The rise and rise of the data scientist

Even a few years ago, the job title ‘data scientist’ wouldn’t have sounded that interesting or important. How times change.

The ever-increasing importance of big data in business has brought with it new demands, both in terms of the infrastructure and computing power required to process big data and the individual skillsets required to unlock its insight. A LinkedIn post last year by EE CEO Olaf Swantee, declared that ‘Data Scientists are the New Rock Stars as Big Data Demands Big Talent’.

A bold claim for sure, but it is true that data scientists can help organisations immeasurably. What are the skills required for a good data scientist and what tools do they need to do their job well? 

Data scientists - a big data ‘must have’?
The ability to extract value and insight from big data is a key business objective for many organisations in 2014. This means the role of the data scientist has become one of the most sought after positions in business as organisations seek to gain competitive advantage from their data.

Research from technology consultancy NewVantage Partners, showed that 70 per cent of organisations planned to hire a data scientist. ALL respondents in the survey said it was ‘somewhat challenging’ to find a competent one.

So what does a data scientist do that warrants such hyperbole? Essentially the role is an evolution from a data analyst. It involves an academic background in maths or physics, some technical coding skills and solid experience in computer science, data modelling and analytics. What sets a data scientist apart is the ability to apply these skills in a business context, addressing business (not IT) problems and having the communication savvy to report their findings back to a business unit.

Other tools of the trade
But a data scientist will also need the right tools to work with. Assuming that most organisations are aware of the need for software that processes massive data sets (such as Hadoop), are they also aware of the enormous demands that such software places on their infrastructure?

Hadoop is a powerful analytics package but it is extremely power hungry. It was primarily designed to run in large physical data centres, on dedicated physical machines so it is no surprise to find that a virtual environment struggles to cope with Hadoop.

This is where bare-metal cloud services can play a major part in any data scientist’s work. These provide the dedicated high-performance environment required to process big data. There is no virtualisation and no performance-hungry hypervisor. A data scientist will frequently need to find quick answers so a reliable and powerful infrastructure is of the highest importance.

Data scientists aren’t exclusively connected to big data but they are strongly linked. A keen and creative business mind to complement existing analytics and computing skills make data scientists among the most sought after roles in business today.

A good data scientist enables an organisation to extract value from their big data and along with the right analytics software and enough computing power make a powerful triumvirate of big data ‘must-haves’