What is the difference between a data scientist and a data analyst?
While the traditional data analyst’s role was focussed on a particular source or software, the data scientist currently being sought by many large companies goes much further. In addition to a solid foundation in the software applications which generate the data, as well as the associated modelling, maths and statistics, the data scientist needs the business acumen to bring together different sources of data, and the communications ability to turn these data streams into useful cross-company information.
According to Peter Noblet, senior regional director of recruitment group Hays Information Technology, the data scientist will become far more influential in determining the strategic direction of the companies for which they work.
“Data scientists should also have business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organisation approaches a business challenge,” Noblet says.“Businesses
“Businesses are looking for individuals who can influence the strategic directions taken by marketing, sales, customer service and business planning, based on technological expertise and business insight.”
What is expected from a data scientist?
Anthony Wilson, head of business review at Woolworths, says the data scientist role at Australia’s largest retailer is about the ability to use existing data to predict problems before they arise, and understand underlying business drivers.
“A data scientist has a strong background in statistical and machine learning, as well as advanced knowledge of databases so they can build models to mine the enormous amounts of data that exist today in any large business,” says Wilson. “Data analysts on the other hand don’t require the same level of technical background and do not generally develop data mining models.”
Like most large organisations, Woolworths deals with large volumes of changing data, from simple transactional information, to complex and interconnected data sets, according to Wilson. Across its different divisions the retailer creates and manages around 3 petabyes of data on an on-going basis, hence the need for a data scientist to figure out what information is useful and relevant at any given time.
“Our data scientist is required to quickly get an understanding of our data, and accurately model risks and opportunities so we can focus our efforts on where it will really make a difference to the business,” says Wilson. “We’ll also be asking them to provide advice on the best use of statistical analysis techniques so Woolworths can remain at the absolute forefront using our data resources.”