Interpret data to guide decision-making and solve business problems.
Data Analysts draw meaningful insights from complex datasets with the goal of making better decisions. Data Analysts work wherever an organisation has data - these days that could be in any function, such as product, sales, marketing, HR, operations and more. Data Analysts will normally be well skilled in querying relational databases using SQL, wrangling & cleaning datasets, conducting analysis and creating simple to read reports & visualisations for the end decision makers. They’ll commonly use a programming language like Python or R to conduct their analysis, and also BI tools like Excel, Tableau, PowerBI and Looker.
Data Analysts are increasingly common positions in modern companies. The digitization of so many industries has seen an explosion in the supply of data generated, creating an enormous opportunity to make better, data-informed decisions. With it, companies then have had to hire people to make sense of the data.
Data Analysts are one of the core roles in any data team. The scope of the role depends quite a bit on the size & industry of the company, and their relative data maturity.
An Intern Data Analyst is a student or recent graduate who supports the data analysis efforts of an organization. They assist in data collection, cleaning, and basic analysis tasks, gaining practical experience in data analytics and contributing to the organization's data-driven decision-making process.
A Graduate Data Analyst brings fresh perspectives to data interpretation, utilizing foundational skills in statistics and data visualization to translate complex datasets into actionable insights. They are detail-oriented, analytically minded, and poised to support data-driven decision-making.
A Junior Data Analyst is an entry-level professional who supports data analysis activities within an organization. They assist in data collection, cleaning, and analysis, while also gaining exposure to various data analysis tools and techniques. Junior Data Analysts are eager to learn and contribute to the organization's data-driven decision-making process.
A Mid-Level Data Analyst plays a crucial role in transforming data into meaningful insights that drive business decisions. With a solid foundation in data analysis and strong technical skills, they collaborate with teams to gather and analyze data, create reports, and provide actionable recommendations. Their expertise helps organizations leverage data to optimize performance and achieve strategic goals.
A Senior Data Analyst is a seasoned professional who turns complex data into actionable insights that inform strategic decisions. They lead analytical projects, mentor junior analysts, and translate data trends to guide business strategies. Their expertise ensures that data narratives are clear and impactful for driving organizational success.
A Lead Data Analyst is a seasoned professional who not only possesses advanced data analysis skills but also provides strategic guidance and leadership to a team of analysts. They are responsible for overseeing complex analytical projects, driving data-driven decision-making, and ensuring the accuracy and integrity of data. With their expertise, they play a crucial role in shaping the organization's data strategy and driving business success.
The duties and responsibilities of a Data Analyst do vary from role to role, industry to industry, company to company. It also depends on how senior or junior the role is. That said, these are the typical responsibilities of a Data Analyst:
Ultimately, the end goal is normally to generate ‘actionable insights’ - this is sometimes called the ‘so-what’ of the analysis. The analysis is considered valuable to the extent that it’s insightful (i.e. telling the audience something that they didn’t know) and ‘actionable’ (can the audience actually do anything with this insight, or is it just more of an academic curiosity).
Just like the responsibilities and duties, the required skills & experiences do vary from role to role. Here’s a typical set of requirements for a Data Analyst:
Titling in analytics is sometimes a little blurry. What one company calls a data scientist, another may call a data analyst, for example. That said, there has been a bit more consistency in titles over the last few years as the data industry has matured.
Depending on the organization, a Data Analyst may also be referred to as an Insights Analyst or a Data & Reporting Analyst.
A title is just a title, and for candidates, we’d recommend reading the job description and asking the hiring manager for details of what you’ll actually be doing to understand the role.
For organizations, we’d recommend aligning your job titles with what the market generally uses. E.g. over-selling a basic reporting role as a Data Analyst will lead to disappointment among candidates, and cause you to attract the wrong types of candidates.
A day-in-the-life of a Data Analyst of course would vary a lot from team to team and organization to organization. Each organization has different datasets, work at different cadences with different methodologies, and have different technology set-ups.
But as a general guide, here’s what you might expect in a typical day:
The scope of a Data Analyst role does vary a little bit by the size of the company, the industry, and the data maturity of the organization. This then dictates the skills that are (or are not) needed to be a Data Analyst.
While generally companies don’t expect tool-specific experience, the caveat to this is contracting roles, that might only last for 3-12 months. For these roles, the expectation is that you can hit the ground running, and so the company would expect you to already have familiarity with their stack, which is fair enough.
For lower data maturity organizations, you should expect to see less well-established data ecosystems. This means most likely an incomprehensive data warehouse, probably with limited access tied up with an ‘IT team’, and a lot of analytics being done in Microsoft Excel.
