The past decade has seen some significant shifts in the way talent is perceived, managed and remunerated in the analytics profession. From back-room quants with sensible shoes to the ‘data science’ darlings of the media sector, analysts have seen a stellar rise in professional cachet.

The annual IAPA Skills and Salary Survey has tracked the rise and rise of the analyst salary, with median salaries now sitting at twice the average Australian’s take home wage in 2017, with an even stronger rise above the norm at the entry and expert ends of the spectrum.

The 2017 report also highlighted a shift in the skill sets demanded by industry – as analytics becomes integral to more aspects of business operation, the ‘consulting data scientist’ with a mix of soft skills and technical expertise is increasingly valued for their ability to provide insight and strategic guidance based on quantified evidence.

Surprising, but also pleasing, is the finding that despite analytics being a heavily male skewed industry, female analysts take an almost equitable seat at the table. The gender pay gap in Analytics is half that of the Australian workforce overall at just 8 per cent, and the pay gap for young women entering the industry is a mere 7 per cent.

We want unicorns

A declining pay gap and increasing entry of women into analytics may be a response to one of the key trends noted in the IAPA survey – businesses are increasingly demanding analytics professionals present with strong soft skills as well as technical expertise. “Data science is one of these areas where they talk about the ‘unicorns,’ or people who are supposed to be able to do statistics and math and present or communicate very well,” says Dr Claudia Perlich, a former data scientist at IBM’s Watson Research Centre, “…and women tend to be very good communicators”.

In Australia, there has been a 108 per cent increase year-on-year in demand for analysts with strong communication skills, and the consulting data scientist with both technical and soft skills is in particularly high demand.

However, while acknowledging the importance of soft skills, analysts and their employers are still highly enamoured of specialised technical skills and place great value on them as a tool for advancement. Having eight or more regularly used ‘soft skills’ on top of a high level of technical expertise results in a 40 per cent salary increase – but more soft skills in the absence of technical expertise results in a 6 per cent salary drop.

IAPA Salary Survey 2017 

 A toolkit for success

The toolkit an analyst brings to the table has a profound influence on their attractiveness to employers and subsequent salary potential. Looking at the median salary differentials associated with different skills, languages and platform knowledge, we – just for fun – analysed which skills would be the most profitable to learn.

  • Adding statistical programming skills (SAS or R) will give you a $10k salary premium, whilst Scala or Spark offers a $35k upside and the 2 per cent of analysts who code in Perl can expect offers $50k above median for their rare skills.
  • Although R Studio skill brings only the same modest salary premium as base R, expertise in Revolution Analytics’ enterprise R products offers a $50K premium.
  • Data engineering expertise on platforms other than SQL Server and Access is highly sought after, with Hive offering a $50k salary boost and Big Query and Hadoop each bringing a $35k pay booster.
  • Expertise in SPSS, PowerBI and Excel bring no employment advantage – all are correlated to a below median salary expectation.
  • Analysts with only SQL and VBA command the same salary potential as those with no programming languages at all indicating these languages are now a ‘given’ rather than offering competitive advantage.

So what does this tell us? Well, if you’re a data analyst and want to boost earning potential, SPSS is not your friend – get the hang of Big Query (hint – you can use your SQL!) and dabble in Spark. If you’re a statistician, become familiar with Revolution Analytics products, and if you are a data engineer, get your head around MapReduce and Hive technologies. If you are starting out and not yet adept in any particular technologies, any data visualisation tool other than PowerBI is going to be a profitable skill to acquire.

It’s encouraging to see our analytic kinfolk becoming highly sought after, and the opportunities to apply analytic techniques in new industries and roles continue to expand. I am curious to see the changes the next 12 months bring – hopefully they will continue to show gains in professional opportunity, gender equity and the development of new and diverse skill sets.

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