The demand for big data expertise is unprecedented. The Centre for Economics and Business Research (CEBR) predicts that 182,000 new UK jobs in big data and IoT will be created by 2020. Within this segment, the skills of data analysts and scientists will be among the most highly prised.
Demand is rising in almost every sector. Take a look around. Consumer brands, political parties, news organisations – even MI5 – are recruiting these experts en masse. Demand for the most skilled type of analyst, ‘the data scientist’ rose by 32% in the first six months of 2016 alone.
Leading brands are vying with each other to attract the best talent. Household names like Estee Lauder, L’Oréal, Unilever and Proctor and Gamble all compete by leveraging market data, sales data and geo-analytical analyses to take advantage of opportunities to be more relevant and responsive to customers. For instance, Unilever might send a promotion code to a customer walking on a beach on a hot day to entice them to buy a Magnum ice cream at a nearby shop. Little wonder then that companies are keen to employ data scientists when, a retailer using big data can reportedly increase their margin by more than 60%. Data science is all about interpreting and acting on data to drive incremental sales and gain a competitive edge.
Bradley Maule-ffinch, EMEA Portfolio Director at MACHINA Summit.AI
Global media giants are setting up whole new departments focussed solely on analysing data. The BBC, Bloomberg and Reuters are recruiting a raft of data journalists to make sense of huge reams of data now available to identify trends and generate new stories. Then there’s the intelligence and security agencies. MI5 and its equivalents collect data that is complex and often incomplete. Making sense of this data quickly and effectively is crucial for national security.
So what exactly is it that data analysts do that’s so important? By analysing data, they determine correlations, interrelationships and trends. It all sounds fairly mundane. But their insights can transform the fortunes of people, businesses or indeed entire societies.
Making data science work is a matter of mastering statistics, maths and programming, and then deriving actionable insights from them, by using the ‘softer’ skills of instinct about customer needs and behaviour that is borne out of experience. They are in huge demand as many organisations are still struggling to makes sense of their data. The majority of the world’s data – produced in the last two years – is unstructured, disconnected and difficult to analyse and derive insights from. Many organisations are currently experiencing Gartner’s ‘trough of disillusionment’ – where interest wanes as experiments and implementations fail to deliver. There are signs however that the end of this phase in the hype cycle is coming to an end.
Last year’s EU referendum provides a portent for the future. AggregateIQ was credited by Vote Leave with swinging the result towards Brexit. The company specialises in micro-targeting users of social media, so that political parties can reach them more effectively. Could it be that a Canadian analytics company with just 20 employees changed the course of British history?
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Who knows? But if you get data analysis and insights right, the rewards are huge. Get it wrong, you may never get another chance. For many, it’s like playing poker. The stakes are that high.
So what is the current state of play when it comes to training, recruitment and supply of experts with such sought after skills? Demand is not currently being met by supply. In Europe, The skills of a data analyst are typically from the highly sought after STEM disciplines. Consequently they demand a premium for their services, the average data scientist takes home £56,000 per annum, with many working at the top earning over £100,000. With such good salaries on offer, a career in the field would be a safe bet for many, you’d think.
But things can change very quickly. It is entirely possible that highly sought after data scientists will become obsolete within a relatively short period of time. AI is predicted to sweep away up to a third of jobs by 2030, and you’d think it a safe bet that a supercomputer will be able to conduct data analysis far better than a human. As IP EXPO Europe 2017 keynote speaker Professor Brian Cox stated: “There is nothing special about human brains. They operate according to the laws of physics. In a sufficiently complex computer, I don’t see any reason why you couldn’t build AI.”
So the analytical role may be easy to replace by a machine in the near future. But perhaps the ‘softer’ human centric skills of commercial experience and gut instinct that the best data scientists possess will be harder to replicate in AI?
It’s difficult to predict. But a recent McKinsey study suggests that when machines do take over automation of some tasks, it unlikely to mean the complete end of that line of work. It cites an example of retailers introducing barcode scanners and point of sales devices in the 80s. Labour costs were cut and more promotions could take place. But there wasn’t a reduction in the number of cashiers in supermarkets. On the contrary, over the next thirty years or so, the number of cashiers employed increased.
Perhaps then the story of AI and automation and its effect on society will be far more nuanced, than the dramatic overnight revolution than some suggest. The more predictable, straightforward elements of many jobs may well become automated over the next decade or so. But this will undoubtedly provide a more opportunities in other areas. New roles will emerge. For data scientists, it’s likely the unique combination of ‘hard’ and ‘soft’ skills means there will be strong demand for these skills for many decades to come.
