The neverending growth of big data means the role of data scientist will play an ever-increasing role in modern business.
What is the role of CIOs in the rise of data science, and how can IT leaders and their executive peers find the expertise they require?
Here are five areas to consider.
1. Recognise the opportunity to add value
Andrew Marks, CIO at Tullow Oil, argues that with the rise of the data scientist, the value of data - and information - is being recognised. This strengthens the hand of the IT organisation - and emphasises the importance of the 'I' in CIO.
"It is the IT infrastructure on which the data is captured, manipulated, and interpreted that is crucial. The accountability for that infrastructure rests squarely with the CIO, no matter whether on-premise, hosted, or cloud," says Marks.
He says the CIO's scope may include managing the data science team, or enabling those for whom data is their day job. "The CIO can now look at ways to bring efficiency and effectiveness in this area and through that tangible value to his or her organisation," says Marks.
"With an understanding of what is possible in terms of data delivery, the CIO has a new partner who has the potential to add significant value in the way an organisation interacts with its customers. Together, data scientists and CIOs might be forming a valuable alliance that will serve both in terms of career and organisational value."
2. Focus on finding great people
Stephen Hand, former CIO and principal consultant at Consult 360, says building on the traditional skills of the data analyst, the newly titled role of data scientist will add a strategic dimension to working with enterprise data.
For him the primary role of the data scientist is to identify from internal and external sources the data that has real business value to the organisation - and develop the delivery path for information into the right parts of the business.
Hand says the rise of the data scientist is unsurprising, especially as it has been long predicted that the industry would see a massive shortage of people with a high level of analytical skills. He says any individual with data science in their LinkedIn profile can expect to be bombarded with emails from recruiters - yet, Hand says smart organisations also focus on two other sources of data scientists.
"The first is to develop a close working relationship with a university, and not just the computer science school as many of the most successful analysts are coming from other schools of science," he says. "The second is to look inside the organisation for core analytical skills and to be prepared to retrain those people in advanced data analytics."
3. Build long-term relationships
Like Stephen Hand, i2O software and IT director Mike Williams recognises that the tough part of data science is getting hold of skilled experts. The water management specialist is a heavy user of Apache Cassandra's open source database technology
"The challenge is the recruitment of people," says Williams. "What we're doing, in terms of using technology, is exciting. But there's a very small talent pool from which to draw. There's not that many people who have experience of using technology like Cassandra."
Williams' attempt to solve the skills gap centres on close working relationships with universities and he mentions Southampton, Bristol, Bath, and Exeter. "We're looking to recruit and then train people," he says. "We're always looking to get our talented people to learn new skills."
4. Work with specialists partners
Jim Anning, head of data and analytics at British Gas Connected Homes, recognises the recruitment of skilled specialists will be a crucial concern for many executives looking to make the most of big data. "Finding data scientists is the key issue," he says. "It's a very eclectic skill and creative generalists can often do more than specialists, but they're tough people to find."
The specific elements of the data scientist - creating the algorithm, taking feedback, and tweaking the mathematics - are highly skilled, says Anning. He says help comes in the form of S2DS, a specialist organisation that runs a five-week workshop to train analytical PhD students and scientists in the techniques of data science.
The aim of the workshop is to create a pipeline of high quality, commercial talent. Anning, meanwhile, is fortunate to be able to already draw on some great internal talent. "I'm lucky because we've got an interesting set of problems to solve," he says.
5. Make sure you are asking the right questions
Like his peers, Omid Shiraji, CIO at Working Links, says the skills concern is real. "There's a massive deficiency because, relatively speaking, you're talking about a young role," he says, before suggesting that - despite the importance of analytics and intelligence - organisations need to understand how they would use data scientists.
"It's an essential role but there's a level of mature conversation that needs to take place within businesses," says Shiraji. "You need skilled people internally that understand your data and how it creates something useful for the organisation. Externally contracted people can't do that."