The fields of data and analytics have made significant advances in recent years: the volume of data has grown exponentially, algorithms are getting ever more sophisticated and computational power continues to improve. But a new report from the McKinsey Global Institute, The Age of Analytics: Competing in a Data-Driver World , has identified a number of challenges and disruptions these advances are bringing to businesses.
Just scratching the surface
Most organisations and industries are barely scratching the surface when it comes to capturing value from data. The biggest challenges to extracting this value are often organisational, companies can struggle to embed data-driven decision making into day-to-day business processes. However, there are some exceptions.
Large retailers, particularly in the US and Europe, have been mining a treasure trove of customer purchase and behavioural data. McKinsey estimates that these firms are realising up to 40 per cent of the potential data-driven improvements to productivity and margins that they’ve identified.
Although retail is one of the sectors leading the way, at least 60 percent of data value is not yet being realised, and McKinsey says one of the main reasons for this is a shortage of analytical talent, not just data scientists but also “business translators” – who have business and functional expertise, alongside data know-how.
The value of data
“Data is the new oil,” proclaims an article on Forbes.com, “Those that figure out how to use it more effectively than their competitors are realising significant, strategic benefits.” A view supported by Mckinsey’s findings. Leading companies are using actionable data insights not only to boost their core offerings but to develop entirely new business models.
Data itself is not intrinsically valuable, the value is tied to its use. Ecosystems are evolving to help unlock this value and Mckinsey predicts that “while data itself will become increasingly commoditized, value is likely to accrue to the owners of scarce data, to players that aggregate data in unique ways, and especially to providers of valuable analytics.”
Machine learning to the rescue?
Recent advances mean that machine learning is being touted as the solution for all kinds of problems, from providing customer service, analyzing medical records or managing logistics. In the field of data and analytics, machines and algorithms can process vast amounts of data in a fraction of the time it would take humans.
On the frontier of research into machine learning is deep learning, which uses “neural networks with many layers (hence the label “deep”) to push the boundaries of machine capabilities.” Using deep learning, data scientists have been making breakthroughs in machine recognition of objects, faces and better understanding and use of language.
Natural language processing has the potential to transform cognitive computing, creating opportunities with high potential in areas as diverse as disease diagnosis, fraud detecting and predictive maintenance and across a broad range of industries including agriculture, finance, pharmaceuticals and manufacturing.
Data and analytics skills gap
Companies report that finding the right talent is the biggest challenge they face in trying to better integrate data and analytics into their business operations. In the McKinsey survey, some 50 percent of executives, across industries and geographies, reported that recruiting analytical talent was harder than for any other role. Big data also topped a recent list of technical skills with explosive growth In job demand, rising 3,977 percent between 2011 and 2015.
The shortage of data scientists, in particular, was highlighted. Average salaries in the US for this group increased 16 percent every year between 2011 and 2014, far outpacing the average salary increase for all occupations of 2 percent over the same period. The shortage of top data scientists has contributed to some acquisitions of cutting-edge AI startups, with deals commanding as much as $10 million per employee.
The scarcity of data scientists is likely to continue. Although graduates of data science programs are set to increase by 7 percent per year, this will be outpaced by growth in demand of 12 percent per year.
However, companies shouldn’t overly focus on data scientists as the solution to data transformation, “another equally vital role is that of business translator, who serves as the link between analytical talent and practical applications to business questions.” While some analytics activities can be outsourced, business translators need to be fully embedded within the organisation to be successful. In the US alone, it is estimated there will be demand for 2-4 million business translators over the next decade.
To find this elite, and increasingly scarce, data and analytics talent, organisations will need to engage with specialists staffing companies such as K2 Partnering Solutions, that have the established talent pools and relationships to deliver the skills they need to stay competitive and thrive.
Dylan Griffiths is Digital and Social Media Director at K2 Partnering Solutions.