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Australian companies are finding it hard to recruit suitable data scientists, and organisational inertia is holding back the change in mindset required for those scientists to approach analytics experiments.
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The need to embrace analytics with an open mind and flexible expectations has often proved difficult to implement in practice, according to Bill Franks, chief analytics officer at Teradata, who was speaking at a recent customer seminar in Melbourne.
Many companies, he said, approach analytics in the way they approach other IT investments – with a rigid scope of engagement, predefined notions of deliverables and a profit-minded reluctance to continue exploring analytics exercises that do not pay off as expected.
This rigidity of thought is counter-productive and shackles the expertise of the data scientists recruited to build the business, said Franks, who advised companies to approach analytics with the same loosely defined terms of engagement as they do when it comes to general business research and development.
“It goes back to the idea of discovery,” he said. “People say analytics seems a bit loosey-goosey and tell the analytics people that ‘it feels like what you’re saying is that you don’t know what you’re doing’.”
Such an experience was common among respondents in the Institute of Analytics Professionals of Australia (IAPA) Skills and Salary Survey 2014, with 39% saying their biggest job challenge was convincing their company of the value of analytics. A further 38% said the challenge was getting the company to act on the insights it produced.
Evan Stubbs, SAS
“There’s a disconnect between what companies say they want and what’s actually happening,” said report author and SAS chief analytics officer Evan Stubbs. “Everyone wants answers and yet analysts struggle to get people to act on those answers.
“Business leaders will need to improve the use of evidence from analytics to guide decision-making and understand how marrying insights and experience improves performance. Analytics professionals need to directly link their efforts to business priorities and communicate insights in ways that are easily assimilated and understood by business decision-makers,” Stubbs added.
Analytics needs research and development phase
Franks has seen companies overcome this inertia by getting executives to look past traditional return on investment metrics and, instead, to focus on empowering data scientists to experiment without fear of failure.
“The key is to reposition it as R&D on analytics,” he said. “R&D is an accepted practice, and that’s how you have to judge analytics. Establish a team dedicated to discovery that has to, for example, come up with a couple of discoveries that have paid for their existence.”
“They aren’t judged by every attempt, and nobody is worried about the failures along the way as long as they’ve documented those failures and can make them into a higher rate of success going forward.”
Giving data scientists room to move may be difficult for results-focused executives. Yet, as the analytics market continues to explode – driving surging demand for data scientists with the skills to turn data into business actions – those executives will need to get over their old habits and embrace the spirit of analytics exploration.
The need is particularly pointed in Australia, where analytics adoption and demand for data is high. ANZ CIOs ranked business intelligence and analytics as their top technology priority for 2015 in Gartner’s annual survey of CIO priorities, which predicted Australian spending on business intelligence (BI) and analytics tools would grow by 12.1% this year to $A670.6m and maintain double-digit annual growth through 2018.
Data scientists in demand
This surging demand is reflected in the salaries of Australian analytics professionals which, according to IAPA, are earning median salaries of A$125,000 – twice the average national individual wage of around A$58,000.
Data scientists with specialised expertise can earn even more, with specialists in social media and social-network data analysis commanding a 50% salary premium and earning median salaries of A$190,000 – more than three times the national average.
Those positions require a unique range of skills, including expertise in bridging business and analysts, and managers are struggling to find them. Nearly 90% of respondents to the survey said they were finding it “harder than” or “as hard as” it was a year ago.
“Analytics is becoming a career magnet,” said IAPA chairman Doug Campbell. “The report shows that respondents are moving into the field from other disciplines – evidence that analytics is emerging as an attractive career option.”
A clear definition of the role of “data scientist” – a job title reported by 24% of respondents to the IAPA survey – was still emerging and those fitting the role “showed a unique set of skills”, the organisation found.
“They’re more multi-skilled, more technical, and use change management, persuasion, business case development and communication skills more than other respondents,” said IAPA.
Read more about data science in Australia
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- Australian businesses, such as the University of Adelaide, are gaining customer insights from large volumes of data through data analytics technology.
Business and technology consultancy Accenture Australia is one of many organisations that has been hiring data scientists rapidly on the back of increasing demand for analytics from its clientele.
“It’s a very hot topic and a lot of people are looking for more and more analytics projects,” said Accenture’s energy industry group managing director Paul Carthy, who has helped drive major analytics projects for companies such as Australian energy giant Woodside Energy.
“We have scaled our teams and data scientists have broad-ranging skills across the resources industry,” he said. “They’re not easy to find, and we have spent time over the years trying to make sure that team is structured correctly. It is quite a niche skill.”
Beware of analytics sprawl
The specialised expertise of data scientists will also be essential in containing what Gartner refers to as “analytics sprawl”, which it defines as “an inconsistent or incomplete use of data, capricious development of metrics and formulae, and either too-restrained or unrestrained sharing of results.”
This sprawl will be exacerbated by the rise in self-service analytics capabilities, which will emerge as companies embrace increasingly casual approaches to analytics that build on the expertise brought by data scientists, particularly around combining data sources and positioning analytics tools for maximum benefit.
“Data preparation is one of most difficult and time-consuming challenges facing business users of BI and data discovery tools, as well as advanced analytics platforms,” warned Gartner research vice-president Rita Sallam.