Are you drawn to a career in Data Analytics and keen to find out what’s going on in today’s industry? We’ve put together this market update to bring you all the latest Data Analytics insights, statistics and trends.
Most companies have a wealth of data at their fingertips. However, in its raw form data isn’t worth that much. Luckily, this is when Data Analytics steps in. Data Analytics is the process of analysing raw data and extracting meaningful, actionable insights. These insights are then shared with team members and used to make smart, well-informed business decisions.
Data analytics is all about finding data patterns which tell us something useful about an area of a business. For example, it can be used to predict the behaviour of customers, or to assess where a website’s traffic is coming from. In short, Data Analytics is a form of business intelligence that companies use for problem solving and strategic decision-making.
Forbes has predicted the amount of new data produced in the next three years will be greater than that produced during the past three decades. This means the demand for Data Analysts with the skills needed to turn raw data into valuable insights is at an all-time high.
According to James Milligan, global Head of Technology at international recruitment firm Hays, “data is the new corporate currency, as advancing digitisation sweeps over every horizontal and vertical market in the world. The impact on the Data Science sector is far-reaching and a range of new roles and skill-sets are in high demand.”
With a full range of industries now keen to harness the power of data, Seek predicts that employment opportunities for Data Analysts in Australia will grow by 27.7% in the next five years, ranking it as one of the nation’s most promising employment markets.
Data Analysts are currently in high demand in Australia and will remain so for the foreseeable future. Wherever there’s new data, there’s a need for Data Analysts.
Currently, there’s a sizable skills gap for Data Analysts in Australia, which is reflected in the 17,451 roles advertised on Seek.com.au (as of August 2022). Breaking these numbers down by state:
It’s also worth remembering that many Data Analyst roles can be fulfilled remotely. Advances with online work systems mean being a remote Data Analyst can be every bit as collaborative and engaging as working in-person.
The earning potential for Data Analysts in Australia is representative of a lucrative industry that’s always on the lookout for skilled professionals. The latest stats from Talent.com record the average Data Analyst salary in Australia as $104,450. Even Junior Data Analysts earn an average annual salary of $90,988, while Senior Data Analysts make $129,500 a year on average.
The average salary in each state is as follows:
Data Analysts have the capabilities to help any kind of business make shrewd, data-backed decisions. As a result, Data Analysts are highly sought after in just about all industries. Some of the industries that most frequently hire Data Analysts include:
As you might have guessed, the finance industry deals with vast amounts of data and relies heavily on the expertise of Data Analysts.
Data Analysts help finance companies complete credit scoring, perform financial modelling, make profitable investments, enhance customer satisfaction and improve business operations.
Because finance is now almost completely data-driven, the industry is one of the highest employers of Data Analysts and an excellent field for a Data Analyst to build a career in.
Another industry that works with masses of data is healthcare. The healthcare industry looks to Data Analysts to gather and analyse data, helping healthcare professionals accurately diagnose patients and provide the most effective treatments.
The outbreak of an international pandemic has seen big data take on a pivotal role in healthcare, with Data Analysts helping healthcare organisations predict the spread and impact of diseases.
Healthcare has long been one of the industries that most frequently hires Data Analysts and remains a promising industry to launch a career in.
The application of Data Analytics in retail enables businesses to make customer recommendations based on purchase history and behaviour, resulting in personalised shopping experiences and improved customer service.
Working with big data also helps retail companies to forecast trends and make strategic decisions based on market analysis. As a result, it’s an industry in which Data Analysts are in high demand.
Large streaming services are now dominating the entertainment market, and they’re using data to drive that growth. Netflix has recently used its data to recommend shows to users with an 80% success rate, cutting costs by a billion dollars each year.
The entertainment industry also uses Data Analytics to decide which productions to greenlight, test marketing strategies, set prices and optimise the user experience offered by online platforms.
With the entertainment sector increasingly focused on tapping into its data, it’s an industry that provides plenty of employment opportunities for Data Analysts.
When you’re searching for Data Analyst positions, it’s worth remembering that Data Analyst roles often go by different titles. In some cases, the following job titles can refer to roles that come with a remit of Data Analytics:
Today’s Data Analyst needs a wide repertoire of capabilities, including a mix of both soft skills and hard skills.
While it’s not imperative for Data Analysts to have advanced programming skills, it’s essential that they at least have a firm grasp of coding basics.
One of the most popular programming languages among Data Analysts is Python. Python enables Data Analysts to work with massive amounts of data, automate reporting and connect to databases seamlessly.
Another programming language that Data Analysts frequently use is SQL, which allows data professionals to access and query databases, and then clean and manipulate any retrieved data.
