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What Is Data Analytics?

This article provides an introduction to data analytics, explaining what it is and how the field is growing.

What is data?

In its simplest form, data is information of any kind. Data on its own can be vast and confounding; however, when synthesised and analysed it provides a wealth of insight into every aspect of the world – from complex business functions, to interpreting unseen ecological environments, and even positing hypotheses about ancient human history.

What is data analytics?

Data analytics is the process of finding, preparing, transforming and modeling data to gain insights that can inform business decision-making. As the complexity of your analysis increases, different types of data analytics can be used to answer a series of questions:

  • What happened?
  • Why did this happen?
  • What will happen?
  • How can I make this happen?

When embedded successfully, data analytics can lift the capability of all areas of an organisation – from managing human resources to measuring customer satisfaction and predicting product trends.

The age of analytics

As a result of widespread adoption of technology across our personal and professional lives, we are now generating volumes of data and, in many cases, have unprecedented access to these data sets. The evolution of analytics, supported by advancements in cloud technology and machine learning, gives us access to highly evolved tools and sophisticated functionality that can disseminate large quantities of data quickly and effectively. Today’s professionals are operating in the age of analytics.

According to ‘The future of work’,[1] 90% of all data in existence has been created in the last 2 years.

For example:

  • 10 billion mobile devices will be in use by 2020.
  • Mobile devices are being used to read over 50% of the 294 billion emails sent every day.
  • Google processes 63,000 searches a second, which translates into 3.8 million searches per minute, 228 million searches per hour, 5.6 billion searches per day, and at least 2 trillion searches per year.
  • Facebook’s data warehouse can now hold over 300 petabytes of data, or about 30,000 times more information than is stored by the U.S. Library of Congress.
  • Trillions of sensors monitor, track, and communicate with each other, populating the Internet of Things (IoT) with real-time data.

As a result of this exponential growth, the world creates an additional 2.5 quintillion bytes of data each year [1].

This exponential growth of big data, supported by increasingly sophisticated algorithms, enhanced computer power, and increasingly cheap data storage, has brought us to the age of analytics.

When we discuss the pursuit of data, and the valuable insights it can give us, we need to consider the implications (at both personal and organisational levels) of working with such massive amounts of data. Data governance and regulation are at the forefront of the data analytics sphere as governments and other bodies respond to the rapid growth in this area. Therefore, considering the morality of data acquisition, how and by whom it is accessed, and how it is used and stored, are fundamental to data analytics.

The demand for data analysts

Deloitte Access Economics forecasts that, in Australia alone, the data science workforce will grow from 301,000 people in 2016–17, to 339,000 people in 2021–22. The average annual growth rate of 2.4% is stronger than the 1.5% per annum growth that is forecast for the entire Australian labour force.[1]

Although this growth is notable, the growth in demand for data-related skills is much more significant. Our future workforce won’t all be data scientists, but a large percentage of them will need to be able to manage and analyse data to do their jobs. This trend is being seen in finance, marketing, healthcare, education, and most other professions as illustrated in an IBM and Burning Glass report into the American economy.[2] Markow, Braganza, and Taska noted that the number of job postings specifically for data scientists did increase in 2016 (+5%), and there was a dramatic growth in job descriptions requesting data-centred skills including quantitative data analysis, data visualisation, and A / B testing.[2]

Bar chart showing percentage increase in demand for data skills across different job areas. The job areas are: machine learning (17% increase), A/B testing (22% increase), data engineering (28% increase), data visualisation (31% increase), quantitative data analysis (38% increase), data science (40% increase) and clinical data analysis (54% increase).

This diversity points to a new reality: success in the jobs of the future will depend on essential ‘transcendent skills’ – such as data analysis – that are valuable in multiple roles and contexts.


  1. The future of work: occupational and education trends in data science in Australia [PDF]. Deloitte Access Economics; 2018. Available from:
  2. Markow W, Braganza S, Taska B. The quant crunch: how the demand for data science skills is disrupting the job market. 2017. Available from:
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Data Analysis and Fundamental Statistics

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