George Nice - Partner in Savannah Group’s Financial Services Practice
More data has been collected over the past two years than in the entire history of the human race. This data-collection frenzy is the result of several technology trends converging and accelerating to give us greater access to data, better data storage and faster data mining – much of which has been driven by the need for data in innovative new technologies, including artificial intelligence.
This data store has enormous potential value. But how well are businesses taking advantage of this fast growing treasure trove? And how are they doing when it comes to hiring the talent needed to create a successful data and analytics function?
The answers are decidedly mixed. Agile, digital firms that have successfully integrated data and analytics into their business strategy have gained a competitive advantage over firms that are still struggling with their legacy platforms. This is what the McKinsey Global Institute (MGI) describes as a shift from the ‘world full of data’ to a ‘data-driven world’ - something that many have found difficult to implement.
Nonetheless, more companies are beginning to prioritise the integration of data and analytics into their business strategies. As they do so, the demand for talent is vastly outstripping supply. This time, a McKinsey multi-industry survey of executives found that half were finding it more difficult to hire analytics talent than to fill any other role. Forty per cent said that retention was also an issue.
As the authors of The Talent Dividend at MIT Sloan Management Review put it: “Technology is no longer the main barrier to creating business value from data: the bigger barrier is a shortage of appropriate skills.”
This skills gap can be partly explained simply as the consequence of rapid growth. However, as MIT point out, the range of analytics skills, roles and titles has become much wider in recent years. The role of data scientist now encompasses data architect, data engineer and data analyst. Each has subtly different responsibilities and demand nuanced skill sets. Executive roles like Chief Data Officer (CDO) also have much greater prominence than before.
Despite all this, MIT tells us that only one in five organisations have changed their approach to data talent. So how do they source, attract and retain the high-calibre people they need?
Adopting a global approach to talent in order to maximise access to candidates is essential. For executives, the US still dominates the market, according to Data Science Central. But countries like India and Singapore have a growing number of data scientists who can bring a fresh perspective and approach to the job.
Rather than narrowing the candidate pool by focusing on a particular sector, companies should retain a broader approach to find the best individuals. Focus on the skill sets on offer, match those to requirements, and be more creative and flexible when it comes to background and previous industry experience. After all, data and analytics is a business function that transcends sectors – particularly while it is still in its relative infancy.
Demonstrating a clear vision for data and analytics is vital. Analytically minded individuals naturally seek environments where there is clear direction and vision around the use of data and its relationship to the business strategy. The right CDO can help here: as the interface between data teams and business strategy they must be technically sharp enough to inspire data teams, while having the necessary gravitas and business acumen to drive growth from data. This is a rare and highly valuable set of skills in the current talent market that will deliver dividends if nurtured appropriately.
The rapid growth in demand for data scientists has had a similar affect on average remuneration packages. In the US, data scientists’ pay increased by 16 per cent between 2012 and 2014. The nominal average was less than two per cent. To attract the best, firms must appreciate the rising cost of data scientists – and be able to respond quickly to remain competitive.
In the long term, fast-track schemes, training courses and education will close the data talent gap. In the medium term, AI and machine learning will also help fill the talent gap as labour-intensive data mining is automated. Our own analysis of talent trends shows that firms are addressing this particular talent shortage by selecting graduates with engineering backgrounds and placing them in areas like corporate M&A where they can develop in a client-centric environment. Although initially counterintuitive, this tactic brings together an analytical background with business functions to build a technically astute but commercially aware workforce.
However, immediate concerns remain. To address them, firms need to adopt a progressive talent strategy that focuses on ways to source, attract and retain executives who can enable businesses to realise the inherent value of data more quickly. By focusing initially on management, firms can develop vision, direction and ultimately growth: the critical talent magnets in any scenario.