable either by a human team or machines. 
  • Evaluating datasets using statistical tools, keeping an eye on key trends and data patterns that could be useful for predictive analytics and diagnostics later on. 
  • Proving the value of their efforts on the scale skill of worldwide, national, and even local levels; they are looking for trends that could both affect sectors and the business. 
  • Making reports that the executive team can use to easily understand patterns, forecasts and trends according to pertinent data.
  • Teaming up with engineers, programmers, and business directors in order to figure out areas where improvements, network wide changes, and data management policies are important.
  • Building key documents such as white papers so that stakeholders and investors can quickly see a roadmap of how the analytic process was done, so that they can themselves clearly replicate the steps if required.

Can you guess which type of data analyst would have been the most important to Netflix after Netflix dropped 39% in value at the beginning of the year?

If you said each of them, you could be correct.

What Does a Data Analyst Do? Data vs. Science vs. Business Analysis

S get the difference between what a business analyst, data scientist, and data analyst do by going over each of these three roles. 

A data analyst acts as the vanguard of a business’s organisational data so the executive team and key stakeholders are always ready to use it to make strategic business moves. This technical role has as a prerequisite an undergraduate degree or masters in either math, science, computer modelling, or analytics. 

A business analyst has a more proactive role focused on strategy; they take the information gathered by the data analyst and user in order to make proposals for upcoming issues and solutions. Those in this role usually have a background in finance, economics, or business administration. 

While a data scientist delves deeper into the information and data visuals created by the data analyst in order to search out deeper threats, patterns, or opportunities for the business. People with this role have backgrounds in computer science or mathematics and have a little bit of knowledge of psychology or human behaviour which helps them to make predictions. 

Nevertheless, when it comes to smaller start-ups and companies, you often find data analysts who are doing a bunch of the predictive model or executive decision-making aspects of this process which would otherwise be handled by a data scientist.   

What Do Data Analysts Make? 💵️

On average, a data analyst makes anywhere from £45,000 to £110,000. But the highest salaries for data analysts are usually with technology and financial companies.

Keep in mind that data analysts have a clear career ladder that leads to more senior data reliant work. For instance, PayScale says that data analysts usually end up as senior data analysts, or they will become business analysts, data scientists, or analytics managers. 

Each of the stepping stones comes with significant boosts in salary. Which, based on IBM’s estimates, will end up as an average salary for data scientists at almost £75,000. By comparison, this is a bit over £80,000 for analytics managers yearly.

Paths to becoming a data analyst?

The following skills are the first steps to becoming a data analyst. These are not set in stone, but they provide a flexible roadmap that can be adjusted to fit different circumstances:

  • Professional certification: In order to get entry-level data analytic work, you can often begin without any pre-existing experience by first getting certified in the specific skill. For instance, these can teach you basic analytical skills such as statistics or SQL for databases – meanwhile, giving you a portfolio that you create in the process of earning the certification, as well as giving you feedback. A couple of professional certified programs on Coursera do this. 
  • Undergraduate degree: Some people recommend that you go through the traditional route of getting a bachelor’s degree if you intend to do work that involves data analysis. If you use this pathway in order to become a data analyst, supposedly it’s important to keep your coursework focused on math, computer science, or statistics in order to build a pre-existing portfolio to show future employers. 
  • Self-learning: If you want to avoid formal training that could cost thousands of pounds, then you could do self-study. It is possible to acquire the skills required for data analytics by yourself. It will also be important to create a functional portfolio. As you can combine this with hands-on work as a freelancer.

Personality Traits of a Great Data Analyst

Engaged – this is the most crucial trait for any kind of work. If you don’t have a real passion for the field, it will be difficult to put in superhuman levels of practice into the thing. There’s some dispute on this however. Some people, such as researcher Cal Newport, believe that practice itself is the thing that automatically builds natural interest in whatever you are studying.

Curiosity – once again, you may have rudimentary interest in a topic, but do you have enough to really dig deep into data? Many of the senior levels of data analytics don’t just want to figure out that something is 25%. They want to know why that 25% is 25%. 

