Ever wondered what a Data Analyst does? It’s a career that is often misunderstood but reaps many employment benefits. One for the number crunchers, data lovers and math gurus.
To help you get a clearer understanding of the world of Data Analytics, we’ve gone and asked an expert about the ins and outs of this career.
Let’s get into it!
Felisa works as a Data & AI Consultant at Deloitte, where she works with client’s data, analytics, AI, machine learning and automation. She generates insights through identifying patterns and trends in data and presents these findings to clients in an accessible way.
How did you end up in this role?
Felisa first studied a Bachelor of International Business majoring in Marketing, but realised she wanted to work in tech and data consultancy. She then studied a Masters degree at the University of Sydney, specialising in Business Analytics and Business Information Systems.
“I did an Industry Placement Program in the team I currently work with, when I was undertaking my Master’s degree at the University of Sydney. That took place over 10 weeks and I was in my last Semester doing a full study load — then 2 months later I finished my degree and joined the firm as a full-time graduate,” Felisa said.
What made you want to work in this industry?
“I started off my career working in different industries, including Automotive, Electrical Manufacturing, Consumer Goods, and Higher Education. I realised that I thrive in an environment that is challenging, dynamic, is constantly evolving, and requires versatility and adaptation. That’s what made me want to work in this field,” she explained.
What is a Data Analyst?
Data Analysts take raw data and turn it into meaningful insights for clients. Often, they will create data models that offer ways to improve a business, manage finances or examine new systems.
Models are different recommendations and discoveries obtained from raw data that are relevant to clients. They can be presented in several ways, but usually make suggestions for how the company can improve.
Roles and Responsibilities
Felisa explained that in her typical day, she will obtain raw data from clients, before analysing this data and presenting it in a way that all stakeholders can understand.
Felisa and other Data Analysts have regular meetings with senior analysts to strategise about possible ways to improve models that are being worked on for clients.
Which industries can this career be found in?
Data Analysts are used in any industry that has a lot of data to decipher!
Analysts are often hired in Agriculture to improve crop rotation and find efficient harvesting systems. Governments use Analysts to find anything from the electorates who recycle the best, to countering congestion on highways.
Some NFL players have even had sensors placed in their shoulder pads so Analysts can understand their play and suggest performance improvements!
What jobs do people sometimes confuse this with?
Surprisingly, there is a difference between a Data Analyst and a Data Scientist.
Data Scientists are more senior and as such have more experience. Due to this experience, they often try to predict future issues based on prior data. They work with old data to interpret the unknown.
Data Analysts, however, work with current and raw data to better understand what it means, generate insights through analysis and present these insights in a consumable way.
Characteristics and Qualities
According to Seek, some key skills are:
- Data analysis
- Analytical thinking
- Data modelling
- Data warehousing
Data Analysts need to understand the data they are using — they must be able to process and interpret it.
However, they also need to think critically about what this data may be telling them. They must then model and present their findings in a way that is easy for their client to understand.
Steps to Becoming a Data Analyst
What should you study?
Becoming a Data Analyst requires an undergraduate degree. Some people study a broader course in maths, statistics or computer science.
However, a lot of universities, including UTS and WSU, offer degrees in Analytics, or Science degrees with a major in Analytics. These degrees last roughly three years.
This career can also be picked up at a postgraduate level, which is how Felisa became involved in the industry. You can study a Master of Data Science at USyd and many universities have Graduate certificates in Analytics available.
Check out these degrees which can lead to a career in Data Analytics:
How long does it take to become a Data Analyst?
A degree in Data Analytics or similar subjects takes three to four years. Many graduates go straight into employment, often with companies that they interned with in the final year of their degree.
It takes roughly four years to become a graduate Data Analyst. However, becoming a Data Scientist is far harder. Professionals must progress through graduate and senior positions, as well as professional development, to be considered a Data Scientist.
Industry Knowledge and Software
“We typically use databases such as SQL Server Management Studio and SAP and data visualisation tools such as Tableau and Power BI, but this is constantly evolving and changing. We work in a very agile manner, and have partnerships and alliances with the best and most innovative software and technology solutions so that we can always provide the best offerings for our clients,” Felisa explained.
Data Analysts also work with forecasting or predictive modelling, clouds (such as those run by Microsoft, Amazon or Google, where mass amounts of data is stored), data visualisations and robotics process automation!
What will this career look like in the future?
How in-demand is this career?
There is a lot of growth in this career, particularly in a commercial setting. Seek projects a job growth of 12.9% in the next five years.
Are there opportunities to grow or specialise?
