Market Update: How much do Data Analysts earn in Australia 2026

Which industries most commonly hire Data Analysts?

It should come as little surprise that the industry which hires the vast majority of Data Analysts is Information & Communication Technology (ICT). The tech sector itself runs on data, from product development to user analytics.

Beyond tech, nearly every major industry is now investing heavily in data talent. Some of the other industries that frequently hire Data Analysts include:

• Finance and Banking: For risk modelling, fraud detection, and investment analysis.
• Healthcare: To analyse patient outcomes, streamline hospital operations, and model disease spread.
• Government: For policy analysis, resource allocation, and building public-facing services.
• Retail and eCommerce: To understand customer behaviour, optimise supply chains, and personalise marketing.
• Professional Services (Consulting): Firms like Deloitte and EY hire teams of analysts to solve problems for their clients.
• Marketing and Advertising: To measure campaign performance, conduct market research, and segment audiences.

What are the top skills a Data Analyst needs?

Today’s Data Analyst needs a wide range of capabilities, including a mix of powerful hard skills and essential soft skills.

Hard Skills

In terms of hard skills, the following are essential:

• SQL (Structured Query Language): SQL is the universal language for “speaking” to databases. A Data Analyst uses SQL to extract, manipulate, and retrieve data from a company’s relational databases. It is the non-negotiable foundation of almost every data role.
• Data Visualisation: Raw data is useless if it can’t be understood. Data visualisation is the art of turning complex data sets into easy-to-understand charts, graphs, and dashboards. Mastery of tools like Tableau or Microsoft Power BI is a core requirement for communicating insights.
• Statistical Programming (Python or R): While not always required for entry-level roles, a programming language like Python (with its libraries like Pandas and NumPy) or R is essential for advancing. These tools are used for cleaning large datasets, performing complex statistical analysis, and building predictive models.
• Statistical Analysis: A Data Analyst must have a solid understanding of core statistical concepts. This includes descriptive statistics (mean, median), inferential statistics, hypothesis testing, and regression analysis to identify significant patterns and trends, rather than just guessing.
• Data Management: Knowing how to collect, organise and store data efficiently and securely is important for Data Analysts to ensure data accuracy, security and accessibility. As organisations deal with big data on a daily basis, Data Analysts need the skills to manage and process large amounts of information efficiently.

Soft Skills

There are also a variety of soft skills that are needed to succeed in the industry, the most important of which include:

• Communication and Data Storytelling This is perhaps the most important soft skill. An analyst must be able to explain their complex findings to non-technical stakeholders, such as managers or marketers. This “data storytelling” turns insights into a compelling narrative that drives action.
• Critical Thinking and Problem-Solving The core of an analyst’s job is to solve problems. This requires a curious and critical mind to ask the right questions, identify the true source of a problem, and devise a data-driven path to a solution.
• Business Acumen A great analyst understands the business they work for. They connect their data analysis to the company’s core goals, such as increasing revenue or improving customer satisfaction.
• Attention to Detail Data analysis is a precise discipline. A small error in a query or a calculation can lead to flawed insights. Top analysts are meticulous and thorough in their work to ensure their data is accurate and reliable.

The latest trends in Data Analytics

With tech and software advances always pushing the possibilities of data, it’s an exciting time to be involved in the industry. Here are a few trends to keep an eye out for in 2026 and beyond.

1. AI and Machine Learning Integration Like many industries, artificial intelligence is becoming a significant part of the analyst’s toolkit. Analysts are increasingly expected to work with AI-powered tools, use basic machine learning models to make predictions (like customer churn), and analyse new, complex data types.
2. Focus on Data Governance and Privacy With the rise of new privacy laws and growing public awareness, data governance is a massive trend. Companies need analysts who understand how to handle data responsibly, ethically, and securely. This includes skills in data security, confidentiality, and compliance.
3. The Rise of Cloud Analytics Data is no longer stored on a single computer. It lives in the cloud. Analysts need to be comfortable working with cloud platforms like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud. These platforms provide the scalable power needed to analyse massive datasets.