That said, for a typical Data Analyst role, these skills would be considered mandatory:
SQL is typically considered a must-have for a Data Analyst. Note, there are various subtle differences in SQL syntax, depending on the relational database management system (RDMS) that an organization is using (e.g. SQL Server, MySQL, etc.). Normally companies don’t require experience in any specific one of these, just that you have experience in any of them, as they’re all very similar. The differences in SQL syntax is comparable to the difference between, say, American English and Australian English.
Data visualization skills are considered a must-have. Most commonly, this will be some experience in putting together dashboards and reports in something like Tableau, PowerBI, Looker etc. Companies are looking not just for experience in the mechanics of creating & maintaining visualizations, but also the general ability to communicate graphically. This is because the goal of analytics is to influence decisions, with visualization being a critical output of the analysis.
Python and R are the most commonly used programming languages as a Data Analyst.
Companies are generally agnostic as to which one a candidate would know well, as long as they know one of them. If you are a candidate and have to choose which to learn from scratch, it would probably be best to go for Python. It’s a general-purpose programming language so you can use it to do pretty much anything you like, while R is really made specifically for statistics and data science. The Python vs R debate is not something we’ll get into, but for what it’s worth, around 80% of candidates when taking tests on Alooba choose Python over R, when given the choice. The popularity has increased consistently over the last 5 years.
Surprise, surprise - a Data Analyst needs to have analytical skills. Included in here is a very keen attention to detail for all types of data. Your stakeholders will be relying on you to scrutinize the data with extreme prejudice and a healthy dose of skepticism. You ultimately have to arrive at the right answer through correct analysis and good assumptions and deal with the ambiguity involved in real-life analysis.
Common sense is also a big part of this - for example, if you check your dashboard in the morning and it shows that there have been no sales at all in the last 12 hours, before you go blasting the ‘All Staff’ mailing list telling everyone the company is dying, you’ll probably want to check that the data warehouse ETL process completed successfully (hint: the data probably just is not up-to-date).
The line between general analytical skills and what’s termed ‘data literacy’ is perhaps a little blurred. Data literacy would include being able to know when to use a median vs a mean, not to take an average of an average (please, just don’t), and why putting time on the X-axis of a line chart probably isn’t a great idea.
The goal of any data analysis is to influence a better decision to be made. As a Data Analyst, you could create the most amazing analysis of all time, but it will all be wasted if you aren’t able to easily describe the key takeaways from the analysis. You will typically need to communicate these to people with less technical skills than yourself, who are less familiar with the data than you are. They’ll often be more senior, time-poor, and might not want to get into the nitty-gritty details. Data Analysts need to be able to explain the ‘so-what’ of their analysis quickly & easily. This could be through visualizations, a written email, a presentation, or simply an informal chat.
Data Analysts are typically working in - or very close to - ‘the business’. Unlike Data Engineer roles that can be a little more abstract and disconnected, Data Analysts are right in the thick of it. There’s definitely an expectation that they’d be able to understand the key drivers of business success, understand what metrics should be tracked and why, understand underlying trends, interpret bigger-picture macro trends, etc. Maybe the best way to describe this skill is that they need to be able to think and act like an owner.
In addition to the above need-to-have skills for a Data Analyst, there’s also a lot of nice-to-have skills. Generally speaking, the smaller the organization you operate in, the wider the scope will be of your role, and so the more skills you’d be expected to have (or pick up).
Data modeling is basically the work of designing & maintaining data marts, datasets, views, etc. While data modeling may ultimately be reduced to some SQL, the modeling itself is distinct from this.
For larger organizations, data analysts are unlikely to be involved in building or maintaining datasets in the data warehouse. This would more commonly fall on a data engineer, business intelligence developer, or SQL developer. Nonetheless, the skillset is useful, especially as you will want to communicate with the person making a change to a data model and understand how it works.
E.g. Imagine you have a Tableau dashboard, and you’d like to create a new visualization that involves a new column not currently in your dataset. You’ll need to add this from the source, and understanding how the dataset is built will help you understand how this needs to be done.
Data analyst roles will often have machine learning - or in any case some kind of more advanced statistics - as a nice to have. If there is an expectation, it would normally be entry-level machine learning, such as regression models (linear or logistical), and some entry-level classification work. The expectation would be that you know how to implement, build & interpret basic models using some relevant package in R or Python. For the most part, though, the machine learning work would be done by data scientists, not data analysts.
Data Analysts will typically use these technologies on a daily basis:
There’s some crossover in each of these technologies in the analytics process. For example, a Data Analyst might do all their data wrangling in SQL, and then pipe the data into a Python environment for their analysis and visualization.
I was at WooliesX (Woolworths) and we used Alooba and it was a highly positive experience. We had a large number of candidates. At WooliesX, previously we were quite dependent on the designed test from the team leads. That was quite a manual process. We realised it would take too much time from us. The time saving is great. Even spending 15 minutes per candidate with a manual test would be huge - hours per week, but with Alooba we just see the numbers immediately.
Shen Liu, Logickube (Principal at Logickube)