David Di Domenico is Managing Director of IQ Analytics, working between its Canary Wharf, Manchester and New York locations. After graduating with a BSc in Physics from Edinburgh, David worked for several years in digital electronics research with Marconi Telecommunications. He entered Recruitment in 1987 and helped to grow 'Intelect Recruitment' from a fledgling company into to a leader in the IT recruitment space. In 2007 David led the sale of Intelect as it became part of the InterQuest Group. Since then David has been the MD of IQ Analytics growing it from inception to a global leader in mathematical, statistical and risk recruitment. He is also a member of the InterQuest Group Operational Management board. Here David shares some thoughts on data science.
"More data has been created in the last two years than in the rest of history."
So says Simon Collins, KPMG's UK chairman, and it's a statistic that I find very easy to believe. Business storage requirements have been expanding at an unprecedented rate, and this growth has been driven by a combination of factors. These include the switch from analogue to digital technologies in industries such as retail and banking, increased Internet and mobile usage, and the low cost of data storage - especially in the cloud - which is encouraging organisations to "save everything."
This explosion in data shows no sign of abating, and CSC predicts that data production will be 44 times greater in 2020 than it was in 2009. I believe this is likely to be in no small part due to the rise of the 'Internet of Things', an emerging network of inexpensive sensors attached to everything from consumer products to industrial equipment which exchange data with their owners, manufacturers and each other.
"That's amazing," Mr Collins went on to say, "but we can all drown in data unless we know what to do with it."
This, I think, is the key point. Collecting vast amounts of data has no value in itself: it's only by analysing it that any value can be realised. And businesses can only gain insights and extract business benefits from all of this data fully if they have people on board who understand how best to exploit it. Those people are Data Scientists.
Data Science is a discipline that has been evolving rapidly over the last couple of years as a large number of companies have embarked on Big Data projects that seek to put the vast stores of data that they are collecting to good use.
Software such as the hugely popular Splunk has made it easy for non-specialist staff to carry out rudimentary Big Data analysis - a recent CompTIA study found that staff in both sales and research departments have seen a rapid rise in involvement in Big Data projects. But to get the full benefit from all this data companies are increasingly recruiting from the small pool of highly skilled specialist Data Scientists.
What is a Data Scientist? Because the term is relatively new there's no precise definition, but a Data Scientist has to be an expert at computer science and statistics so that creating algorithms, and writing scripts to run data through these algorithms, is the easy part of their job.
But a data scientist also needs to know what data sets to analyse, and, in my view at least, to do that they need a business brain as well. Why? So they can gain a deep understanding of the underlying business and the challenges it faces - and thus the types of business insights that need to be surfaced from the data.
There's one more requirement - and it's a requirement that I believe is increasingly being recognised as one of the most important ones. A data scientist has to be able to communicate their findings to others in the organization clearly: not as numbers or correlations, but in business terms as actionable insights that can lead to measurable benefits.
Because ultimately that's what a Data Scientist is all about: extracting business value from all this data that's being created.