Self-motivation – how proactive is your analysis? Are you waiting for your manager to ask, “Why?”.  Or do you really have the drive to search out patterns behind why things are occurring? 

Blank slate – going into data analytics with a preconceived notion to close your mind to what is really happening. The opposite approach to this is being adjustable to what will happen next based on what you find in real time. 

Creativity – in other words, not just being able to imagine ways of visualising data and processes, but also approaches to analysing data. This involves questioning what happens if one approach is taken, compared to another. And taking risks to break data sets apart in new ways. 

Mistrusting – another way of saying that you don’t automatically trust that your data is accurate. There is no dataset that is completely flawless and so can be to your advantage to take a step back and review it. What a look at the bigger picture of it all, doing your due diligence, and following the scientific process dispassionately.

Discernment – another word for common sense. Interfere out valuable data from invaluable data. National data analysts can be predisposed to take forever to go through datasets, forgetting about how much time is running down the clock. You have to filter out the gold from the dross quickly. 

Systematic – taking a methodical approach, using internal checklists, making sure that you have applied an airtight system to how you evaluate a data set – to avoid inaccurate or incomplete data.

Pattern detection – something of an inherent ability as well as something you can train to a degree. Sometimes these trends do not emerge until you put things into a visual format. But this component is one reason why a lot of employers look for prospective hires who have a background in mathematics. 

Analytical – engineers fall into this category. You need to be able to take datasets, or numbers and to strip them down to separate parts in order to understand how they all piece together. For instance, look past the fact that 75% of customers purchase the product in the last 30 days. See what proportion of that 75% were male or female. And how old are they? Do they have a family? What type of work do they do? And so on. 

Reverse engineering – piecing things back together, in order to tell a story that is accurate. These different data sets, themes, and trends need to come together in a way that is useful to the market research team and stakeholders. For instance, what was the public response to Elon’s Twitter takeover bid?

What Does a Data Analyst Do – FAQs 📚

Is Data Analytics a Good Job?

Sure, if it suits your personality. Data analysts are in high demand and paid relatively well. If problem-solving, analytical thinking, playing with numbers and so on, then working as a data analyst could be a good fit. 

What Should You Study to Become a Data Analyst?‎

Most of the entry-level data analytic jobs asked for at least an undergraduate degree. A couple of feels that a good match are finance, mathematics, economics, computer science, or data analysis. And as for masters degrees, pursuing one in business ethics, data science, or data analysis can open up the door to better paid work in this field. 

Do New Programming for Data Analysis?‎

Not necessarily, for your daily tasks as data analysis. Nevertheless, knowing how to program some basic R or Python, as well as being able to work with databases to write SQL (Structured Query Language) queries can be immensely helpful in helping you to manage, clean up, visualise, and analyse data. 

How Difficult Is It to Become a Data Analyst?

The trickiest part of the job are the statistical, mathematical, and data analytical components. But with consistency in your plan of attack, all of these can be achieved with time.

What Are the Chances of Getting a Job As a Data Analyst Without Experience?

The reality is there even some junior data analyst jobs will ask you for the pre-existing experience. Nevertheless, it’s pretty straightforward to get some basic experience by getting certified. This can be done through online courses, certification courses, or degree programs. Each of these should give you a working portfolio in the process of completing the data analytics course. 

For those doing self-study, there are a bunch of free datasets online that you can begin working with in order to build up some basic experience (and a portfolio). 

How Long Does It Take to Become a Data Analyst?‎

This is dependent on your capacity for learning, the amount of time you invest, and your specific roadmap for pursuing a career as a data analyst. Overall, this may not be as extreme as you might think. In fact, according to the 2021 Global Skills Report published by Coursera, it took on average 64 hours of study for students to develop enough skills to get their first entry-level position as a data analyst. While the IBM Data Analyst Professional Certificate and Google Data Analytics can both be accomplished in under six months.

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