What is data analytics?

Data is generated – and collected – everywhere. Interact with a website: data. Use an app: data. Use contactless payment: data. Sign up to a mailing list: data. Just have a mobile phone switched on: it’s all data. And all that data needs people to sift through it and work out what it means. That’s where data analytics comes in. Making sense of data is now crucial in almost every industry – and data analysts (the pros who make sense of it all) are in demand. So let’s take a look at this vital function: what is data analytics? What do data analysts do? And what does the future hold?

What is data analytics and how does it work?

Woman in front of a white board discussing data analytics

At its heart, data analytics is the art of unraveling the mysteries hidden within vast amounts of data – to reveal valuable insights that guide decision-making. It’s like being a detective in the digital age, using sophisticated techniques to transform raw data into actionable intelligence. From spotting trends to connecting the dots, it’s all about staying ahead of the curve. Plus, it ensures decisions are backed by solid evidence, promoting transparency and accountability within organisations. And it’s essential across diverse fields everything from business and healthcare.

Data analytics has three basic forms:

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  • descriptive
  • predictive
  • prescriptive

Picture it like this: Descriptive analytics paints a picture of the past, revealing historical trends and patterns. Predictive analytics forecasts future outcomes using complex statistical models. It’s like having a crystal ball for strategic planning and risk management. And prescriptive analytics offers strategic guidance, showing us how to navigate the ever-changing business landscape with agility and resilience. It’s the compass that guides strategic decision-making and thus drives progress.

What does a data analyst do?

A data analyst working at a computer

Data analysts dive into complex data, sorting, analysing and spotting trends to help businesses make smart decisions. They clean up data for accuracy, find patterns and also create visualisations (charts and presentations, to you and me) to show off insights. Basically, they help companies understand what customers want and where markets are heading.

Analysts collect data from diverse sources such as databases, spreadsheets, websites, surveys, internal company systems and application programming interfaces (which are sets of rules and protocols that allow different software applications to communicate with each other).

Working closely with lots of different teams, data analysts hone their investigations based on business requirements and ensure that their analyses align with where organisations want to go. Put simply, their work is crucial for success in modern business.

However, it’s also worth noting that data analysis is not just for businesses. Its getting more and more crucial across the board. From government to non-profits – and everything in between.

For example, the UN believes data can drive sustainability:

Data is the lifeblood of decision-making and the raw material for accountability. Today, in the private sector, analysis of big data is commonplace, with consumer profiling, personalised services, and predictive analysis being used for marketing, advertising and management. Similar techniques could also be adopted to gain real-time insights into people’s wellbeing and to target aid interventions to vulnerable groups. New sources of data, such as satellite data, new technologies, and new analytical approaches, if applied responsibly, can enable more agile, efficient and evidence-based decision-making and can better measure progress on the Sustainable Development Goals (SDGs) in a way that is both inclusive and fair.

[Psst: Charles Sturt has signed up to the UN’s SDGs, and we weave them into everything we do, including all our courses. So even if you’re studying IT and tech, you’ll learn how to make your operations greener.]

Where do analysts work?

Data analysts work in various industries where data-driven decisions matter. They’re common in the tech sector, of course, helping to understand user behaviour and enhance performance of products and services. Meanwhile, finance, investment and banking
firms rely on them to analyse market trends and manage risks.

Healthcare utilises analysts to improve patient outcomes and also streamline operations. Retail, manufacturing and government sectors also employ them for efficiency and to inform policymaking. Plus, they are key players in industries like marketing and media – places where clicks matter.

So if you’re interested in a career in data analytics, you can pretty much choose where you want to use your expertise to help.

Bonus: you can also choose to work remotely. Data analysts can often work from anywhere there’s a decent wi-fi connection.

What are the limitations of data?

Data has shortcomings, despite its usefulness. It can carry biases from how it’s gathered, processed and understood. These biases might distort the truth, giving us a skewed view of reality.

Moreover, data mainly reflects what’s already happened, not what’s to come. While it reveals trends and patterns, it can’t foresee unforeseen events or changes. With technology evolving rapidly, yesterday’s data might not mean much tomorrow.

So, data analysts always approach data with a critical eye, looking to understand its origins and limitations before drawing conclusions.

Will AI take over data analytics?

Right now, AI is like the sidekick that boosts data science, not the boss that replaces it. Data science isn’t just about crunching numbers. It’s about human smarts, knowing your stuff and then making sense of it all.

Sure, AI can do some heavy lifting with data, but it’s humans who bring the magic touch of interpretation and decision-making to the table.

AI’s growth depends on data science. It needs the juicy datasets and insights that data scientists provide to learn and evolve. Without them, AI would be useless.

But it’s not just about the tech stuff. Ethical dilemmas? That’s where humans shine. Data scientists make sure AI plays fair, stays transparent and can also be held accountable. They’re the guardians of fairness and the architects of ethical AI.

So, will AI take over data science? Nope, they’re more like partners, each bringing their own skills to the table.

How to become a data analyst

To become a data analyst, you need technical skills, analytical thinking and also practical experience. At Charles Sturt University, the Bachelor of Information Technology1 covers all
of them. You’ll learn and apply statistical concepts, programming and systems analytics. You can also gain real work experience through internships with companies like IBM Australia.

And after gaining experience, how about coming back to do a postgraduate degree such as the Graduate Certificate in Business Data Analytics? It will deepen your understanding of data interpretation and ability to apply insights to foster innovation.

Next steps

Get in touch and we’ll help you start your career in data analysis on the front foot.

1Cricos: 012006F