Regression modelling is a sophisticated way to measure the relationship between multiple variables in a dataset. This helps analysts confidently determine which factors within data matter most and which can be ignored, as well as how important factors influence each other. Using regression modelling, Data Analysts are able to make all kinds of accurate predictions.
Regression modelling is a go-to method in analytics that many Data Analysts will use at some stage in their career. For anyone keen to advance their career, it’s a technical skill that’s well worth developing.
Data insights are of minimal value unless they can be shared with colleagues and used to influence decision making. For this reason, Data Analysts will normally convert their findings into high-impact visualisations (charts, graphs, tables and other infographics) which are used to persuade stakeholders to pursue a new course of action.
While spreadsheets like Excel can be used to build basic visualisations, using programming languages like Python allows Data Analysts to create custom infographics that are packed with data insights yet easy on the eye.
While many Data Analysts focus on learning technical skills, there are a range of soft skills that are needed to advance in the industry. Some of the vital soft skills any Data Analyst should have include:
With tech and software advances always pushing the possibilities of Data Analytics, it’s an exciting time to be involved in the industry. Here are five Data Analytics trends to keep an eye out for 2022 and beyond.
AI analytics is a subset of business intelligence that relies on machine learning techniques to uncover patterns and reveal insights in data. In practice, AI analytics involves automating much of the work that a data analyst would normally conduct manually.
It should be noted that the aim of AI analytics is not to replace analysts. Instead, it can enhance the scale and granularity of data that a Data Analyst can analyse, while also making their work more time-efficient.
Composable data analytics is a process by which businesses combine analytics capabilities from various data sources. Composable data analytics means companies are able to easily pool all their information, leading to effective decision-making that takes more data into account.
Additionally, with composable analytics, businesses can drastically reduce data centre costs. Gartner analysts have predicted that by 2023, 60% of businesses will have built applications composed of components from at least three analytics solutions.
If you still think of Data Analytics as a relm reserved only for professional Data Analysts, your conception is probably a little outdated. These days, people working in every kind of role in every kind of industry have access to data and are using it to enhance their professional performance.
Gone are the days when professionals were entirely reliant on data teams to perform analytics on request. The democratisation of data will see more and more people analysing data on their own, aided by simple, user-friendly software and tools that are designed to make the processes of Data Analytics accessible to everyone.
The ability to quickly and easily grasp crucial information has long been a core focus of the Data Analytics process. Going way beyond simple pie charts and graphs, modern Data Analysts build interactive data visualisations that not only keep viewers informed, but also encourage them to actively engage with data insights.
Recent years have seen the emergence of immersive videographics, data illustrations and real-time infographics (which constantly update with the latest data).
Sticking with the real-time theme, Data Analytics is now all about the speed with which decisions are made in response to data insights. The ability to make quick, accurate decisions has always been crucial in data science, but the onset of the pandemic made speed an even more essential trait of the analytics process.
Now, more and more businesses want their data to be as current as possible and their decision-making processes to be ultra nimble. As a result, expect to see an agile, real-time approach brought to business analytics in the coming years.
Entering the world of Data Analytics might seem like a daunting feat, but getting started in the industry is easier than you might think.
To launch a career as a Data Analyst you’ll need to follow a few simple steps:
When searching for your first role, it’s not uncommon to start as a Junior Data Analyst, which will entail:
Once you’ve built up your industry experience and a more extensive portfolio you’ll have the chance to apply for mid-level roles.
As you gain more exposure to the industry’s practices, you’ll be able to apply for senior Data Analyst roles. As a senior Data Analyst, there are normally chances to lead data projects and teams, which means you might even land a formal management role. This involves overseeing data research budgets, as well as managing timelines, workflows and team development.
Beyond this, high-level work experience and further formal training will allow you to apply for the most senior roles, such as Data Scientist and Data Architect.
For those with big ambitions, the top of the career ladder can lead to executive positions, such as Chief Technology Officer.
It’s also worth keeping in mind that Data Analysts often go freelance. Many clients will hire freelance Data Analysts for their unique skill-set on a short-term contract or project-basis.
When you’re working as a freelance Data Analyst, no two projects will be alike, but there are common skills that clients always look for.
To kickstart a career as a Data Analyst, it’s vital you undergo practical training that helps you to get to grips with the industry’s latest tools and techniques.
Academy Xi offers a range of Data Analytics courses that are built and taught by industry experts.
Harness the power of data in your career, even as a non-data professional, with our Data Analytics: Elevate course:
Upskill and revamp your role with our Data Analytics Pro: Elevate course:
Change careers with our Data Analytics: Transform course:
Get to grips with Python essentials and learn to programme custom visualisations with our Data Visualisation with Python: Elevate course:
Want to discuss your transferable skills and course options? Speak to a course advisor today and take the first step in your Data Analytics journey.