. However, extracting maximum mileage from analytics initiatives requires the right talent resources.
To address this issue, organisations will need to answer the bigger question of what indeed is the ‘right’ analytical talent. This refers to individuals with an interdisciplinary knowledge of mathematics, business, technology and behavioural sciences, as well as design thinking capabilities to solve complex business problems in an efficient and sustainable manner. The solution to this talent gap lies in the foundation level; an increased emphasis on recruitment
and training of talent through specifically designed programs through Universities and analytics companies.
The Australian Universities – James Cook University, Monash University, the University of Technology, Sydney (UTS) and Victoria University – are already working towards bridging this gap. These institutions are introducing data science courses in 20152
. Other premier Australian Universities are also looking to include data analytics related courses in their curriculum as early as this year. This is a clear indication of the focus the Australian market is placing on creating the best analytical talent from ground up.
While there is a plethora of job opportunities in the analytics field, the supply of analytical talent is still falling short of the demand. Here are a few strategies to help corporate Australia cross the analytics divide and hire the right talent:
Creation Vs. Acquisition: Debunking the ‘top tier’ myth
The notion that only top-tier Australian institutions produce the best talent is a debatable one. The underlying fact is the overall scarcity of analytics talent. In order to institutionalise analytics initiatives within their ecosystems, organisations will need to work towards building the talent pipeline not just for today but for the foreseeable future, by creating and nurturing this talent base through tailored programs.
Since an individual might not be endowed with all the necessary skills required for business problem solving, it is important to look for ones who have the right orientation to business problem solving – an analytical and quantitative bent of mind fused with a creative outlook. The onus then lies on both academic and corporate institutions to hone these multi-disciplinary skills of mathematics, technological capabilities, business acumen, combined with behavioral science to create the new age analytics professional or the ‘Decision Scientist’
– someone who is not just capable of sifting through the trove of data and generating insights, but is equally adept at synthesizing, translating and communicating these insights thereby taking an active part in the business decision making process.
The hunt for the right decision sciences professionals should not be limited only to top tier universities, rather the hiring spectrum must be extended to accommodate mid-tier institutions as well. With the costs associated with training and molding raw talent being the same across all universities, the overall ranking of the university makes little difference. What is important is that organisations streamline their hiring methodologies to suit the rising industry demand.
’Expert’ mentality Vs ‘Experiential’ learning: “curiosity” and “learnability” are key traits
In any sphere, be it business or otherwise, the only constant is change. The present day rapidly changing, competitive business landscape requires businesses to be suitably equipped to deal with the rate of change. They cannot afford to remain inflexible since what was acceptable a few years ago is in all probability heading for obsolescence in the coming years. While the expert mindset means that there is substantial knowledge built over time, if this knowledge is rendered unworthy over time, it will not enable a sustainable business model for organisations.
Businesses need to foster an ecosystem of continuous experimentation fuelled by curiosity rather than just relying on knowledge assets. Human progress hinges on one fundamental trait: curiosity. Hence, the talent pool should be garnered with skills that enable a questioning mindset, constantly learning through experience and by adopting ‘creative destruction’ – shedding older, redundant concepts and making way for newer principles. To ride the analytics and Decision Sciences wave, innovate and stay ahead of the curve, organizations need to take the ‘fail fast and often’ approach and have an agile talent pool that is capable of quickly observing, absorbing, and adapting to changing business needs.
When evaluating prospective decision sciences talent, organisations will need to evaluate candidates on their ability to think from ‘first principles’, and learn continuously by displaying a sense of inquisitiveness in their spheres of academic and extracurricular life. Inculcating the art of asking the right questions at the right time and maintaining objectivity are crucial to problem solving. Candidates’ ability to solve problems can also be further gauged on their “out-of-the-box” thinking, and tendency to take new challenges head on.
To enhance these skills, organisations will need to substantially invest in continuous and holistic training of selected candidates. And in the field of decision sciences and analytics, this training must encompass skills necessary for consulting, structured thinking, advanced statistics, analytical techniques, design thinking, machine learning along with a real-time understanding of industry verticals and business functions such as marketing, risk, and supply chain. Hence, a well- designed recruitment
program to test these primary skills is paramount.
Appreciating the new ‘IP’ – ‘Inter-disciplinary Perspective’
Contrary to popular job descriptions, the role of the Decision Scientist
extends beyond designated tasks or single skill experts. Decision Scientists are required to be versatile in their ability, combining both the right brained heuristics with left brained logic to exhibit a multi-disciplinary skillset. These candidates must have more than just a quantitative aptitude and number crunching skills. They should also be able to grasp the nuances of the business, abstract and synthesize, communicate their findings and insights effectively ensuring a balance of hypothesis and discovery-driven approach to problem solving. They should be able to view the problem landscape holistically, look at seemingly unrelated problems and identify linkages and interconnections between.
As these decision sciences professionals move up the rungs to take on bigger roles, their traits evolve to accommodate client relationships, people management and delivery management across global teams. Hence it is critical for organisations to design innovative hiring practices that can evaluate candidates for all-round fitment.
The end objective? A decision scientist needs to connect the dots to a business problem through exploration, understand and appreciate the world of business problems in its entirety in order to extract maximum value and create maximum impact.
About the author
Rajat Mishra is Regional Head, Mu Sigma
Gartner estimates that 2015 will witness a surge in Australian big-data centric jobs, with numbers being estimated at four million globally