There are four main types of data analytics which people each person in the industry must know, but may also choose to specialise in.
- Descriptive analysis: Examines what happened and is rooted by a set of rules. Includes data mining and summary statistics.
- Diagnostic analysis: Looks at why an event happened, based on probability.
- Predictive analysis: Sees what might happen if certain conditions occur, is based on future events looking and current information. Includes quantitative analysis and machine learning algorithms.
- Prescriptive analysis: Looks at which actions are best based on desired outcomes. Uses known data rules to determine the best outcome. Includes artificial intelligence.
Felisa explained that though some Analysts may choose to specialise, this is unusual. Most Analysts and consultants are expected to be able to digest and interpret raw data for any type of client.
The Data Analytics profession, however, does have quite a lineal career progression. Felisa told us that most people become a graduate or Junior Data Analyst, then a Senior Data Analyst, before progressing to being a Data Scientist.
“Data Scientists are highly sought after… A lot of companies will hire for them, as they are very experienced, and it takes a lot to be considered a true Data Scientist,” Felisa said.
|Annual Salary||Future Growth||Skill Level Rating|
|$110,000+||Very strong over the next 5 years||Very high skill|
This career requires a very high skill level. The most common salary according to Seek is $110k a year.
The Future of this Industry
Felisa told us about two key areas that Data Analytics are evolving.
“As more and more companies migrate to the cloud, there will be less data sources and legacy systems to worry about, but the amount of data that exists will multiply drastically. This makes managing data more and more challenging, and there will be more regulations around managing data,” she explained.
Essentially, our professional landscapes are becoming increasingly automated. Felisa doesn’t believe, however, that this will make a huge difference within her career.
“I think it will really change the landscape for the next generation,” she said.
Best Thing & Worst Thing
What do you enjoy most about your job?
“Successfully extracting highly valuable insights after sifting through an immensely high volume of data from multiple sources, and piecing the right pieces of information together for analysis and to identify trends and patterns. The most rewarding part of the job is actually presenting the results back to the client through strong visualisations that tell a story.”
Felisa explained that as a consultant, she must develop visually pleasing, digestible ways of presenting complex information and insights to clients.
What do you feel is the worst part of this job?
Felisa said, “A data analyst’s nightmare is sorting through bad data and not having enough client-based business knowledge to understand how to make sense of it!”
Advice for Aspiring Data Analysts
What do you wish you had known before you started working in this career?
“Good data is a myth,” Felisa laughed.
“Even [when] streaming and ingesting real-time data, it needs to go through an ETL or ELT process depending on whether it’s going into the cloud, and not just be ingested raw.”
ETL, or Extraction, Transformation and Loading, is a process in which raw data from multiple sources (like data hubs) is synthesised into a concise document or pattern. This allows Analysts to understand the history of a client’s data, compare data and propose solutions to stakeholders.
Why should people consider taking on this career?
“If you are a highly curious person, have excellent analytical and problem solving skills and a strong attention to detail, then [you may be well-suited],” Felisa said.
“It requires a purple person, which means they’re neither pure technologist, nor pure business — they’re a blend of both… You need strong mathematical and numeracy skills, but also [need to] have an eye for design and good taste so that you can present and communicate your analysis effectively without losing the audience’s interest and attention.”
Felisa explained that Data Analysts need to understand the story of a business, so they can communicate with stakeholders on a personal level. Your clients must be able to trust you!
Data Analytics is generally a job that requires independent work, and many companies are starting to recognise that different staff are productive at varying times throughout the day. It is therefore a career that allows for remote and flexible working.
“Our company promotes wellbeing and flexible working, and example is set from the top. We’re generally encouraged to work from home, with the option to go into the office if it helps with our productivity. The hours are also quite flexible, aside from daily stand-ups and client meetings,” Felisa said.
What is the workplace culture like?
Felisa said, “It’s generally quite fast paced as we’re encouraged to churn out a large amount of work in a fairly short period of time. Which you can imagine can be quite challenging because with data analytics, things usually start quite slow as you’re spending most of the early stages trying to understand and make sense of the data coming from various sources, and making an early call on what can be useful.”
The exciting part, she explained, is identifying trends and patterns which can be visualised and told as a story to the clients.
Lucinda Garbutt-Young hopes to one day be writing for a big-shot newspaper… or maybe just for a friendly magazine in the arts sector. Right now, she is enjoying studying a Bachelor of Public Communication (Public Relations and Journalism) at UTS while she writes on the side. She also loves making coffees for people in her job as a barista, and loves nothing more than a sun shower.