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The volume of data available to organisations is growing exponentially and so is demand for the professionals who know how to gain advantages from the use of “big data”. Universities are responding by launching more courses to meet demand for more data experts.

Amid the skills shortage, employers are paying a premium for data scientists and analytics professionals that have superior technical skills to manage and take advantage of the data boom. From industries as varied as health, defence, finance, transport and logistics, agriculture to media and technology, organisations are relying on data science and analytics professionals to deliver insights from data to drive their organisations forward.

The “big data” boom has been created by growing internet usage and cloud computing, as well as the proliferation of online platforms and devices on which people are accessing an ever-increasing number of services. Some organisations need to extract knowledge from the data to do their core business and others are seeking ways to use data to obtain strategic advantage or support innovation.

Data scientists use analytic tools in mathematics, statistics and computer science to extract the insights that power business and organisational development. They scrutinise data for patterns of behaviour and insights to predict future trends and improve productivity and sales. Data scientists often came with qualifications in computer science or mathematics but increasingly Data Science programs are being created to provide qualifications. Some of these programs are at the undergraduate level but most are at the postgraduate level, building on an undergraduate background in mathematics, statistics or computer science.

On the other hand, analytics professionals might come from a business background and use a broad set of strategic decision making and analytical capabilities to produce evidence-based insights from data that can help organisations make more informed decisions to underpin organisational or business strategies. Analytics professionals typically have a specific goal in mind when they work with data, looking for ways to support organisations in making strategic decisions.

Data science and data analytics are central to internet searches, fraud detection, targeted advertising, route planning, speech recognition, image analysis, genetic risk prediction, virtual reality, customer loyalty, product development and autonomous vehicles. But there are many areas awaiting development in the future.

Demand for analytics skills grows

IBM and BurningGlass have predicted that, by 2020, the number of positions for data and analytics experts in the US will increase by 600 per cent. Demand is coming from business, government, healthcare providers and other organisations who need analytics professionals to organise and extract meaning from data. Similar growth can be predicted for Australia.

As demand grows, salaries for data scientists and analytics professionals are increasing at a much greater rate than professional salaries generally.

The Institute of Analytics Professionals of Australia (IAPA) 2017 Skills Salary Survey reported that the top 10 per cent of earners of all data analytics professionals reported an average jump of 7 per cent to a median salary of $235,000 in 2017. The median salary of team managers and technical specialists was $163,000, while the average salary of an analytics professional was $130,000, well above the average salary of professionals at around $91,000 in May 2018.[1]

A survey by jobs website Indeed.com.au as at 6 May 2019 reveals that the average annual salary for data scientists in Australia was $116,889.^[2] This figure compares favourably to the average of $94,131 for solicitors and $71,719 for accountants. Indeed’s analysis of data scientist salaries was based on 327 salaries submitted to it anonymously by data scientist employees and users and collected from job advertisements on the site in the past 36 months.

Data scientists also command a premium compared to other IT professionals, with systems engineers averaging $112,846 and web developers averaging $77,322, according to Indeed.com.

What is also impressive about the analytics industry is that the wage gap between males and females is narrower than in many other industries. The pay gap between male and female salaries in 2017 improved slightly to 8 per cent, almost half that of the Australian job market at 15.3 per cent, according to the IAPA 2017 survey.

Providing the skills needed

Data science and analytics skills are increasingly being demanded by organisations of all kinds as they seek to derive value and insights from their data. Since there aren’t enough experts to go around, universities are launching undergraduate and postgraduate degrees in both data science and analytics fields.

Data scientists need knowledge of database systems, including modelling, design and implementation. They need very high-level programming skills that extend beyond Python. They need the skills to develop artificial intelligence and machine learning technology and the algorithms to capture and organise data and data sets.

They also typically need a full knowledge of mathematics and advanced statistics, including distribution theory, statistical inference, Bayesian statistics and techniques widely used to analyse relationships between variables such as regression analysis.

On the other hand, analytics professionals may use a broad set of strategic decision making and analytical skills to produce evidence-based insights from data. Analytics experts need to understand how to design and implement application systems to support evidence-based decision-making in organisational contexts. They often adopt a big-picture, more managerial perspective and consider the ‘people’ side of the organisation as well as the data side. Tertiary courses may include the study of analytics solutions based on online analytical processing (OLAP) models and technologies.

Expertise in the use of analytics tools, including Python, R, Tableau, AWS, Google Cloud Solutions and NoSQL systems is also necessary. According to the Institute of Analytics Professionals of Australia (IAPA) 2017 Skills Salary Survey, the most sought-after analytics professionals have the skills and knowledge to work with HDSF, NoSQL, Hive, Pig/MapReduce and Spark, text analysis, Tableau and R. Professionals. Professionals with these skills can command salaries $30,000 to $45,000 above the median salary.

Postgraduate study in the data science or analytics area can build on individuals’ core technical competencies as well as help to prepare them for more senior management roles, which command higher salaries. Completing a higher degree signals to an employer a professional’s greater competency in their field and readiness for promotion. As the big data boom rolls on, we will see more demand for postgraduate study from data experts keen to realise the benefits of further study. For people still deciding on their careers, data science and analytics offer great employment opportunities and salary potential.

Reflecting that, LinkedIn this year named ‘data scientist’ as leading its list of The Most Promising Jobs of 2019.[3] Data scientist positions “come with high salaries, a significant number of job openings and year-over-year growth, and are more likely to lead to a promotion”. US job openings for data scientists posted year-on-year growth of 56 per cent to 4,000-plus openings, according to LinkedIn. In Australia, the growth in data science and analytics jobs is similarly great as the digital age gains momentum.

Tracey Wilcox is the Academic Director, Postgraduate Programs for UNSW’s Business School.

Professor Bruce Henry is the Head of The School of Mathematics and Statistics, UNSW.

[1] Based on average weekly total cash earnings of $1,751 of professionals, ABS, Employee Earnings and Hours, Australia, May 2018

[2] https://au.indeed.com/salaries/Data-Scientist-Salaries?from=serpsalaryblock

[3] LinkedIn’s Most Promising Jobs of 2019

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