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What Does a Data Analyst Do?

In a tech-driven world like today, businesses need to keep track of massive data volumes to drive valuable insights from them. 

Here’s where small and large enterprises need data analysts for efficient business processes. But what does a data analyst do?

Put simply, they help gather, clean, and interpret data sets to resolve issues or answer questions.  

However, it’s hard to define the precise role of data analysts in the 21st century because the career is lumped into various fields, including machine learning, AI, data science, and business analytics. 

A deeper insight into data analysts' day-to-day tasks and duties will help you discover what the role entails.

What Is Data Analytics? 

Data analytics intends to draw meaning from unorganised information. 

Data analysts explore the data patterns and relationships to derive helpful insights. 

However, you may question what relies on data. Pretty much everything!

You can divide data into two forms: quantitative and qualitative. The former includes numerical data, while the latter contains images, sounds, words, or anything you can interpret. 

A data analyst works with raw data for the most part. Raw data has no context and is almost meaningless. Therefore, someone who isn’t a data specialist cannot order the raw data because they won’t understand the context. 

Data analytics help bring order to chaos. The process gathers, cleans, and orders the data. Besides, effective data analytics implements programming, statistics, and various other techniques for an efficient outcome. 

Why Is It Important? 

Data analytics matters because it helps businesses improve their performance by identifying new opportunities. Another perk of data analytics is its evidence-based nature - companies do not need to base their decisions on instincts or opinions. 

Instead, information derived from data analytics helps them approach problems more scientifically. 

Note that data analytics isn’t always 100% accurate. However, it offers a reasonably reliable method to forecast future trends and deduce results based on past events. 

What’s more, data analytics can be applied to several applications. For instance, businesses can use it to anticipate future sales and crucial competitor data.

What Does a Data Analyst Do?

A data analyst is required to convert raw, disorganised data into meaningful, valuable data. Following this, they must answer specific questions or solve problems based on the information derived from the data. 

Later, they share this information with the company’s stakeholders to help them plan transformations and take action.

Data analysts also monitor the processes to collect and store data and set standards for data quality. 

One of the best ways to figure out what a data analyst does is to look at the job descriptions of data analysts. For instance, the below job duties were posted on Indeed.

  • Create and execute databases and collection systems 

  • Gather data from primary and secondary sources 

  • Work with the management to spot crucial metrics and KPIs

  • Clean the data 

  • Use tools to spot, analyse, and identify complicated data patterns and trends 

  • Create and customise data reports 

  • Develop and maintain reports about data models and infrastructure

  • Determine national and global trends that may affect the industry and the company 

  • Work closely with engineers, management, and programmers to spot improvement opportunities and plan data governance tactics.

  • Fix code errors and remove corrupted data 

Note that the list isn’t exhaustive, and the specific duties vary per organisation and the nature of your role. 

Besides, the above mentioned duties provide a high-level look at what a data analyst does. We need to dig deeper to learn more about the particular tasks associated with data analytics.

Day-to-Day Tasks of Data Analysts

As a data analyst, you’re responsible for keeping an eye on each step of the data analytics process. The keener your eye, the better you’ll do at identifying and resolving the problem. 

You can always switch to specialisation and take up a particular area as you advance your career. However, learning the primary data analyst duties as a newbie is important. 

How can you provide an answer without knowing the question? Understanding the problem or defining your objective is key to analysing the data and reaching a meaningful conclusion. 

Often, a problem that seems obvious isn’t so as you get to its core. For example, hypothetically speaking, you work for a business that wants to work on increasing its revenue. 

The company management is taking steps to launch new products and investing in sources to create top-notch items. However, after inquiring, you found that there isn’t a problem with the brand’s existing products. 

Instead, the company’s sales process is to blame. The inefficient sales process is causing customer dissatisfaction and increasing churn. It’s the same reason why the company fails to retain its existing customers. 

So, investing in the products won’t solve the issue, but working on sales training will retain existing customers, boost customer satisfaction, and, eventually, the company’s revenue. 

While we only painted a scenario, it explains the importance of probing a problem from various angles before investing time in the wrong place. 

However, ensure you understand a company’s demands to monitor the KPIs and metrics and take action accordingly. 

Gathering Data 

Once you define your objective, you must discover the right data to help you reach your goal. It can be qualitative or quantitative data. The latter may include competitor marketing figures, and the former may be customer feedback. 

You can also divide the data into different categories: 

  • First-party data. It is the data you directly collect from your customers. It may include social data, subscription data, or data collected from customer behaviour and actions. 

  • Second-party data. This requires you to collect the data from another company that owns it. You can consider it the first-party data of another organisation. Therefore, you’d need to pay to get it from them. 

  • Third-party data. Data analysts may purchase this data from third-party sources that aren’t original data collectors. 

If, however, you do not have sources to collect the data, you’d need to devise a plan to gather it. For instance, data analysts often conduct surveys, web analytics, interviews, transactional tracking, and social media monitoring. 

If you have a unique data collection method in your head, feel free to implement it. 

Once you have the data at your hand, it’s time to filter and clean it.  

Cleaning It 

The data you just collected is unordered and raw. Put simply; it isn’t organised. Therefore, you must clean it to organise it and check it for errors. Otherwise, you’ll fail to perform effective data analysis. 

Data analysts typically use tools and software to convert the raw data into a readable, comprehensible, and meaningful format. 

The automation tools quickly remove the errors and unwanted data (anything that doesn’t serve the purpose). Following this, they structure the data by filling the gaps and converting it into valuable datasets. 

At this point, data analysts check the data for accuracy and see whether it meets the required guidelines. If it doesn’t, they turn back to fix the errors. 

