Data science is being increasingly deployed across all sectors, however, its influence is felt more strongly in industries where there is already an enormous volume of data. Leading the way are the financial services, retail and life sciences sectors.
In financial services, data science is enabling banks to understand their customers better by analysing their behaviour and improving services accordingly to boost both retention and acquisition. Often that service is now delivered by automated chatbots.
In an increasingly complicated cyber-landscape, data science is also improving fraud detection through early-warning prediction systems, as well as enhancing risk management with machine-learning algorithms that analyse past spending patterns.
However, data science developments haven’t always been on the right side of banks. The technology has helped lower barriers to entry and driven the rise of fintech startups that are disrupting the sector with their agile approach to new innovation.
This has also been the case in the retail sector where data science has powered the rise of recommendation engines that are commonplace among ecommerce giants such as Amazon and eBay. Personalisation has transformed the way we buy and this is only possible when retailers are able to analyse the choices their customers make online.
Similarly, such technology has enabled retailers to improve their price strategies by implementing data-driven optimisation mechanisms that decipher how certain customer groups will respond to changes. Data science has also been applied at the backend to improve inventory management and create a more efficient supply chain.
Revolutionising life sciences
But it is in life sciences where data is aiding perhaps the most important developments. The ability to gather and analyse huge volumes of data constantly has revolutionised medicine by making image processing, gene-profiling and clinical trials more effective.
Data science is now instrumental in improving a whole range of patient outcomes. Diagnoses can be made automatically following scans and biopsies, and outbreaks of diseases can be predicted to a far greater degree of accuracy as can their impact. Real-time monitoring of health using wearable trackers is changing the way care is delivered.
All these new capabilities, along with the continued explosion of data, have triggered a huge demand for data science talent that can’t be delivered. The Royal Society reports that of 9.2 million job adverts posted in the UK over the past five-and-a-half years, 996,000 have asked for data expertise. While research from SAS estimates the adoption of big data analytics and the internet of things will have created 182,000 new jobs in the UK between 2015 and 2020. Yet in a recent study by Domo, nearly three quarters of UK chief executives say the skills shortage could create major problems in their business.
Improving data literacy within an organisation is the crucial element
The result is a much-needed move towards the democratisation of data, driven by a growing recognition that not all aspects of data science must be done by data scientists. Although professionals will always be needed to perform advanced analytics and navigate statistical anomalies, capable domain experts can also be very proactive with data.
Improving data literacy within an organisation is the crucial element. According to research by Qlik, companies with strong data literacy have an enterprise value 3 to 5 per cent higher than others, which for large organisations can equate to up to $534 million.
“It improves all metrics of corporate performance, including productivity and revenue growth,” says Elif Tutuk, senior director of research at Qlik. “By empowering all employees and nurturing data literacy, businesses may gain more than they could ever realise by looking beyond one sole data employee to make a good decision.”
Citizen data scientists
Such empowerment creates what Gartner refers to as citizen data scientists, domain experts from any part of the organisation given access to intuitive self-service analytics tools that allow them to apply data science to their respective business challenges.
Armed with such tools, they can interrogate datasets and explore patterns to answer questions without relying on statisticians or scientists. They can spot inefficiencies or trends that may indicate new risks or revenue opportunities and they can use the insights to create new services to meet customer requirements more effectively.
Citizen data scientists are becoming so prevalent that leading business schools are now introducing courses dedicated to training them
“Unless you can bring business intelligence to operational staff, organisations won’t see a dramatic uplift in knowledge, efficiency or revenue,” says Aditya Sriram, data scientist at Information Builders. “Data, as the lifeblood of any organisation, must reach every part for healthy operation. Domain experts can build a coherent story which they can then communicate to the decision-makers in their organisations.”
Citizen data scientists are becoming so prevalent that leading business schools are now introducing courses dedicated to training them. According to Clement Levallois, professor at Emlyon business school in France, citizen data scientists help introduce a logic of data-driven processes in a measured way, with a lower risk of project failure because they can produce incremental innovations, close to existing business practices.
“These quick wins can help justify and develop more ambitious projects, involving data scientists, at a later stage,” he says. “I think this is a key position helping bridge the gap between quants and business specialists. It is a matter of adopting a good pace of progress in upskilling and adoption of a culture of data in the organisation.”
However, there is still a lot of work to be done before businesses are really reaping the rewards of empowering data science to be conducted at every level of the organisation. Research by MicroStrategy found that at 46 per cent of UK companies, less than half the staff have access to data and analytics. It falls to the board to lead the democratisation of data, from policy through to training and the provision of technology that every employee can use to create new efficiencies throughout the organisation.
