In a recent survey by digital transformation firm Atos, 90% of businesses who responded said they will be using data analytics in their key functions by 2020. However, while turning to a technology proven in its ability to benefit businesses is understandable, the ability of most organizations to carry out their plans is questionable at best. Indeed, Gartner predicts that this year as many as 60% of data projects will not make it to fruition. They will be left to languish in the piloting and experimentation phases before being discarded, wasting time, money, and potential earnings.
One of the most oft-cited reasons for this high rate of failure is the difficulty that organizations have employing the talent to carry it out. This is a struggle for two reasons. Firstly, such talent is not easy to come by. The skills gap is a well-documented phenomenon, with the McKinsey Global Institute predicting that the shortage of data scientists in the US could increase to 250,000 by 2024. Dr Kepa Mendibil, course leader of the MSc in Data Science at the University of Stirling’s School of Management, for one, notes that, ‘There is a shortage of graduates emerging with the skills to apply the technical aspects of data science and use analytics to make sound business decisions.’
The second issue is that many organizations still don’t know exactly what they want to do with their data, much less how to organize a team to collect, prepare, and analyze it. As such, they struggle to correctly identify and attract the candidates they need. Data scientists do not come cheap. The Robert Walters Global Salary Survey for 2017 gave an estimated range for data scientist salaries of $116,000 to $163,500 in 2017, up 6.4% on 2016 levels. So organizations need to get it right.
Before you do anything, you need to define what exactly it is you need. Gary Damiano, vice president of marketing at NoSQL database specialists Couchbase notes that, ‘The two things you need to consider in hiring a data scientist are: how are you going to use them and how does their skill set match the use?’
Data science is a relatively new discipline, and understandably many in business know only that they need to utilize their data. This is not enough. You need to understand what your goals are, and what skills someone will need to achieve them. Data science is an incredibly broad field, and it helps to educate yourself at least to the extent that you know what you’re looking for - or bring in a consultant who can tell you.
The first question you must ask is are you looking for somebody who is going to be developing algorithms, such as those for recommendation engines, or are you looking for someone who is going to be looking more at using data to better understand your business? If the answer is developing algorithms, you will require someone with a strong mathematics and computer science background, but not necessarily any experience in your industry. Walter Storm, Chief Data Scientist at Lockheed Martin, ‘If they (data scientists) have a passion for coding, deep learning, and technology itself, then the industry they’re in doesn’t matter.