Therefore, data cleaning is repetitive because even the most skilled data analysts iterate the process to achieve accuracy. Note that the combination of data collection and cleaning process is termed data wrangling. 

Analysing It 

Once you have a clean dataset at your fingertips, it’s time to analyse it. There are several ways to analyse the data; the key is to find the best approach for you and the project at hand. The three primary types of data analysis include the following: 

  • Descriptive. Data analysts summarise the collected data to comprehend it better. This step doesn’t necessarily help draw conclusions. However, it is used to decide how to analyse the data further. 

  • Diagnostic. Here, you focus on comprehending why something happened. For instance, it helps spot the problem and is often used to define the objective (in the first step of data analytics) 

  • Predictive. It spots the trends based on historical data. 

  • Prescriptive. It decides future actions. 

Sharing Results 

Now that you’ve gathered, refined, and analysed the data, it’s time to communicate it to the management. However, you must present the data in a visually appealing format. For instance, consider creating a chart or a graph. 

You can also create documents, dashboards, presentations, or a report. While this step may go unnoticed, it is crucial to deliver the data right. In fact, properly presenting the data will complement all the hard work you’ve done this far. 

Plus, you might be sharing the data with a non-technical person. Therefore, handing out high-quality data in a readable format will help them understand your valuable insights.

Best Tools for Data Analysts 

Gone are the days when data analysts were bound to perform all the tasks manually. A wide set of software and tools help streamline and automate processes, boosting your productivity at work. Yes, you must be a little tech-savvy to understand the basics. 

However, understanding the in-depth processes would be less challenging once you've covered the fundamentals. 

We’ve curated a list of some popular tools data analysts use regularly. 

MS Excel is every data analyst’s best friend. It allows them to figure out the data and break it into smaller sets to comprehend it easily. The standard functions include pivot tables, XLOOKUP, and VLOOKUP. 

Further, they also use the AVERAGE function that provides an average of the given numbers. 

Plus, SUMIF allows you to calculate the sum of different cells. The key excel features combined with various software tools make it a commodity for beginner and pro data analysts. 


SQL is one of the most important tools for data analysts. It is a computer language designed to manage relational databases. Although relational databases are built in various languages, SQL offers a relatively easy method to comprehend the data. 

Its code is fairly simple to read and lets you edit and add data per your preferences. So, you do not need prior knowledge of the database language. 

Besides, SQL also enables you to gather data similar to Excel. However, it offers a way to simultaneously compile more extensive datasets across multiple tables. 


SAS (Statistical Analysis System) is another popular tool for data analysts. Although it was developed in the 1960s, the tool has significantly evolved. 

The software is powerful and user-friendly - a perfect fit for data analysts working in small and large organisations. It helps you mine data, report it, and conduct predictive modelling. 


Python is the most popular of all programming languages for the right reasons. Its straightforward syntax makes it easy to understand and learn. Further, it offers an extensive range of software libraries. 

You can use the language for various steps in data analytics. For instance, NumPy performs well when manipulating complicated mathematical operations. 

Further, Pandas is the right choice for handling qualitative data. You can pick from hundreds of Python packages to streamline your data analytic processes. 


Although R is more complicated than Python, it remains another popular choice among data analysts. Historically, it was used in statistical programming. Therefore, it offers advantages in a discipline like data analytics. 

Although it doesn’t provide image processing like Python, it offers several data analytics operations to help organise the data processes. CRAN, for instance, is a popular library software with helpful features for data analysts. You can always explore additional packages to know more. 

Microsoft Power BI 

Microsoft Power BI is a relatively newer tool for data analysts. Initially, it was introduced as an Excel plug-in. However, it was updated as a comprehensive set of corporate data analysis tools in 2010. 

It can be your go-to tool to generate visually appealing dashboards and reports. So, if you aren’t skilled at visualisation, Microsoft Power IB can be your saviour. 

Besides, its data connectivity is one of the most noticeable features. It is compatible with text files, Excel, SQL, and even Facebook and Google Analytics. 

Databases and Management Systems 

Data storing and management systems are continuously evolving, given the complicated datasets. You must know how databases work in the realm of data analytics. MySQL, for instance, is a common database management system used by many data analysts. 

You can also explore more complicated systems. However, they’ll have a complex framework for storing, handling, and processing the data. Therefore, starting with a basic system makes sense. You can gradually shift to complex data management systems. 


How Much Do Data Analysts Make? 

According to Indeed, the average salary of data analysts in Ireland is €49,000+. However, this varies depending on your experience, skill, and organisation. As a data analyst, your earning potential is more if you have more experience. Plus, a large firm is likely to pay higher than a smaller organisation. 

Is Being a Data Analyst Hard? 

The road to becoming a data analyst is certainly not easy. However, proper training, right mentorship, consistency, and dedication minimise the hurdles and make your journey smoother. 

Is Data Analyst a Stressful Job?

A data analyst job can be stressful only if you’re less passionate about it. Almost every other job is demanding, and the case isn’t any different with a data analyst career. However, a lot of stress is alleviated if you enjoy doing your job. So, choose a data analyst role if you’re enthusiastic about it.

Wrap Up

People in technical job positions often question, “What does a data analyst do?” A data analyst collects, organises, and analyses the data. Later, presents it to the firm to derive actionable results and tweak business strategies for the better. 

We explained the key duties of data analysts, the skills they need, and the basic tools they must be familiar with. Once you have the right skills, you’re a step away from entering the field. 

Further, you can take a Data Analytics Course by UCD Professional Academy to advance your career. The course teaches you fundamental and complicated data analytics concepts to demonstrate your skills in a professional environment and eventually switch to a more prominent role in the industry.